2024/03/17 17:11:37 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.5 (default, Sep 4 2020, 07:30:14) [GCC 7.3.0] CUDA available: True numpy_random_seed: 1629659436 GPU 0,1,2,3,4,5,6,7: NVIDIA A10 CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 11.3, V11.3.109 GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5) PyTorch: 1.11.0+cu113 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.3 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.2 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.12.0+cu113 OpenCV: 4.9.0 MMEngine: 0.7.2 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: pytorch Distributed training: True GPU number: 8 ------------------------------------------------------------ 2024/03/17 17:11:39 - mmengine - INFO - Config: default_scope = 'mmyolo' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict( type='YOLOv5ParamSchedulerHook', scheduler_type='linear', lr_factor=0.01, max_epochs=80), checkpoint=dict( type='CheckpointHook', interval=5, save_best=None, max_keep_ckpts=-1), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='mmdet.DetVisualizationHook')) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='mmdet.DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = 'pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth' resume = False backend_args = None _backend_args = None tta_model = dict( type='mmdet.DetTTAModel', tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.65), max_per_img=300)) img_scales = [(640, 640), (320, 320), (960, 960)] _multiscale_resize_transforms = [ dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(320, 320)), dict( type='LetterResize', scale=(320, 320), allow_scale_up=False, pad_val=dict(img=114)) ]), dict( type='Compose', transforms=[ dict(type='YOLOv5KeepRatioResize', scale=(960, 960)), dict( type='LetterResize', scale=(960, 960), allow_scale_up=False, pad_val=dict(img=114)) ]) ] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='TestTimeAug', transforms=[[{ 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (640, 640) }, { 'type': 'LetterResize', 'scale': (640, 640), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (320, 320) }, { 'type': 'LetterResize', 'scale': (320, 320), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }, { 'type': 'Compose', 'transforms': [{ 'type': 'YOLOv5KeepRatioResize', 'scale': (960, 960) }, { 'type': 'LetterResize', 'scale': (960, 960), 'allow_scale_up': False, 'pad_val': { 'img': 114 } }] }], [{ 'type': 'mmdet.RandomFlip', 'prob': 1.0 }, { 'type': 'mmdet.RandomFlip', 'prob': 0.0 }], [{ 'type': 'mmdet.LoadAnnotations', 'with_bbox': True }], [{ 'type': 'mmdet.PackDetInputs', 'meta_keys': ('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'flip', 'flip_direction') }]]) ] data_root = 'data/coco/' train_ann_file = 'annotations/instances_train2017.json' train_data_prefix = 'train2017/' val_ann_file = 'annotations/instances_val2017.json' val_data_prefix = 'val2017/' num_classes = 80 train_batch_size_per_gpu = 16 train_num_workers = 8 persistent_workers = False base_lr = 0.0002 max_epochs = 80 close_mosaic_epochs = 10 model_test_cfg = dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300) img_scale = (640, 640) dataset_type = 'YOLOv5CocoDataset' val_batch_size_per_gpu = 1 val_num_workers = 2 batch_shapes_cfg = None deepen_factor = 1.0 widen_factor = 1.0 strides = [8, 16, 32] last_stage_out_channels = 512 num_det_layers = 3 norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) affine_scale = 0.9 max_aspect_ratio = 100 tal_topk = 10 tal_alpha = 0.5 tal_beta = 6.0 loss_cls_weight = 0.5 loss_bbox_weight = 7.5 loss_dfl_weight = 0.375 lr_factor = 0.01 weight_decay = 0.05 save_epoch_intervals = 5 val_interval_stage2 = 1 max_keep_ckpts = 2 model = dict( type='YOLOWorldDetector', data_preprocessor=dict( type='YOLOWDetDataPreprocessor', mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], bgr_to_rgb=True), backbone=dict( type='MultiModalYOLOBackbone', image_model=dict( type='YOLOv8CSPDarknet', arch='P5', last_stage_out_channels=512, deepen_factor=1.0, widen_factor=1.0, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), text_model=dict( type='HuggingCLIPLanguageBackbone', model_name='../pretrained_models/clip-vit-base-patch32-projection', frozen_modules=['all'])), neck=dict( type='YOLOWorldPAFPN', deepen_factor=1.0, widen_factor=1.0, in_channels=[256, 512, 512], out_channels=[256, 512, 512], num_csp_blocks=3, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), guide_channels=512, embed_channels=[128, 256, 256], num_heads=[4, 8, 8], block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv')), bbox_head=dict( type='YOLOWorldHead', head_module=dict( type='YOLOWorldHeadModule', num_classes=80, in_channels=[256, 512, 512], widen_factor=1.0, reg_max=16, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True), featmap_strides=[8, 16, 32], use_bn_head=True, embed_dims=512), prior_generator=dict( type='mmdet.MlvlPointGenerator', offset=0.5, strides=[8, 16, 32]), bbox_coder=dict(type='DistancePointBBoxCoder'), loss_cls=dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='none', loss_weight=0.5), loss_bbox=dict( type='IoULoss', iou_mode='ciou', bbox_format='xyxy', reduction='sum', loss_weight=7.5, return_iou=False), loss_dfl=dict( type='mmdet.DistributionFocalLoss', reduction='mean', loss_weight=0.375)), train_cfg=dict( assigner=dict( type='BatchTaskAlignedAssigner', num_classes=80, use_ciou=True, topk=10, alpha=0.5, beta=6.0, eps=1e-09)), test_cfg=dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.7), max_per_img=300), mm_neck=True, num_train_classes=80, num_test_classes=80) albu_train_transforms = [ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ] pre_transform = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ] last_transform = [ dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')) ] train_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] train_pipeline_stage2 = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=True, pad_val=dict(img=114.0)), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.09999999999999998, 1.9), max_aspect_ratio=100, border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] train_dataloader = dict( batch_size=16, num_workers=8, persistent_workers=False, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), collate_fn=dict(type='yolow_collate'), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ])) test_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ] val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ])) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ])) param_scheduler = None optim_wrapper = dict( type='AmpOptimWrapper', clip_grad=dict(max_norm=10.0), optimizer=dict( type='AdamW', lr=0.0002, weight_decay=0.05, batch_size_per_gpu=16), constructor='YOLOWv5OptimizerConstructor', paramwise_cfg=dict( bias_decay_mult=0.0, norm_decay_mult=0.0, custom_keys=dict({ 'backbone.text_model': dict(lr_mult=0.01), 'logit_scale': dict(weight_decay=0.0) })), loss_scale='dynamic') custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0001, update_buffers=True, strict_load=False, priority=49), dict( type='mmdet.PipelineSwitchHook', switch_epoch=70, switch_pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=True, pad_val=dict(img=114.0)), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.09999999999999998, 1.9), max_aspect_ratio=100, border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ]) ] val_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/annotations/instances_val2017.json', metric='bbox') test_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/annotations/instances_val2017.json', metric='bbox') train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=80, val_interval=5, dynamic_intervals=[(70, 1)]) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') use_mask2refine = True min_area_ratio = 0.01 mixup_prob = 0.15 copypaste_prob = 0.3 mosaic_affine_transform = [ dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ] custom_imports = dict(imports=['yolo_world'], allow_failed_imports=False) num_training_classes = 80 text_channels = 512 neck_embed_channels = [128, 256, 256] neck_num_heads = [4, 8, 8] text_model_name = '../pretrained_models/clip-vit-base-patch32-projection' text_transform = [ dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ] coco_train_dataset = dict( _delete_=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_train2017.json', data_prefix=dict(img='train2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True), dict( type='YOLOv5MultiModalMixUp', prob=0.15, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True), dict( type='MultiModalMosaic', img_scale=(640, 640), pad_val=114.0, pre_transform=[ dict(type='LoadImageFromFile', backend_args=None), dict( type='LoadAnnotations', with_bbox=True, with_mask=True, mask2bbox=True) ]), dict(type='YOLOv5CopyPaste', prob=0.3), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, max_aspect_ratio=100.0, scaling_ratio_range=(0.09999999999999998, 1.9), border=(-320, -320), border_val=(114, 114, 114), min_area_ratio=0.01, use_mask_refine=True) ]), dict(type='RemoveDataElement', keys=['gt_masks']), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='RandomLoadText', num_neg_samples=(80, 80), max_num_samples=80, padding_to_max=True, padding_value=''), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction', 'texts')) ]) coco_val_dataset = dict( _delete_=True, type='MultiModalDataset', dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco', ann_file='annotations/instances_val2017.json', data_prefix=dict(img='val2017/'), filter_cfg=dict(filter_empty_gt=False, min_size=32)), class_text_path='data/texts/coco_class_texts.json', pipeline=[ dict(type='LoadImageFromFile', backend_args=None), dict(type='YOLOv5KeepRatioResize', scale=(640, 640)), dict( type='LetterResize', scale=(640, 640), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), dict(type='LoadText'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param', 'texts')) ]) launcher = 'pytorch' work_dir = './work_dirs/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco' 2024/03/17 17:11:43 - mmengine - INFO - Using SyncBatchNorm() 2024/03/17 17:11:43 - mmengine - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (BELOW_NORMAL) LoggerHook -------------------- after_load_checkpoint: (49 ) EMAHook -------------------- before_train: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_train_epoch: (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook (NORMAL ) DistSamplerSeedHook (NORMAL ) PipelineSwitchHook -------------------- before_train_iter: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (NORMAL ) IterTimerHook -------------------- after_train_iter: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (VERY_LOW ) CheckpointHook -------------------- after_train_epoch: (9 ) YOLOv5ParamSchedulerHook (NORMAL ) IterTimerHook (VERY_LOW ) CheckpointHook -------------------- before_val_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_val_iter: (NORMAL ) IterTimerHook -------------------- after_val_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_val_epoch: (9 ) YOLOv5ParamSchedulerHook (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook (VERY_LOW ) CheckpointHook -------------------- before_save_checkpoint: (49 ) EMAHook -------------------- after_train: (VERY_LOW ) CheckpointHook -------------------- before_test_epoch: (49 ) EMAHook (NORMAL ) IterTimerHook -------------------- before_test_iter: (NORMAL ) IterTimerHook -------------------- after_test_iter: (NORMAL ) IterTimerHook (NORMAL ) DetVisualizationHook (BELOW_NORMAL) LoggerHook -------------------- after_test_epoch: (VERY_HIGH ) RuntimeInfoHook (49 ) EMAHook (NORMAL ) IterTimerHook (BELOW_NORMAL) LoggerHook -------------------- after_run: (BELOW_NORMAL) LoggerHook -------------------- 2024/03/17 17:12:14 - mmengine - INFO - Scaled weight_decay to 0.1 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stem.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stem.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.main_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.main_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.final_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.final_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.blocks.0.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage1.1.blocks.0.conv1.bn.bias:weight_decay=0.0 2024/03/17 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backbone.image_model.stage4.1.blocks.1.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.1.blocks.1.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.1.blocks.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.1.blocks.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.1.blocks.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.1.blocks.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- backbone.image_model.stage4.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.main_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.main_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.final_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.final_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.0.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.0.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.0.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.0.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.1.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.1.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.1.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.1.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.blocks.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.attn_block.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.attn_block.guide_fc.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.attn_block.project_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.0.attn_block.project_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.main_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.main_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.final_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.final_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.0.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.0.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.0.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.0.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.1.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.1.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.1.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.1.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.blocks.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.attn_block.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.attn_block.guide_fc.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.attn_block.project_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.top_down_layers.1.attn_block.project_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.downsample_layers.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.downsample_layers.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.downsample_layers.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.downsample_layers.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.main_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.main_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.final_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.final_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.0.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.0.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.0.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.0.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.1.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.1.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.1.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.1.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.blocks.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.attn_block.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.attn_block.guide_fc.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.attn_block.project_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.0.attn_block.project_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.main_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.main_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.final_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.final_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.0.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.0.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.0.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.0.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.1.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.1.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.1.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.1.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.2.conv1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.2.conv1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.2.conv2.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.blocks.2.conv2.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.attn_block.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.attn_block.guide_fc.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.attn_block.project_conv.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- neck.bottom_up_layers.1.attn_block.project_conv.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.0.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.0.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.0.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.0.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.0.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.1.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.1.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.1.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.1.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.1.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.2.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.2.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.2.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.2.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_preds.2.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.0.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.0.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.0.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.0.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.0.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.1.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.1.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.1.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.1.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.1.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.2.0.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.2.0.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.2.1.bn.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.2.1.bn.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.reg_preds.2.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:lr=0.0002 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.logit_scale:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.norm.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.0.norm.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:lr=0.0002 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.logit_scale:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.norm.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.1.norm.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.bias:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:lr=0.0002 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.logit_scale:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.norm.weight:weight_decay=0.0 2024/03/17 17:12:14 - mmengine - INFO - paramwise_options -- bbox_head.head_module.cls_contrasts.2.norm.bias:weight_decay=0.0 Name of parameter - Initialization information backbone.image_model.stem.conv.weight - torch.Size([64, 3, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stem.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stem.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.conv.weight - torch.Size([128, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.0.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.0.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.conv.weight - torch.Size([128, 128, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.main_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.main_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.conv.weight - torch.Size([128, 320, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.final_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.final_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.0.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.0.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.1.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.1.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv1.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv2.conv.weight - torch.Size([64, 64, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage1.1.blocks.2.conv2.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage1.1.blocks.2.conv2.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.conv.weight - torch.Size([256, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.conv.weight - torch.Size([256, 256, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.conv.weight - torch.Size([256, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.3.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.3.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.4.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.4.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage2.1.blocks.5.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage2.1.blocks.5.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.conv.weight - torch.Size([512, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.conv.weight - torch.Size([512, 2048, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.3.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.3.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.4.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.4.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage3.1.blocks.5.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage3.1.blocks.5.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.0.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.0.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.conv.weight - torch.Size([512, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.conv.weight - torch.Size([512, 1280, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.conv.weight - torch.Size([256, 512, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOv8CSPDarknet backbone.image_model.stage4.2.conv2.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.image_model.stage4.2.conv2.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.embeddings.token_embedding.weight - torch.Size([49408, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.embeddings.position_embedding.weight - torch.Size([77, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.0.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.1.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.2.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.3.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.4.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.5.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.6.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.7.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.8.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.9.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.10.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.k_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.k_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.v_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.v_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.q_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.q_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.out_proj.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.self_attn.out_proj.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc1.weight - torch.Size([2048, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc1.bias - torch.Size([2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc2.weight - torch.Size([512, 2048]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.mlp.fc2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm2.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.encoder.layers.11.layer_norm2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.final_layer_norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_model.final_layer_norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector backbone.text_model.model.text_projection.weight - torch.Size([512, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.main_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.main_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.conv.weight - torch.Size([256, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.final_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.final_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.0.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.0.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.1.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.1.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv1.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv2.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.blocks.2.conv2.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.blocks.2.conv2.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.bias - torch.Size([4]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.guide_fc.weight - torch.Size([128, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.guide_fc.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.project_conv.conv.weight - torch.Size([128, 128, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.top_down_layers.1.attn_block.project_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.top_down_layers.1.attn_block.project_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.conv.weight - torch.Size([512, 512, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.downsample_layers.1.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.downsample_layers.1.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.conv.weight - torch.Size([512, 768, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.0.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.0.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.conv.weight - torch.Size([512, 1024, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.main_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.main_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.conv.weight - torch.Size([512, 1536, 1, 1]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.final_conv.bn.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.final_conv.bn.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.0.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.0.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.1.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.1.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv1.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv2.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.blocks.2.conv2.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.blocks.2.conv2.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.bias - torch.Size([8]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.guide_fc.weight - torch.Size([256, 512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.guide_fc.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.project_conv.conv.weight - torch.Size([256, 256, 3, 3]): Initialized by user-defined `init_weights` in YOLOWorldPAFPN neck.bottom_up_layers.1.attn_block.project_conv.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector neck.bottom_up_layers.1.attn_block.project_conv.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.0.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.1.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.1.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_preds.2.0.conv.weight - torch.Size([256, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.0.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.0.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.conv.weight - torch.Size([256, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.bn.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.1.bn.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.2.weight - torch.Size([512, 256, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_preds.2.2.bias - torch.Size([512]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.0.0.conv.weight - torch.Size([64, 256, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.0.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.1.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.1.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.reg_preds.2.0.conv.weight - torch.Size([64, 512, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.conv.weight - torch.Size([64, 64, 3, 3]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.1.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.2.weight - torch.Size([64, 64, 1, 1]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.reg_preds.2.2.bias - torch.Size([64]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.0.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.0.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.0.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.0.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.1.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.1.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.bias - torch.Size([]): Initialized by user-defined `init_weights` in YOLOWorldHeadModule bbox_head.head_module.cls_contrasts.2.logit_scale - torch.Size([]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.norm.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector bbox_head.head_module.cls_contrasts.2.norm.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of YOLOWorldDetector 2024/03/17 17:12:27 - mmengine - INFO - Load checkpoint from pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth 2024/03/17 17:12:27 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2024/03/17 17:12:27 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2024/03/17 17:12:27 - mmengine - INFO - Checkpoints will be saved to /group/40034/adriancheng/YOLOWorld_Master/work_dirs/yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco. 2024/03/17 17:13:12 - mmengine - INFO - Epoch(train) [1][ 50/925] lr: 3.5315e-06 eta: 18:20:16 time: 0.8927 data_time: 0.1139 memory: 17803 grad_norm: nan loss: 507.5543 loss_cls: 207.6161 loss_bbox: 143.1035 loss_dfl: 156.8346 2024/03/17 17:13:42 - mmengine - INFO - Epoch(train) [1][100/925] lr: 7.1351e-06 eta: 15:16:19 time: 0.5952 data_time: 0.0052 memory: 11454 grad_norm: 818.7420 loss: 477.4016 loss_cls: 184.9844 loss_bbox: 138.1832 loss_dfl: 154.2340 2024/03/17 17:14:13 - mmengine - INFO - Epoch(train) [1][150/925] lr: 1.0739e-05 eta: 14:25:37 time: 0.6219 data_time: 0.0053 memory: 11201 grad_norm: 760.7777 loss: 456.3222 loss_cls: 173.0905 loss_bbox: 132.8075 loss_dfl: 150.4242 2024/03/17 17:14:44 - mmengine - INFO - Epoch(train) [1][200/925] lr: 1.4342e-05 eta: 13:57:39 time: 0.6143 data_time: 0.0046 memory: 11334 grad_norm: 709.1682 loss: 446.1747 loss_cls: 168.4502 loss_bbox: 128.4820 loss_dfl: 149.2425 2024/03/17 17:15:14 - mmengine - INFO - Epoch(train) [1][250/925] lr: 1.7946e-05 eta: 13:40:50 time: 0.6149 data_time: 0.0048 memory: 11387 grad_norm: 703.8501 loss: 435.8437 loss_cls: 162.4824 loss_bbox: 126.6719 loss_dfl: 146.6893 2024/03/17 17:15:45 - mmengine - INFO - Epoch(train) [1][300/925] lr: 2.1550e-05 eta: 13:29:06 time: 0.6132 data_time: 0.0048 memory: 11401 grad_norm: 715.6118 loss: 431.4844 loss_cls: 160.4793 loss_bbox: 124.7132 loss_dfl: 146.2919 2024/03/17 17:16:16 - mmengine - INFO - Epoch(train) [1][350/925] lr: 2.5153e-05 eta: 13:21:57 time: 0.6211 data_time: 0.0047 memory: 11321 grad_norm: 764.6171 loss: 424.8940 loss_cls: 157.6658 loss_bbox: 123.4134 loss_dfl: 143.8147 2024/03/17 17:16:48 - mmengine - INFO - Epoch(train) [1][400/925] lr: 2.8757e-05 eta: 13:17:29 time: 0.6277 data_time: 0.0051 memory: 11254 grad_norm: 716.2170 loss: 424.6041 loss_cls: 156.2693 loss_bbox: 122.9597 loss_dfl: 145.3751 2024/03/17 17:17:17 - mmengine - INFO - Epoch(train) [1][450/925] lr: 3.2360e-05 eta: 13:09:19 time: 0.5942 data_time: 0.0045 memory: 11227 grad_norm: 726.0157 loss: 417.7762 loss_cls: 151.1923 loss_bbox: 122.4843 loss_dfl: 144.0996 2024/03/17 17:17:49 - mmengine - INFO - Epoch(train) [1][500/925] lr: 3.5964e-05 eta: 13:06:44 time: 0.6272 data_time: 0.0050 memory: 11720 grad_norm: 745.9637 loss: 422.2477 loss_cls: 154.3148 loss_bbox: 123.4012 loss_dfl: 144.5318 2024/03/17 17:18:20 - mmengine - INFO - Epoch(train) [1][550/925] lr: 3.9568e-05 eta: 13:03:29 time: 0.6178 data_time: 0.0047 memory: 11321 grad_norm: 721.3622 loss: 423.0999 loss_cls: 154.8502 loss_bbox: 124.3046 loss_dfl: 143.9450 2024/03/17 17:18:50 - mmengine - INFO - Epoch(train) [1][600/925] lr: 4.3171e-05 eta: 12:59:26 time: 0.6055 data_time: 0.0050 memory: 11694 grad_norm: 693.6552 loss: 422.8400 loss_cls: 155.1065 loss_bbox: 123.3136 loss_dfl: 144.4199 2024/03/17 17:19:21 - mmengine - INFO - Epoch(train) [1][650/925] lr: 4.6775e-05 eta: 12:57:18 time: 0.6202 data_time: 0.0047 memory: 11561 grad_norm: 750.7275 loss: 422.2487 loss_cls: 155.2221 loss_bbox: 123.8612 loss_dfl: 143.1654 2024/03/17 17:19:52 - mmengine - INFO - Epoch(train) [1][700/925] lr: 5.0378e-05 eta: 12:55:11 time: 0.6177 data_time: 0.0047 memory: 11441 grad_norm: 790.4427 loss: 411.7667 loss_cls: 148.0861 loss_bbox: 121.0137 loss_dfl: 142.6669 2024/03/17 17:20:22 - mmengine - INFO - Epoch(train) [1][750/925] lr: 5.3982e-05 eta: 12:52:28 time: 0.6076 data_time: 0.0048 memory: 11334 grad_norm: 762.5554 loss: 423.1929 loss_cls: 155.1181 loss_bbox: 124.3666 loss_dfl: 143.7082 2024/03/17 17:20:52 - mmengine - INFO - Epoch(train) [1][800/925] lr: 5.7586e-05 eta: 12:49:46 time: 0.6041 data_time: 0.0048 memory: 11534 grad_norm: 757.1504 loss: 420.7540 loss_cls: 153.5950 loss_bbox: 123.9784 loss_dfl: 143.1805 2024/03/17 17:21:24 - mmengine - INFO - Epoch(train) [1][850/925] lr: 6.1189e-05 eta: 12:48:47 time: 0.6247 data_time: 0.0048 memory: 11294 grad_norm: 807.5121 loss: 416.2980 loss_cls: 150.4890 loss_bbox: 124.2788 loss_dfl: 141.5302 2024/03/17 17:21:55 - mmengine - INFO - Epoch(train) [1][900/925] lr: 6.4793e-05 eta: 12:47:26 time: 0.6183 data_time: 0.0050 memory: 12135 grad_norm: 775.3831 loss: 414.4378 loss_cls: 149.5401 loss_bbox: 122.8280 loss_dfl: 142.0697 2024/03/17 17:22:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:22:50 - mmengine - INFO - Epoch(train) [2][ 50/925] lr: 6.9329e-05 eta: 12:55:47 time: 0.6938 data_time: 0.0604 memory: 11229 grad_norm: 799.9406 loss: 417.7913 loss_cls: 151.9458 loss_bbox: 122.7650 loss_dfl: 143.0806 2024/03/17 17:23:05 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:23:21 - mmengine - INFO - Epoch(train) [2][100/925] lr: 7.2889e-05 eta: 12:54:37 time: 0.6266 data_time: 0.0050 memory: 11719 grad_norm: 748.3675 loss: 414.6643 loss_cls: 148.4719 loss_bbox: 123.0555 loss_dfl: 143.1369 2024/03/17 17:23:51 - mmengine - INFO - Epoch(train) [2][150/925] lr: 7.6448e-05 eta: 12:52:27 time: 0.6079 data_time: 0.0047 memory: 11479 grad_norm: 779.1025 loss: 416.0730 loss_cls: 152.1783 loss_bbox: 121.3992 loss_dfl: 142.4955 2024/03/17 17:24:22 - mmengine - INFO - Epoch(train) [2][200/925] lr: 8.0007e-05 eta: 12:51:01 time: 0.6190 data_time: 0.0049 memory: 11239 grad_norm: 905.3205 loss: 415.7120 loss_cls: 149.1372 loss_bbox: 123.0815 loss_dfl: 143.4932 2024/03/17 17:24:54 - mmengine - INFO - Epoch(train) [2][250/925] lr: 8.3566e-05 eta: 12:50:11 time: 0.6289 data_time: 0.0048 memory: 11172 grad_norm: 814.8923 loss: 413.5756 loss_cls: 150.4823 loss_bbox: 121.0187 loss_dfl: 142.0747 2024/03/17 17:25:25 - mmengine - INFO - Epoch(train) [2][300/925] lr: 8.7125e-05 eta: 12:49:24 time: 0.6293 data_time: 0.0049 memory: 11252 grad_norm: 829.9625 loss: 413.6755 loss_cls: 148.9640 loss_bbox: 121.8182 loss_dfl: 142.8932 2024/03/17 17:25:56 - mmengine - INFO - Epoch(train) [2][350/925] lr: 9.0684e-05 eta: 12:47:58 time: 0.6155 data_time: 0.0049 memory: 11346 grad_norm: 841.0188 loss: 416.0887 loss_cls: 150.6021 loss_bbox: 122.8612 loss_dfl: 142.6254 2024/03/17 17:26:27 - mmengine - INFO - Epoch(train) [2][400/925] lr: 9.4243e-05 eta: 12:47:16 time: 0.6298 data_time: 0.0048 memory: 11652 grad_norm: 802.1724 loss: 412.5326 loss_cls: 149.4311 loss_bbox: 121.2534 loss_dfl: 141.8481 2024/03/17 17:26:59 - mmengine - INFO - Epoch(train) [2][450/925] lr: 9.7802e-05 eta: 12:46:33 time: 0.6292 data_time: 0.0046 memory: 11359 grad_norm: 788.9381 loss: 415.8308 loss_cls: 151.3093 loss_bbox: 121.6495 loss_dfl: 142.8720 2024/03/17 17:27:29 - mmengine - INFO - Epoch(train) [2][500/925] lr: 1.0136e-04 eta: 12:44:44 time: 0.6028 data_time: 0.0050 memory: 11439 grad_norm: 842.1263 loss: 418.0257 loss_cls: 150.4479 loss_bbox: 123.6082 loss_dfl: 143.9696 2024/03/17 17:28:01 - mmengine - INFO - Epoch(train) [2][550/925] lr: 1.0492e-04 eta: 12:44:16 time: 0.6336 data_time: 0.0049 memory: 11599 grad_norm: 862.9286 loss: 413.7597 loss_cls: 148.0268 loss_bbox: 123.5898 loss_dfl: 142.1431 2024/03/17 17:28:33 - mmengine - INFO - Epoch(train) [2][600/925] lr: 1.0848e-04 eta: 12:43:51 time: 0.6352 data_time: 0.0051 memory: 11732 grad_norm: 784.2422 loss: 414.2538 loss_cls: 148.1700 loss_bbox: 124.2037 loss_dfl: 141.8801 2024/03/17 17:29:03 - mmengine - INFO - Epoch(train) [2][650/925] lr: 1.1204e-04 eta: 12:42:43 time: 0.6167 data_time: 0.0047 memory: 11226 grad_norm: 769.6033 loss: 424.4877 loss_cls: 155.1354 loss_bbox: 125.4227 loss_dfl: 143.9296 2024/03/17 17:29:35 - mmengine - INFO - Epoch(train) [2][700/925] lr: 1.1560e-04 eta: 12:42:17 time: 0.6342 data_time: 0.0048 memory: 11212 grad_norm: 808.9840 loss: 418.4210 loss_cls: 151.7186 loss_bbox: 123.4012 loss_dfl: 143.3013 2024/03/17 17:30:07 - mmengine - INFO - Epoch(train) [2][750/925] lr: 1.1916e-04 eta: 12:41:46 time: 0.6323 data_time: 0.0049 memory: 11572 grad_norm: 814.7176 loss: 418.6472 loss_cls: 151.0072 loss_bbox: 123.8828 loss_dfl: 143.7573 2024/03/17 17:30:37 - mmengine - INFO - Epoch(train) [2][800/925] lr: 1.2271e-04 eta: 12:40:28 time: 0.6097 data_time: 0.0048 memory: 11826 grad_norm: 795.8486 loss: 418.5395 loss_cls: 150.9136 loss_bbox: 124.0775 loss_dfl: 143.5484 2024/03/17 17:31:08 - mmengine - INFO - Epoch(train) [2][850/925] lr: 1.2627e-04 eta: 12:39:36 time: 0.6212 data_time: 0.0052 memory: 11252 grad_norm: 789.0006 loss: 417.2144 loss_cls: 149.4775 loss_bbox: 123.5526 loss_dfl: 144.1844 2024/03/17 17:31:40 - mmengine - INFO - Epoch(train) [2][900/925] lr: 1.2983e-04 eta: 12:39:06 time: 0.6319 data_time: 0.0045 memory: 11652 grad_norm: 817.6458 loss: 419.6698 loss_cls: 152.7897 loss_bbox: 123.6598 loss_dfl: 143.2203 2024/03/17 17:31:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:32:30 - mmengine - INFO - Epoch(train) [3][ 50/925] lr: 1.3348e-04 eta: 12:40:01 time: 0.6864 data_time: 0.0708 memory: 11279 grad_norm: 820.8120 loss: 412.9600 loss_cls: 146.6382 loss_bbox: 123.2867 loss_dfl: 143.0351 2024/03/17 17:33:02 - mmengine - INFO - Epoch(train) [3][100/925] lr: 1.3699e-04 eta: 12:39:08 time: 0.6206 data_time: 0.0047 memory: 11359 grad_norm: 846.6941 loss: 424.1166 loss_cls: 152.5506 loss_bbox: 126.1607 loss_dfl: 145.4054 2024/03/17 17:33:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:33:33 - mmengine - INFO - Epoch(train) [3][150/925] lr: 1.4051e-04 eta: 12:38:40 time: 0.6345 data_time: 0.0053 memory: 11199 grad_norm: 802.7132 loss: 421.4650 loss_cls: 153.6938 loss_bbox: 123.7724 loss_dfl: 143.9988 2024/03/17 17:34:05 - mmengine - INFO - Epoch(train) [3][200/925] lr: 1.4402e-04 eta: 12:38:04 time: 0.6299 data_time: 0.0047 memory: 11252 grad_norm: 797.9141 loss: 419.5157 loss_cls: 152.7610 loss_bbox: 122.9991 loss_dfl: 143.7556 2024/03/17 17:34:35 - mmengine - INFO - Epoch(train) [3][250/925] lr: 1.4754e-04 eta: 12:36:45 time: 0.6041 data_time: 0.0048 memory: 11652 grad_norm: 835.4820 loss: 425.8817 loss_cls: 152.6766 loss_bbox: 127.9376 loss_dfl: 145.2675 2024/03/17 17:35:06 - mmengine - INFO - Epoch(train) [3][300/925] lr: 1.5105e-04 eta: 12:35:51 time: 0.6184 data_time: 0.0044 memory: 11359 grad_norm: inf loss: 423.7941 loss_cls: 153.4184 loss_bbox: 125.2451 loss_dfl: 145.1307 2024/03/17 17:35:37 - mmengine - INFO - Epoch(train) [3][350/925] lr: 1.5456e-04 eta: 12:35:18 time: 0.6301 data_time: 0.0046 memory: 11386 grad_norm: 809.6737 loss: 418.8364 loss_cls: 152.2632 loss_bbox: 122.8275 loss_dfl: 143.7456 2024/03/17 17:36:08 - mmengine - INFO - Epoch(train) [3][400/925] lr: 1.5808e-04 eta: 12:34:11 time: 0.6088 data_time: 0.0046 memory: 11426 grad_norm: 893.5858 loss: 416.6340 loss_cls: 148.7658 loss_bbox: 124.5145 loss_dfl: 143.3537 2024/03/17 17:36:39 - mmengine - INFO - Epoch(train) [3][450/925] lr: 1.6159e-04 eta: 12:33:40 time: 0.6310 data_time: 0.0044 memory: 11826 grad_norm: 814.9145 loss: 427.7126 loss_cls: 154.1377 loss_bbox: 127.1855 loss_dfl: 146.3894 2024/03/17 17:37:12 - mmengine - INFO - Epoch(train) [3][500/925] lr: 1.6511e-04 eta: 12:33:41 time: 0.6519 data_time: 0.0051 memory: 11212 grad_norm: 771.7190 loss: 417.3815 loss_cls: 148.7133 loss_bbox: 124.4245 loss_dfl: 144.2438 2024/03/17 17:37:43 - mmengine - INFO - Epoch(train) [3][550/925] lr: 1.6862e-04 eta: 12:32:35 time: 0.6087 data_time: 0.0045 memory: 11359 grad_norm: 832.1459 loss: 421.8739 loss_cls: 153.1627 loss_bbox: 125.1520 loss_dfl: 143.5592 2024/03/17 17:38:14 - mmengine - INFO - Epoch(train) [3][600/925] lr: 1.7214e-04 eta: 12:32:07 time: 0.6328 data_time: 0.0049 memory: 11492 grad_norm: 824.6558 loss: 421.3447 loss_cls: 152.1040 loss_bbox: 125.7791 loss_dfl: 143.4616 2024/03/17 17:38:46 - mmengine - INFO - Epoch(train) [3][650/925] lr: 1.7565e-04 eta: 12:31:51 time: 0.6412 data_time: 0.0049 memory: 11546 grad_norm: 858.8713 loss: 422.4950 loss_cls: 153.4872 loss_bbox: 124.5599 loss_dfl: 144.4478 2024/03/17 17:39:18 - mmengine - INFO - Epoch(train) [3][700/925] lr: 1.7916e-04 eta: 12:31:11 time: 0.6256 data_time: 0.0045 memory: 11252 grad_norm: 853.7439 loss: 428.3626 loss_cls: 156.9317 loss_bbox: 125.1689 loss_dfl: 146.2619 2024/03/17 17:39:49 - mmengine - INFO - Epoch(train) [3][750/925] lr: 1.8268e-04 eta: 12:30:39 time: 0.6301 data_time: 0.0052 memory: 11279 grad_norm: 835.4324 loss: 423.7725 loss_cls: 153.3516 loss_bbox: 125.1878 loss_dfl: 145.2331 2024/03/17 17:40:22 - mmengine - INFO - Epoch(train) [3][800/925] lr: 1.8619e-04 eta: 12:30:38 time: 0.6533 data_time: 0.0053 memory: 11146 grad_norm: 769.0688 loss: 427.9667 loss_cls: 155.0055 loss_bbox: 128.5695 loss_dfl: 144.3917 2024/03/17 17:40:54 - mmengine - INFO - Epoch(train) [3][850/925] lr: 1.8971e-04 eta: 12:30:19 time: 0.6407 data_time: 0.0047 memory: 11572 grad_norm: 765.2902 loss: 430.5122 loss_cls: 157.2341 loss_bbox: 127.1481 loss_dfl: 146.1301 2024/03/17 17:41:25 - mmengine - INFO - Epoch(train) [3][900/925] lr: 1.9322e-04 eta: 12:29:36 time: 0.6226 data_time: 0.0055 memory: 11426 grad_norm: 876.1321 loss: 422.0359 loss_cls: 151.6932 loss_bbox: 125.9470 loss_dfl: 144.3957 2024/03/17 17:41:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:42:16 - mmengine - INFO - Epoch(train) [4][ 50/925] lr: 1.9258e-04 eta: 12:30:22 time: 0.7011 data_time: 0.0514 memory: 11546 grad_norm: 834.6873 loss: 424.2006 loss_cls: 154.4278 loss_bbox: 125.3695 loss_dfl: 144.4033 2024/03/17 17:42:48 - mmengine - INFO - Epoch(train) [4][100/925] lr: 1.9258e-04 eta: 12:29:47 time: 0.6291 data_time: 0.0025 memory: 11746 grad_norm: 827.0834 loss: 428.9582 loss_cls: 155.2370 loss_bbox: 127.8588 loss_dfl: 145.8624 2024/03/17 17:43:19 - mmengine - INFO - Epoch(train) [4][150/925] lr: 1.9258e-04 eta: 12:29:10 time: 0.6287 data_time: 0.0026 memory: 11532 grad_norm: 747.9365 loss: 426.0776 loss_cls: 154.6638 loss_bbox: 126.7956 loss_dfl: 144.6182 2024/03/17 17:43:51 - mmengine - INFO - Epoch(train) [4][200/925] lr: 1.9258e-04 eta: 12:28:38 time: 0.6323 data_time: 0.0024 memory: 11519 grad_norm: 746.1122 loss: 426.6658 loss_cls: 155.1355 loss_bbox: 127.0242 loss_dfl: 144.5061 2024/03/17 17:44:07 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:44:23 - mmengine - INFO - Epoch(train) [4][250/925] lr: 1.9258e-04 eta: 12:28:19 time: 0.6428 data_time: 0.0025 memory: 11492 grad_norm: 809.4609 loss: 438.1379 loss_cls: 161.2159 loss_bbox: 129.6426 loss_dfl: 147.2794 2024/03/17 17:44:54 - mmengine - INFO - Epoch(train) [4][300/925] lr: 1.9258e-04 eta: 12:27:17 time: 0.6060 data_time: 0.0025 memory: 11439 grad_norm: 779.8309 loss: 424.9575 loss_cls: 151.9879 loss_bbox: 128.4160 loss_dfl: 144.5536 2024/03/17 17:45:25 - mmengine - INFO - Epoch(train) [4][350/925] lr: 1.9258e-04 eta: 12:26:50 time: 0.6363 data_time: 0.0025 memory: 11292 grad_norm: 815.1611 loss: 427.1597 loss_cls: 155.5997 loss_bbox: 126.1378 loss_dfl: 145.4222 2024/03/17 17:45:57 - mmengine - INFO - Epoch(train) [4][400/925] lr: 1.9258e-04 eta: 12:26:21 time: 0.6350 data_time: 0.0024 memory: 11412 grad_norm: 828.8034 loss: 427.7790 loss_cls: 154.7038 loss_bbox: 127.8388 loss_dfl: 145.2364 2024/03/17 17:46:28 - mmengine - INFO - Epoch(train) [4][450/925] lr: 1.9258e-04 eta: 12:25:28 time: 0.6129 data_time: 0.0024 memory: 11239 grad_norm: 815.6897 loss: 420.4304 loss_cls: 152.1606 loss_bbox: 124.0411 loss_dfl: 144.2288 2024/03/17 17:47:00 - mmengine - INFO - Epoch(train) [4][500/925] lr: 1.9258e-04 eta: 12:25:01 time: 0.6362 data_time: 0.0024 memory: 11586 grad_norm: 738.4840 loss: 429.0100 loss_cls: 155.0292 loss_bbox: 127.6804 loss_dfl: 146.3004 2024/03/17 17:47:32 - mmengine - INFO - Epoch(train) [4][550/925] lr: 1.9258e-04 eta: 12:24:37 time: 0.6393 data_time: 0.0025 memory: 11572 grad_norm: 794.2187 loss: 429.8389 loss_cls: 154.9723 loss_bbox: 129.2508 loss_dfl: 145.6158 2024/03/17 17:48:03 - mmengine - INFO - Epoch(train) [4][600/925] lr: 1.9258e-04 eta: 12:23:55 time: 0.6223 data_time: 0.0025 memory: 11452 grad_norm: 814.1335 loss: 423.2079 loss_cls: 153.3322 loss_bbox: 125.0186 loss_dfl: 144.8571 2024/03/17 17:48:34 - mmengine - INFO - Epoch(train) [4][650/925] lr: 1.9258e-04 eta: 12:23:14 time: 0.6231 data_time: 0.0027 memory: 11519 grad_norm: 832.3848 loss: 428.1146 loss_cls: 154.8541 loss_bbox: 127.7873 loss_dfl: 145.4731 2024/03/17 17:49:06 - mmengine - INFO - Epoch(train) [4][700/925] lr: 1.9258e-04 eta: 12:22:46 time: 0.6346 data_time: 0.0025 memory: 11746 grad_norm: 846.6441 loss: 420.0644 loss_cls: 151.0353 loss_bbox: 124.6866 loss_dfl: 144.3425 2024/03/17 17:49:37 - mmengine - INFO - Epoch(train) [4][750/925] lr: 1.9258e-04 eta: 12:22:12 time: 0.6301 data_time: 0.0026 memory: 11332 grad_norm: 795.6777 loss: 425.0182 loss_cls: 153.8190 loss_bbox: 125.3501 loss_dfl: 145.8490 2024/03/17 17:50:08 - mmengine - INFO - Epoch(train) [4][800/925] lr: 1.9258e-04 eta: 12:21:23 time: 0.6138 data_time: 0.0023 memory: 11226 grad_norm: 812.3219 loss: 424.0792 loss_cls: 152.1856 loss_bbox: 127.2311 loss_dfl: 144.6625 2024/03/17 17:50:40 - mmengine - INFO - Epoch(train) [4][850/925] lr: 1.9258e-04 eta: 12:21:01 time: 0.6416 data_time: 0.0024 memory: 11359 grad_norm: 813.9349 loss: 424.2030 loss_cls: 153.7864 loss_bbox: 125.5875 loss_dfl: 144.8292 2024/03/17 17:51:12 - mmengine - INFO - Epoch(train) [4][900/925] lr: 1.9258e-04 eta: 12:20:32 time: 0.6345 data_time: 0.0024 memory: 11026 grad_norm: 792.2703 loss: 423.0218 loss_cls: 152.9619 loss_bbox: 125.3814 loss_dfl: 144.6786 2024/03/17 17:51:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:52:02 - mmengine - INFO - Epoch(train) [5][ 50/925] lr: 1.9258e-04 eta: 12:20:32 time: 0.6992 data_time: 0.0702 memory: 11626 grad_norm: 825.0880 loss: 420.5528 loss_cls: 150.3657 loss_bbox: 126.4511 loss_dfl: 143.7360 2024/03/17 17:52:35 - mmengine - INFO - Epoch(train) [5][100/925] lr: 1.9258e-04 eta: 12:20:23 time: 0.6564 data_time: 0.0090 memory: 11199 grad_norm: 776.6087 loss: 424.1378 loss_cls: 153.8277 loss_bbox: 125.6046 loss_dfl: 144.7056 2024/03/17 17:53:07 - mmengine - INFO - Epoch(train) [5][150/925] lr: 1.9258e-04 eta: 12:19:54 time: 0.6366 data_time: 0.0025 memory: 11292 grad_norm: 754.2292 loss: 426.1097 loss_cls: 154.6286 loss_bbox: 127.5369 loss_dfl: 143.9443 2024/03/17 17:53:38 - mmengine - INFO - Epoch(train) [5][200/925] lr: 1.9258e-04 eta: 12:19:21 time: 0.6304 data_time: 0.0025 memory: 11506 grad_norm: 786.6503 loss: 424.0257 loss_cls: 152.4332 loss_bbox: 126.8646 loss_dfl: 144.7279 2024/03/17 17:54:11 - mmengine - INFO - Epoch(train) [5][250/925] lr: 1.9258e-04 eta: 12:19:08 time: 0.6539 data_time: 0.0026 memory: 11319 grad_norm: 769.0601 loss: 423.4417 loss_cls: 153.1800 loss_bbox: 124.7853 loss_dfl: 145.4764 2024/03/17 17:54:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 17:54:44 - mmengine - INFO - Epoch(train) [5][300/925] lr: 1.9258e-04 eta: 12:18:53 time: 0.6527 data_time: 0.0024 memory: 11306 grad_norm: 829.3823 loss: 425.1213 loss_cls: 153.3570 loss_bbox: 126.8177 loss_dfl: 144.9467 2024/03/17 17:55:16 - mmengine - INFO - Epoch(train) [5][350/925] lr: 1.9258e-04 eta: 12:18:31 time: 0.6438 data_time: 0.0025 memory: 11239 grad_norm: 725.7086 loss: 423.6696 loss_cls: 154.3607 loss_bbox: 125.0914 loss_dfl: 144.2175 2024/03/17 17:55:49 - mmengine - INFO - Epoch(train) [5][400/925] lr: 1.9258e-04 eta: 12:18:30 time: 0.6699 data_time: 0.0025 memory: 11226 grad_norm: 747.7927 loss: 423.2709 loss_cls: 153.7157 loss_bbox: 125.5895 loss_dfl: 143.9657 2024/03/17 17:56:22 - mmengine - INFO - Epoch(train) [5][450/925] lr: 1.9258e-04 eta: 12:18:22 time: 0.6622 data_time: 0.0024 memory: 11452 grad_norm: 831.7910 loss: 424.8688 loss_cls: 155.3869 loss_bbox: 125.0534 loss_dfl: 144.4284 2024/03/17 17:56:54 - mmengine - INFO - Epoch(train) [5][500/925] lr: 1.9258e-04 eta: 12:17:56 time: 0.6414 data_time: 0.0027 memory: 11879 grad_norm: 803.0438 loss: 419.6435 loss_cls: 151.1151 loss_bbox: 124.9672 loss_dfl: 143.5612 2024/03/17 17:57:27 - mmengine - INFO - Epoch(train) [5][550/925] lr: 1.9258e-04 eta: 12:17:36 time: 0.6484 data_time: 0.0024 memory: 11692 grad_norm: 751.6821 loss: 423.1117 loss_cls: 152.4713 loss_bbox: 126.8387 loss_dfl: 143.8017 2024/03/17 17:58:00 - mmengine - INFO - Epoch(train) [5][600/925] lr: 1.9258e-04 eta: 12:17:23 time: 0.6577 data_time: 0.0023 memory: 11372 grad_norm: 764.0236 loss: 421.8332 loss_cls: 154.1210 loss_bbox: 124.2217 loss_dfl: 143.4904 2024/03/17 17:58:33 - mmengine - INFO - Epoch(train) [5][650/925] lr: 1.9258e-04 eta: 12:17:13 time: 0.6612 data_time: 0.0027 memory: 11759 grad_norm: 752.6500 loss: 418.5174 loss_cls: 150.2075 loss_bbox: 123.6746 loss_dfl: 144.6353 2024/03/17 17:59:04 - mmengine - INFO - Epoch(train) [5][700/925] lr: 1.9258e-04 eta: 12:16:34 time: 0.6270 data_time: 0.0023 memory: 11266 grad_norm: 732.4214 loss: 423.0499 loss_cls: 153.4975 loss_bbox: 124.1920 loss_dfl: 145.3603 2024/03/17 17:59:37 - mmengine - INFO - Epoch(train) [5][750/925] lr: 1.9258e-04 eta: 12:16:21 time: 0.6584 data_time: 0.0024 memory: 11626 grad_norm: 804.9019 loss: 428.6185 loss_cls: 154.2474 loss_bbox: 128.3695 loss_dfl: 146.0017 2024/03/17 18:00:10 - mmengine - INFO - Epoch(train) [5][800/925] lr: 1.9258e-04 eta: 12:16:10 time: 0.6618 data_time: 0.0027 memory: 11719 grad_norm: 772.6505 loss: 420.3704 loss_cls: 151.3785 loss_bbox: 124.0980 loss_dfl: 144.8939 2024/03/17 18:00:42 - mmengine - INFO - Epoch(train) [5][850/925] lr: 1.9258e-04 eta: 12:15:31 time: 0.6272 data_time: 0.0023 memory: 11319 grad_norm: 734.1689 loss: 424.6357 loss_cls: 153.4580 loss_bbox: 126.5653 loss_dfl: 144.6123 2024/03/17 18:01:15 - mmengine - INFO - Epoch(train) [5][900/925] lr: 1.9258e-04 eta: 12:15:16 time: 0.6567 data_time: 0.0023 memory: 11479 grad_norm: 750.8150 loss: 423.7134 loss_cls: 151.9889 loss_bbox: 125.7347 loss_dfl: 145.9899 2024/03/17 18:01:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:01:32 - mmengine - INFO - Saving checkpoint at 5 epochs 2024/03/17 18:01:34 - mmengine - WARNING - `save_param_scheduler` is True but `self.param_schedulers` is None, so skip saving parameter schedulers 2024/03/17 18:01:42 - mmengine - INFO - Epoch(val) [5][ 50/625] eta: 0:00:43 time: 0.0749 data_time: 0.0082 memory: 11479 2024/03/17 18:01:44 - mmengine - INFO - Epoch(val) [5][100/625] eta: 0:00:26 time: 0.0270 data_time: 0.0006 memory: 1709 2024/03/17 18:01:45 - mmengine - INFO - Epoch(val) [5][150/625] eta: 0:00:20 time: 0.0270 data_time: 0.0009 memory: 1709 2024/03/17 18:01:47 - mmengine - INFO - Epoch(val) [5][200/625] eta: 0:00:16 time: 0.0288 data_time: 0.0024 memory: 1709 2024/03/17 18:01:48 - mmengine - INFO - Epoch(val) [5][250/625] eta: 0:00:13 time: 0.0258 data_time: 0.0011 memory: 1709 2024/03/17 18:01:49 - mmengine - INFO - Epoch(val) [5][300/625] eta: 0:00:11 time: 0.0266 data_time: 0.0010 memory: 1709 2024/03/17 18:01:51 - mmengine - INFO - Epoch(val) [5][350/625] eta: 0:00:09 time: 0.0276 data_time: 0.0011 memory: 1709 2024/03/17 18:01:52 - mmengine - INFO - Epoch(val) [5][400/625] eta: 0:00:07 time: 0.0258 data_time: 0.0015 memory: 1709 2024/03/17 18:01:53 - mmengine - INFO - Epoch(val) [5][450/625] eta: 0:00:05 time: 0.0258 data_time: 0.0022 memory: 1709 2024/03/17 18:01:54 - mmengine - INFO - Epoch(val) [5][500/625] eta: 0:00:03 time: 0.0242 data_time: 0.0013 memory: 1709 2024/03/17 18:01:56 - mmengine - INFO - Epoch(val) [5][550/625] eta: 0:00:02 time: 0.0241 data_time: 0.0021 memory: 1709 2024/03/17 18:01:57 - mmengine - INFO - Epoch(val) [5][600/625] eta: 0:00:00 time: 0.0229 data_time: 0.0012 memory: 1709 2024/03/17 18:02:07 - mmengine - INFO - Evaluating bbox... 2024/03/17 18:03:19 - mmengine - INFO - bbox_mAP_copypaste: 0.437 0.594 0.477 0.266 0.486 0.569 2024/03/17 18:03:21 - mmengine - INFO - Epoch(val) [5][625/625] coco/bbox_mAP: 0.4370 coco/bbox_mAP_50: 0.5940 coco/bbox_mAP_75: 0.4770 coco/bbox_mAP_s: 0.2660 coco/bbox_mAP_m: 0.4860 coco/bbox_mAP_l: 0.5690 data_time: 0.0012 time: 0.0225 2024/03/17 18:03:56 - mmengine - INFO - Epoch(train) [6][ 50/925] lr: 1.9010e-04 eta: 12:15:22 time: 0.6957 data_time: 0.0682 memory: 11575 grad_norm: 693.9102 loss: 423.8030 loss_cls: 153.1104 loss_bbox: 125.5646 loss_dfl: 145.1280 2024/03/17 18:04:28 - mmengine - INFO - Epoch(train) [6][100/925] lr: 1.9010e-04 eta: 12:14:55 time: 0.6423 data_time: 0.0025 memory: 11522 grad_norm: 748.1908 loss: 430.4472 loss_cls: 155.7349 loss_bbox: 127.6086 loss_dfl: 147.1037 2024/03/17 18:05:00 - mmengine - INFO - Epoch(train) [6][150/925] lr: 1.9010e-04 eta: 12:14:31 time: 0.6472 data_time: 0.0025 memory: 11415 grad_norm: 712.1763 loss: 422.6713 loss_cls: 152.5887 loss_bbox: 126.3329 loss_dfl: 143.7496 2024/03/17 18:05:33 - mmengine - INFO - Epoch(train) [6][200/925] lr: 1.9010e-04 eta: 12:14:09 time: 0.6516 data_time: 0.0026 memory: 11695 grad_norm: 733.4101 loss: 425.4421 loss_cls: 153.8271 loss_bbox: 126.8418 loss_dfl: 144.7732 2024/03/17 18:06:04 - mmengine - INFO - Epoch(train) [6][250/925] lr: 1.9010e-04 eta: 12:13:28 time: 0.6232 data_time: 0.0022 memory: 11335 grad_norm: 781.0239 loss: 415.9441 loss_cls: 148.5675 loss_bbox: 124.9247 loss_dfl: 142.4519 2024/03/17 18:06:37 - mmengine - INFO - Epoch(train) [6][300/925] lr: 1.9010e-04 eta: 12:13:12 time: 0.6595 data_time: 0.0022 memory: 11535 grad_norm: 787.9199 loss: 419.3523 loss_cls: 150.8150 loss_bbox: 124.7613 loss_dfl: 143.7760 2024/03/17 18:07:11 - mmengine - INFO - Epoch(train) [6][350/925] lr: 1.9010e-04 eta: 12:13:04 time: 0.6703 data_time: 0.0027 memory: 11282 grad_norm: 777.1719 loss: 422.1892 loss_cls: 152.8465 loss_bbox: 125.1037 loss_dfl: 144.2390 2024/03/17 18:07:27 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:07:42 - mmengine - INFO - Epoch(train) [6][400/925] lr: 1.9010e-04 eta: 12:12:28 time: 0.6321 data_time: 0.0024 memory: 11309 grad_norm: 745.5718 loss: 418.4307 loss_cls: 149.4716 loss_bbox: 125.1792 loss_dfl: 143.7799 2024/03/17 18:08:15 - mmengine - INFO - Epoch(train) [6][450/925] lr: 1.9010e-04 eta: 12:12:06 time: 0.6518 data_time: 0.0024 memory: 11642 grad_norm: 742.4696 loss: 415.5535 loss_cls: 146.3535 loss_bbox: 126.0969 loss_dfl: 143.1031 2024/03/17 18:08:48 - mmengine - INFO - Epoch(train) [6][500/925] lr: 1.9010e-04 eta: 12:11:56 time: 0.6696 data_time: 0.0026 memory: 11389 grad_norm: 805.4832 loss: 423.0703 loss_cls: 151.3801 loss_bbox: 126.0272 loss_dfl: 145.6630 2024/03/17 18:09:20 - mmengine - INFO - Epoch(train) [6][550/925] lr: 1.9010e-04 eta: 12:11:24 time: 0.6380 data_time: 0.0020 memory: 11749 grad_norm: 747.6550 loss: 421.7049 loss_cls: 152.2792 loss_bbox: 124.6017 loss_dfl: 144.8240 2024/03/17 18:09:53 - mmengine - INFO - Epoch(train) [6][600/925] lr: 1.9010e-04 eta: 12:10:59 time: 0.6470 data_time: 0.0026 memory: 11589 grad_norm: 689.3159 loss: 417.8456 loss_cls: 149.3232 loss_bbox: 124.2450 loss_dfl: 144.2775 2024/03/17 18:10:26 - mmengine - INFO - Epoch(train) [6][650/925] lr: 1.9010e-04 eta: 12:10:43 time: 0.6628 data_time: 0.0024 memory: 11255 grad_norm: 798.4644 loss: 422.2515 loss_cls: 150.9813 loss_bbox: 126.6722 loss_dfl: 144.5980 2024/03/17 18:10:58 - mmengine - INFO - Epoch(train) [6][700/925] lr: 1.9010e-04 eta: 12:10:19 time: 0.6494 data_time: 0.0025 memory: 11535 grad_norm: 808.5383 loss: 418.3651 loss_cls: 149.4241 loss_bbox: 124.2771 loss_dfl: 144.6638 2024/03/17 18:11:31 - mmengine - INFO - Epoch(train) [6][750/925] lr: 1.9010e-04 eta: 12:09:54 time: 0.6488 data_time: 0.0028 memory: 11335 grad_norm: 740.2031 loss: 428.9423 loss_cls: 156.2834 loss_bbox: 127.3761 loss_dfl: 145.2828 2024/03/17 18:12:03 - mmengine - INFO - Epoch(train) [6][800/925] lr: 1.9010e-04 eta: 12:09:33 time: 0.6559 data_time: 0.0025 memory: 11375 grad_norm: 732.2927 loss: 422.3489 loss_cls: 152.9939 loss_bbox: 125.3904 loss_dfl: 143.9646 2024/03/17 18:12:37 - mmengine - INFO - Epoch(train) [6][850/925] lr: 1.9010e-04 eta: 12:09:15 time: 0.6609 data_time: 0.0026 memory: 11429 grad_norm: 763.6251 loss: 421.6234 loss_cls: 151.1110 loss_bbox: 126.7529 loss_dfl: 143.7595 2024/03/17 18:13:08 - mmengine - INFO - Epoch(train) [6][900/925] lr: 1.9010e-04 eta: 12:08:38 time: 0.6299 data_time: 0.0024 memory: 11349 grad_norm: 781.1061 loss: 423.9430 loss_cls: 153.0738 loss_bbox: 126.2319 loss_dfl: 144.6374 2024/03/17 18:13:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:14:01 - mmengine - INFO - Epoch(train) [7][ 50/925] lr: 1.8762e-04 eta: 12:08:49 time: 0.7305 data_time: 0.0650 memory: 11349 grad_norm: 746.9942 loss: 415.0800 loss_cls: 146.3293 loss_bbox: 125.1956 loss_dfl: 143.5551 2024/03/17 18:14:33 - mmengine - INFO - Epoch(train) [7][100/925] lr: 1.8762e-04 eta: 12:08:15 time: 0.6369 data_time: 0.0024 memory: 11562 grad_norm: 687.3009 loss: 415.5975 loss_cls: 147.0613 loss_bbox: 125.7222 loss_dfl: 142.8141 2024/03/17 18:15:04 - mmengine - INFO - Epoch(train) [7][150/925] lr: 1.8762e-04 eta: 12:07:31 time: 0.6193 data_time: 0.0023 memory: 11349 grad_norm: 777.4894 loss: 426.2337 loss_cls: 153.5605 loss_bbox: 126.8151 loss_dfl: 145.8581 2024/03/17 18:15:37 - mmengine - INFO - Epoch(train) [7][200/925] lr: 1.8762e-04 eta: 12:07:13 time: 0.6626 data_time: 0.0027 memory: 11389 grad_norm: inf loss: 418.2943 loss_cls: 146.4112 loss_bbox: 127.2880 loss_dfl: 144.5951 2024/03/17 18:16:09 - mmengine - INFO - Epoch(train) [7][250/925] lr: 1.8762e-04 eta: 12:06:43 time: 0.6417 data_time: 0.0023 memory: 11309 grad_norm: 710.0954 loss: 416.9542 loss_cls: 147.0898 loss_bbox: 125.3405 loss_dfl: 144.5240 2024/03/17 18:16:41 - mmengine - INFO - Epoch(train) [7][300/925] lr: 1.8762e-04 eta: 12:06:02 time: 0.6239 data_time: 0.0026 memory: 11442 grad_norm: 748.1104 loss: 414.1437 loss_cls: 145.2552 loss_bbox: 125.7827 loss_dfl: 143.1058 2024/03/17 18:17:13 - mmengine - INFO - Epoch(train) [7][350/925] lr: 1.8762e-04 eta: 12:05:37 time: 0.6510 data_time: 0.0026 memory: 11375 grad_norm: 713.7202 loss: 416.0311 loss_cls: 150.0416 loss_bbox: 122.7115 loss_dfl: 143.2780 2024/03/17 18:17:46 - mmengine - INFO - Epoch(train) [7][400/925] lr: 1.8762e-04 eta: 12:05:10 time: 0.6479 data_time: 0.0024 memory: 11309 grad_norm: 753.6196 loss: 420.1701 loss_cls: 150.6716 loss_bbox: 125.6709 loss_dfl: 143.8276 2024/03/17 18:18:17 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:18:17 - mmengine - INFO - Epoch(train) [7][450/925] lr: 1.8762e-04 eta: 12:04:32 time: 0.6292 data_time: 0.0026 memory: 11189 grad_norm: 792.2060 loss: 417.8119 loss_cls: 149.9511 loss_bbox: 124.4483 loss_dfl: 143.4125 2024/03/17 18:18:49 - mmengine - INFO - Epoch(train) [7][500/925] lr: 1.8762e-04 eta: 12:03:55 time: 0.6302 data_time: 0.0024 memory: 11575 grad_norm: 745.5465 loss: 418.3299 loss_cls: 150.1156 loss_bbox: 124.4741 loss_dfl: 143.7402 2024/03/17 18:19:21 - mmengine - INFO - Epoch(train) [7][550/925] lr: 1.8762e-04 eta: 12:03:27 time: 0.6473 data_time: 0.0024 memory: 11282 grad_norm: 728.2655 loss: 420.2570 loss_cls: 149.4169 loss_bbox: 127.0997 loss_dfl: 143.7403 2024/03/17 18:19:54 - mmengine - INFO - Epoch(train) [7][600/925] lr: 1.8762e-04 eta: 12:03:02 time: 0.6512 data_time: 0.0026 memory: 11655 grad_norm: 749.5302 loss: 419.6252 loss_cls: 149.6030 loss_bbox: 125.6868 loss_dfl: 144.3354 2024/03/17 18:20:26 - mmengine - INFO - Epoch(train) [7][650/925] lr: 1.8762e-04 eta: 12:02:32 time: 0.6441 data_time: 0.0026 memory: 11655 grad_norm: 810.5268 loss: 416.3148 loss_cls: 147.7937 loss_bbox: 124.5040 loss_dfl: 144.0172 2024/03/17 18:20:58 - mmengine - INFO - Epoch(train) [7][700/925] lr: 1.8762e-04 eta: 12:02:03 time: 0.6442 data_time: 0.0023 memory: 11335 grad_norm: 768.8950 loss: 422.0382 loss_cls: 151.4276 loss_bbox: 125.7429 loss_dfl: 144.8677 2024/03/17 18:21:30 - mmengine - INFO - Epoch(train) [7][750/925] lr: 1.8762e-04 eta: 12:01:34 time: 0.6447 data_time: 0.0026 memory: 11189 grad_norm: 748.1432 loss: 417.5990 loss_cls: 148.6044 loss_bbox: 125.2368 loss_dfl: 143.7578 2024/03/17 18:22:02 - mmengine - INFO - Epoch(train) [7][800/925] lr: 1.8762e-04 eta: 12:00:59 time: 0.6342 data_time: 0.0027 memory: 11509 grad_norm: 769.5269 loss: 417.3207 loss_cls: 148.8039 loss_bbox: 124.4733 loss_dfl: 144.0435 2024/03/17 18:22:35 - mmengine - INFO - Epoch(train) [7][850/925] lr: 1.8762e-04 eta: 12:00:38 time: 0.6604 data_time: 0.0123 memory: 11509 grad_norm: 764.7506 loss: 420.1087 loss_cls: 149.6314 loss_bbox: 126.0404 loss_dfl: 144.4368 2024/03/17 18:23:08 - mmengine - INFO - Epoch(train) [7][900/925] lr: 1.8762e-04 eta: 12:00:18 time: 0.6625 data_time: 0.0027 memory: 11655 grad_norm: 716.5335 loss: 415.5773 loss_cls: 148.6036 loss_bbox: 124.0057 loss_dfl: 142.9680 2024/03/17 18:23:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:23:59 - mmengine - INFO - Epoch(train) [8][ 50/925] lr: 1.8515e-04 eta: 11:59:52 time: 0.6977 data_time: 0.0828 memory: 11842 grad_norm: 742.8308 loss: 415.8147 loss_cls: 146.3334 loss_bbox: 125.1511 loss_dfl: 144.3303 2024/03/17 18:24:31 - mmengine - INFO - Epoch(train) [8][100/925] lr: 1.8515e-04 eta: 11:59:26 time: 0.6522 data_time: 0.0025 memory: 11695 grad_norm: 730.6083 loss: 423.4334 loss_cls: 151.8537 loss_bbox: 126.8996 loss_dfl: 144.6801 2024/03/17 18:25:03 - mmengine - INFO - Epoch(train) [8][150/925] lr: 1.8515e-04 eta: 11:58:54 time: 0.6397 data_time: 0.0026 memory: 11455 grad_norm: 714.8849 loss: 421.7135 loss_cls: 149.4166 loss_bbox: 127.5439 loss_dfl: 144.7531 2024/03/17 18:25:34 - mmengine - INFO - Epoch(train) [8][200/925] lr: 1.8515e-04 eta: 11:58:12 time: 0.6202 data_time: 0.0025 memory: 11389 grad_norm: 685.4612 loss: 411.9976 loss_cls: 146.2546 loss_bbox: 123.6009 loss_dfl: 142.1421 2024/03/17 18:26:07 - mmengine - INFO - Epoch(train) [8][250/925] lr: 1.8515e-04 eta: 11:57:47 time: 0.6542 data_time: 0.0027 memory: 11189 grad_norm: 684.1932 loss: 417.6517 loss_cls: 148.0319 loss_bbox: 125.6867 loss_dfl: 143.9331 2024/03/17 18:26:39 - mmengine - INFO - Epoch(train) [8][300/925] lr: 1.8515e-04 eta: 11:57:12 time: 0.6351 data_time: 0.0024 memory: 11455 grad_norm: 710.2559 loss: 419.1514 loss_cls: 149.0109 loss_bbox: 125.3930 loss_dfl: 144.7475 2024/03/17 18:27:11 - mmengine - INFO - Epoch(train) [8][350/925] lr: 1.8515e-04 eta: 11:56:39 time: 0.6379 data_time: 0.0027 memory: 11695 grad_norm: 736.8486 loss: 419.0588 loss_cls: 149.4594 loss_bbox: 125.5641 loss_dfl: 144.0352 2024/03/17 18:27:42 - mmengine - INFO - Epoch(train) [8][400/925] lr: 1.8515e-04 eta: 11:56:01 time: 0.6283 data_time: 0.0025 memory: 11362 grad_norm: 757.3530 loss: 422.2650 loss_cls: 149.7960 loss_bbox: 126.4889 loss_dfl: 145.9802 2024/03/17 18:28:14 - mmengine - INFO - Epoch(train) [8][450/925] lr: 1.8515e-04 eta: 11:55:28 time: 0.6382 data_time: 0.0023 memory: 11429 grad_norm: 776.4622 loss: 416.2438 loss_cls: 148.4348 loss_bbox: 124.3789 loss_dfl: 143.4301 2024/03/17 18:28:46 - mmengine - INFO - Epoch(train) [8][500/925] lr: 1.8515e-04 eta: 11:54:50 time: 0.6261 data_time: 0.0024 memory: 11682 grad_norm: 730.6615 loss: 422.3620 loss_cls: 150.4732 loss_bbox: 127.5436 loss_dfl: 144.3452 2024/03/17 18:29:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:29:18 - mmengine - INFO - Epoch(train) [8][550/925] lr: 1.8515e-04 eta: 11:54:20 time: 0.6456 data_time: 0.0029 memory: 11495 grad_norm: 736.2559 loss: 421.0016 loss_cls: 151.0115 loss_bbox: 126.1237 loss_dfl: 143.8664 2024/03/17 18:29:51 - mmengine - INFO - Epoch(train) [8][600/925] lr: 1.8515e-04 eta: 11:53:55 time: 0.6549 data_time: 0.0026 memory: 11135 grad_norm: 712.5046 loss: 408.3596 loss_cls: 143.1496 loss_bbox: 123.5516 loss_dfl: 141.6584 2024/03/17 18:30:22 - mmengine - INFO - Epoch(train) [8][650/925] lr: 1.8515e-04 eta: 11:53:15 time: 0.6214 data_time: 0.0022 memory: 11095 grad_norm: 778.1753 loss: 410.7086 loss_cls: 143.4605 loss_bbox: 124.0388 loss_dfl: 143.2094 2024/03/17 18:30:53 - mmengine - INFO - Epoch(train) [8][700/925] lr: 1.8515e-04 eta: 11:52:34 time: 0.6207 data_time: 0.0025 memory: 11375 grad_norm: 761.1747 loss: 416.8511 loss_cls: 148.6517 loss_bbox: 123.3587 loss_dfl: 144.8407 2024/03/17 18:31:26 - mmengine - INFO - Epoch(train) [8][750/925] lr: 1.8515e-04 eta: 11:52:12 time: 0.6614 data_time: 0.0028 memory: 11282 grad_norm: 699.5513 loss: 420.3820 loss_cls: 151.4092 loss_bbox: 124.5084 loss_dfl: 144.4643 2024/03/17 18:31:58 - mmengine - INFO - Epoch(train) [8][800/925] lr: 1.8515e-04 eta: 11:51:43 time: 0.6466 data_time: 0.0027 memory: 11322 grad_norm: 722.0369 loss: 420.8297 loss_cls: 150.1790 loss_bbox: 126.3271 loss_dfl: 144.3235 2024/03/17 18:32:29 - mmengine - INFO - Epoch(train) [8][850/925] lr: 1.8515e-04 eta: 11:51:00 time: 0.6168 data_time: 0.0023 memory: 11402 grad_norm: 808.4380 loss: 409.4206 loss_cls: 143.7676 loss_bbox: 123.0746 loss_dfl: 142.5784 2024/03/17 18:33:01 - mmengine - INFO - Epoch(train) [8][900/925] lr: 1.8515e-04 eta: 11:50:32 time: 0.6490 data_time: 0.0027 memory: 11415 grad_norm: 743.5161 loss: 417.9836 loss_cls: 147.7861 loss_bbox: 125.5162 loss_dfl: 144.6812 2024/03/17 18:33:18 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:33:53 - mmengine - INFO - Epoch(train) [9][ 50/925] lr: 1.8268e-04 eta: 11:50:12 time: 0.7014 data_time: 0.0769 memory: 11415 grad_norm: 789.6555 loss: 412.6316 loss_cls: 145.4645 loss_bbox: 124.0652 loss_dfl: 143.1019 2024/03/17 18:34:25 - mmengine - INFO - Epoch(train) [9][100/925] lr: 1.8268e-04 eta: 11:49:37 time: 0.6335 data_time: 0.0024 memory: 11162 grad_norm: 698.5610 loss: 407.1733 loss_cls: 143.6022 loss_bbox: 120.9620 loss_dfl: 142.6090 2024/03/17 18:34:57 - mmengine - INFO - Epoch(train) [9][150/925] lr: 1.8268e-04 eta: 11:49:09 time: 0.6482 data_time: 0.0021 memory: 11522 grad_norm: 684.2365 loss: 422.2475 loss_cls: 151.8649 loss_bbox: 125.6392 loss_dfl: 144.7433 2024/03/17 18:35:29 - mmengine - INFO - Epoch(train) [9][200/925] lr: 1.8268e-04 eta: 11:48:35 time: 0.6356 data_time: 0.0023 memory: 11495 grad_norm: 703.3192 loss: 415.5710 loss_cls: 147.8986 loss_bbox: 123.2341 loss_dfl: 144.4383 2024/03/17 18:36:01 - mmengine - INFO - Epoch(train) [9][250/925] lr: 1.8268e-04 eta: 11:47:58 time: 0.6310 data_time: 0.0024 memory: 11549 grad_norm: 747.2444 loss: 415.7934 loss_cls: 147.5586 loss_bbox: 123.9571 loss_dfl: 144.2777 2024/03/17 18:36:34 - mmengine - INFO - Epoch(train) [9][300/925] lr: 1.8268e-04 eta: 11:47:36 time: 0.6615 data_time: 0.0026 memory: 11175 grad_norm: 716.1247 loss: 412.7278 loss_cls: 147.0609 loss_bbox: 122.8504 loss_dfl: 142.8165 2024/03/17 18:37:06 - mmengine - INFO - Epoch(train) [9][350/925] lr: 1.8268e-04 eta: 11:47:04 time: 0.6407 data_time: 0.0025 memory: 11269 grad_norm: 693.8352 loss: 413.2772 loss_cls: 146.3736 loss_bbox: 124.7392 loss_dfl: 142.1644 2024/03/17 18:37:38 - mmengine - INFO - Epoch(train) [9][400/925] lr: 1.8268e-04 eta: 11:46:29 time: 0.6342 data_time: 0.0029 memory: 11229 grad_norm: 721.4560 loss: 418.2810 loss_cls: 149.5699 loss_bbox: 124.5645 loss_dfl: 144.1466 2024/03/17 18:38:10 - mmengine - INFO - Epoch(train) [9][450/925] lr: 1.8268e-04 eta: 11:45:58 time: 0.6427 data_time: 0.0023 memory: 11135 grad_norm: 687.3252 loss: 419.3354 loss_cls: 150.1362 loss_bbox: 125.0538 loss_dfl: 144.1453 2024/03/17 18:38:42 - mmengine - INFO - Epoch(train) [9][500/925] lr: 1.8268e-04 eta: 11:45:28 time: 0.6456 data_time: 0.0025 memory: 11375 grad_norm: 664.6664 loss: 413.4875 loss_cls: 145.5245 loss_bbox: 125.1290 loss_dfl: 142.8340 2024/03/17 18:39:14 - mmengine - INFO - Epoch(train) [9][550/925] lr: 1.8268e-04 eta: 11:44:59 time: 0.6459 data_time: 0.0027 memory: 11255 grad_norm: 724.8513 loss: 412.8152 loss_cls: 145.4798 loss_bbox: 124.6357 loss_dfl: 142.6997 2024/03/17 18:39:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:39:46 - mmengine - INFO - Epoch(train) [9][600/925] lr: 1.8268e-04 eta: 11:44:24 time: 0.6346 data_time: 0.0026 memory: 11829 grad_norm: 718.5605 loss: 419.8103 loss_cls: 149.3963 loss_bbox: 124.8059 loss_dfl: 145.6082 2024/03/17 18:40:19 - mmengine - INFO - Epoch(train) [9][650/925] lr: 1.8268e-04 eta: 11:43:59 time: 0.6573 data_time: 0.0027 memory: 11109 grad_norm: 722.9648 loss: 408.1790 loss_cls: 143.0305 loss_bbox: 122.1787 loss_dfl: 142.9698 2024/03/17 18:40:50 - mmengine - INFO - Epoch(train) [9][700/925] lr: 1.8268e-04 eta: 11:43:21 time: 0.6259 data_time: 0.0025 memory: 11255 grad_norm: 708.4336 loss: 415.2644 loss_cls: 147.4395 loss_bbox: 123.4875 loss_dfl: 144.3374 2024/03/17 18:41:22 - mmengine - INFO - Epoch(train) [9][750/925] lr: 1.8268e-04 eta: 11:42:50 time: 0.6425 data_time: 0.0025 memory: 11215 grad_norm: inf loss: 411.0424 loss_cls: 145.0835 loss_bbox: 123.2369 loss_dfl: 142.7221 2024/03/17 18:41:55 - mmengine - INFO - Epoch(train) [9][800/925] lr: 1.8268e-04 eta: 11:42:26 time: 0.6601 data_time: 0.0026 memory: 11402 grad_norm: 677.0335 loss: 416.9467 loss_cls: 147.3805 loss_bbox: 125.7713 loss_dfl: 143.7949 2024/03/17 18:42:28 - mmengine - INFO - Epoch(train) [9][850/925] lr: 1.8268e-04 eta: 11:41:56 time: 0.6462 data_time: 0.0025 memory: 11362 grad_norm: 785.0204 loss: 417.0958 loss_cls: 146.7562 loss_bbox: 125.7463 loss_dfl: 144.5934 2024/03/17 18:43:00 - mmengine - INFO - Epoch(train) [9][900/925] lr: 1.8268e-04 eta: 11:41:22 time: 0.6367 data_time: 0.0026 memory: 11375 grad_norm: 744.5932 loss: 415.6268 loss_cls: 146.7069 loss_bbox: 125.0686 loss_dfl: 143.8514 2024/03/17 18:43:15 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:43:53 - mmengine - INFO - Epoch(train) [10][ 50/925] lr: 1.8020e-04 eta: 11:41:12 time: 0.7468 data_time: 0.0822 memory: 11402 grad_norm: 706.4275 loss: 406.9972 loss_cls: 143.8376 loss_bbox: 121.4653 loss_dfl: 141.6943 2024/03/17 18:44:25 - mmengine - INFO - Epoch(train) [10][100/925] lr: 1.8020e-04 eta: 11:40:38 time: 0.6382 data_time: 0.0024 memory: 11669 grad_norm: 712.5875 loss: 417.7718 loss_cls: 147.2936 loss_bbox: 124.9846 loss_dfl: 145.4936 2024/03/17 18:44:57 - mmengine - INFO - Epoch(train) [10][150/925] lr: 1.8020e-04 eta: 11:40:06 time: 0.6403 data_time: 0.0023 memory: 11842 grad_norm: 717.8040 loss: 411.3400 loss_cls: 145.9731 loss_bbox: 122.2897 loss_dfl: 143.0772 2024/03/17 18:45:30 - mmengine - INFO - Epoch(train) [10][200/925] lr: 1.8020e-04 eta: 11:39:44 time: 0.6677 data_time: 0.0026 memory: 11455 grad_norm: 728.2967 loss: 415.7473 loss_cls: 148.4318 loss_bbox: 123.6552 loss_dfl: 143.6603 2024/03/17 18:46:02 - mmengine - INFO - Epoch(train) [10][250/925] lr: 1.8020e-04 eta: 11:39:12 time: 0.6415 data_time: 0.0025 memory: 11442 grad_norm: 700.2186 loss: 415.5068 loss_cls: 147.8654 loss_bbox: 122.8629 loss_dfl: 144.7785 2024/03/17 18:46:35 - mmengine - INFO - Epoch(train) [10][300/925] lr: 1.8020e-04 eta: 11:38:47 time: 0.6580 data_time: 0.0024 memory: 11215 grad_norm: 780.2626 loss: 415.6697 loss_cls: 146.0487 loss_bbox: 125.9802 loss_dfl: 143.6408 2024/03/17 18:47:08 - mmengine - INFO - Epoch(train) [10][350/925] lr: 1.8020e-04 eta: 11:38:19 time: 0.6535 data_time: 0.0025 memory: 11429 grad_norm: 789.7792 loss: 412.9279 loss_cls: 144.2698 loss_bbox: 124.9496 loss_dfl: 143.7085 2024/03/17 18:47:41 - mmengine - INFO - Epoch(train) [10][400/925] lr: 1.8020e-04 eta: 11:37:51 time: 0.6519 data_time: 0.0026 memory: 11775 grad_norm: 676.5082 loss: 416.2780 loss_cls: 147.4706 loss_bbox: 124.9350 loss_dfl: 143.8725 2024/03/17 18:48:14 - mmengine - INFO - Epoch(train) [10][450/925] lr: 1.8020e-04 eta: 11:37:26 time: 0.6617 data_time: 0.0026 memory: 11295 grad_norm: 766.1320 loss: 406.6033 loss_cls: 142.3817 loss_bbox: 122.2311 loss_dfl: 141.9905 2024/03/17 18:48:46 - mmengine - INFO - Epoch(train) [10][500/925] lr: 1.8020e-04 eta: 11:36:55 time: 0.6426 data_time: 0.0025 memory: 11389 grad_norm: 743.5051 loss: 405.4648 loss_cls: 140.1458 loss_bbox: 122.6517 loss_dfl: 142.6673 2024/03/17 18:49:20 - mmengine - INFO - Epoch(train) [10][550/925] lr: 1.8020e-04 eta: 11:36:34 time: 0.6725 data_time: 0.0028 memory: 11282 grad_norm: 715.5646 loss: 412.3757 loss_cls: 144.9659 loss_bbox: 124.2901 loss_dfl: 143.1197 2024/03/17 18:49:52 - mmengine - INFO - Epoch(train) [10][600/925] lr: 1.8020e-04 eta: 11:36:02 time: 0.6428 data_time: 0.0024 memory: 11442 grad_norm: 725.4100 loss: 414.1907 loss_cls: 144.1239 loss_bbox: 126.0181 loss_dfl: 144.0487 2024/03/17 18:50:24 - mmengine - INFO - Epoch(train) [10][650/925] lr: 1.8020e-04 eta: 11:35:35 time: 0.6538 data_time: 0.0024 memory: 11389 grad_norm: 743.1272 loss: 409.9315 loss_cls: 144.2021 loss_bbox: 122.5327 loss_dfl: 143.1967 2024/03/17 18:50:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:50:58 - mmengine - INFO - Epoch(train) [10][700/925] lr: 1.8020e-04 eta: 11:35:12 time: 0.6675 data_time: 0.0027 memory: 11202 grad_norm: 684.8067 loss: 416.2436 loss_cls: 145.8649 loss_bbox: 125.0931 loss_dfl: 145.2856 2024/03/17 18:51:31 - mmengine - INFO - Epoch(train) [10][750/925] lr: 1.8020e-04 eta: 11:34:46 time: 0.6586 data_time: 0.0028 memory: 11642 grad_norm: 791.3278 loss: 415.9556 loss_cls: 145.4748 loss_bbox: 126.2505 loss_dfl: 144.2303 2024/03/17 18:52:03 - mmengine - INFO - Epoch(train) [10][800/925] lr: 1.8020e-04 eta: 11:34:17 time: 0.6507 data_time: 0.0027 memory: 11309 grad_norm: 746.6448 loss: 409.7895 loss_cls: 144.3901 loss_bbox: 122.7597 loss_dfl: 142.6397 2024/03/17 18:52:36 - mmengine - INFO - Epoch(train) [10][850/925] lr: 1.8020e-04 eta: 11:33:49 time: 0.6542 data_time: 0.0026 memory: 11389 grad_norm: 681.7952 loss: 416.4924 loss_cls: 147.2473 loss_bbox: 125.4873 loss_dfl: 143.7578 2024/03/17 18:53:08 - mmengine - INFO - Epoch(train) [10][900/925] lr: 1.8020e-04 eta: 11:33:17 time: 0.6435 data_time: 0.0024 memory: 11215 grad_norm: 745.4739 loss: 409.6183 loss_cls: 142.2448 loss_bbox: 124.3999 loss_dfl: 142.9736 2024/03/17 18:53:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 18:53:24 - mmengine - INFO - Saving checkpoint at 10 epochs 2024/03/17 18:53:32 - mmengine - INFO - Epoch(val) [10][ 50/625] eta: 0:00:13 time: 0.0242 data_time: 0.0027 memory: 11135 2024/03/17 18:53:33 - mmengine - INFO - Epoch(val) [10][100/625] eta: 0:00:12 time: 0.0235 data_time: 0.0021 memory: 1709 2024/03/17 18:53:35 - mmengine - INFO - Epoch(val) [10][150/625] eta: 0:00:11 time: 0.0241 data_time: 0.0026 memory: 1709 2024/03/17 18:53:36 - mmengine - INFO - Epoch(val) [10][200/625] eta: 0:00:10 time: 0.0241 data_time: 0.0026 memory: 1709 2024/03/17 18:53:37 - mmengine - INFO - Epoch(val) [10][250/625] eta: 0:00:09 time: 0.0244 data_time: 0.0022 memory: 1709 2024/03/17 18:53:38 - mmengine - INFO - Epoch(val) [10][300/625] eta: 0:00:07 time: 0.0244 data_time: 0.0021 memory: 1709 2024/03/17 18:53:39 - mmengine - INFO - Epoch(val) [10][350/625] eta: 0:00:06 time: 0.0247 data_time: 0.0023 memory: 1709 2024/03/17 18:53:41 - mmengine - INFO - Epoch(val) [10][400/625] eta: 0:00:05 time: 0.0267 data_time: 0.0022 memory: 1709 2024/03/17 18:53:42 - mmengine - INFO - Epoch(val) [10][450/625] eta: 0:00:04 time: 0.0255 data_time: 0.0017 memory: 1709 2024/03/17 18:53:43 - mmengine - INFO - Epoch(val) [10][500/625] eta: 0:00:03 time: 0.0254 data_time: 0.0011 memory: 1709 2024/03/17 18:53:45 - mmengine - INFO - Epoch(val) [10][550/625] eta: 0:00:01 time: 0.0242 data_time: 0.0014 memory: 1709 2024/03/17 18:53:46 - mmengine - INFO - Epoch(val) [10][600/625] eta: 0:00:00 time: 0.0226 data_time: 0.0012 memory: 1709 2024/03/17 18:53:59 - mmengine - INFO - Evaluating bbox... 2024/03/17 18:55:24 - mmengine - INFO - bbox_mAP_copypaste: 0.488 0.652 0.537 0.310 0.538 0.627 2024/03/17 18:55:26 - mmengine - INFO - Epoch(val) [10][625/625] coco/bbox_mAP: 0.4880 coco/bbox_mAP_50: 0.6520 coco/bbox_mAP_75: 0.5370 coco/bbox_mAP_s: 0.3100 coco/bbox_mAP_m: 0.5380 coco/bbox_mAP_l: 0.6270 data_time: 0.0010 time: 0.0234 2024/03/17 18:56:03 - mmengine - INFO - Epoch(train) [11][ 50/925] lr: 1.7772e-04 eta: 11:32:56 time: 0.7296 data_time: 0.0877 memory: 11215 grad_norm: 723.5383 loss: 405.3456 loss_cls: 141.6360 loss_bbox: 121.2787 loss_dfl: 142.4310 2024/03/17 18:56:36 - mmengine - INFO - Epoch(train) [11][100/925] lr: 1.7772e-04 eta: 11:32:34 time: 0.6706 data_time: 0.0026 memory: 11762 grad_norm: 700.6855 loss: 413.2992 loss_cls: 145.5996 loss_bbox: 125.3225 loss_dfl: 142.3772 2024/03/17 18:57:09 - mmengine - INFO - Epoch(train) [11][150/925] lr: 1.7772e-04 eta: 11:32:06 time: 0.6538 data_time: 0.0024 memory: 11202 grad_norm: 750.0764 loss: 411.4146 loss_cls: 145.3316 loss_bbox: 123.2169 loss_dfl: 142.8661 2024/03/17 18:57:42 - mmengine - INFO - Epoch(train) [11][200/925] lr: 1.7772e-04 eta: 11:31:41 time: 0.6652 data_time: 0.0027 memory: 11162 grad_norm: 778.8097 loss: 410.7533 loss_cls: 143.2429 loss_bbox: 124.4602 loss_dfl: 143.0502 2024/03/17 18:58:15 - mmengine - INFO - Epoch(train) [11][250/925] lr: 1.7772e-04 eta: 11:31:11 time: 0.6494 data_time: 0.0026 memory: 12016 grad_norm: 751.7463 loss: 409.5708 loss_cls: 142.7894 loss_bbox: 124.2449 loss_dfl: 142.5365 2024/03/17 18:58:47 - mmengine - INFO - Epoch(train) [11][300/925] lr: 1.7772e-04 eta: 11:30:42 time: 0.6513 data_time: 0.0028 memory: 11239 grad_norm: 654.5617 loss: 403.2639 loss_cls: 141.4558 loss_bbox: 120.0466 loss_dfl: 141.7616 2024/03/17 18:59:21 - mmengine - INFO - Epoch(train) [11][350/925] lr: 1.7772e-04 eta: 11:30:17 time: 0.6645 data_time: 0.0026 memory: 11492 grad_norm: 747.0761 loss: 409.6031 loss_cls: 143.9985 loss_bbox: 123.1970 loss_dfl: 142.4076 2024/03/17 18:59:52 - mmengine - INFO - Epoch(train) [11][400/925] lr: 1.7772e-04 eta: 11:29:41 time: 0.6325 data_time: 0.0026 memory: 11532 grad_norm: 734.2077 loss: 414.0147 loss_cls: 145.4994 loss_bbox: 125.1198 loss_dfl: 143.3954 2024/03/17 19:00:25 - mmengine - INFO - Epoch(train) [11][450/925] lr: 1.7772e-04 eta: 11:29:11 time: 0.6498 data_time: 0.0024 memory: 11412 grad_norm: 691.5342 loss: 409.0431 loss_cls: 142.7561 loss_bbox: 123.4783 loss_dfl: 142.8087 2024/03/17 19:00:58 - mmengine - INFO - Epoch(train) [11][500/925] lr: 1.7772e-04 eta: 11:28:43 time: 0.6565 data_time: 0.0024 memory: 11412 grad_norm: 724.3543 loss: 411.1841 loss_cls: 145.3823 loss_bbox: 123.7062 loss_dfl: 142.0957 2024/03/17 19:01:30 - mmengine - INFO - Epoch(train) [11][550/925] lr: 1.7772e-04 eta: 11:28:10 time: 0.6394 data_time: 0.0027 memory: 11212 grad_norm: 700.6010 loss: 414.6213 loss_cls: 146.6435 loss_bbox: 124.7655 loss_dfl: 143.2123 2024/03/17 19:02:03 - mmengine - INFO - Epoch(train) [11][600/925] lr: 1.7772e-04 eta: 11:27:44 time: 0.6606 data_time: 0.0024 memory: 11172 grad_norm: 704.6062 loss: 412.8030 loss_cls: 145.6079 loss_bbox: 123.9265 loss_dfl: 143.2686 2024/03/17 19:02:35 - mmengine - INFO - Epoch(train) [11][650/925] lr: 1.7772e-04 eta: 11:27:15 time: 0.6551 data_time: 0.0026 memory: 11412 grad_norm: 665.7252 loss: 416.3347 loss_cls: 148.4830 loss_bbox: 123.9231 loss_dfl: 143.9286 2024/03/17 19:03:07 - mmengine - INFO - Epoch(train) [11][700/925] lr: 1.7772e-04 eta: 11:26:43 time: 0.6419 data_time: 0.0025 memory: 11106 grad_norm: 725.3611 loss: 407.2470 loss_cls: 141.6476 loss_bbox: 122.4578 loss_dfl: 143.1416 2024/03/17 19:03:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:03:41 - mmengine - INFO - Epoch(train) [11][750/925] lr: 1.7772e-04 eta: 11:26:17 time: 0.6640 data_time: 0.0027 memory: 11586 grad_norm: 743.0831 loss: 406.5427 loss_cls: 141.7814 loss_bbox: 121.9696 loss_dfl: 142.7917 2024/03/17 19:04:13 - mmengine - INFO - Epoch(train) [11][800/925] lr: 1.7772e-04 eta: 11:25:48 time: 0.6540 data_time: 0.0025 memory: 11612 grad_norm: 735.0838 loss: 408.4702 loss_cls: 143.3441 loss_bbox: 122.6829 loss_dfl: 142.4433 2024/03/17 19:04:47 - mmengine - INFO - Epoch(train) [11][850/925] lr: 1.7772e-04 eta: 11:25:24 time: 0.6677 data_time: 0.0027 memory: 11279 grad_norm: 689.3245 loss: 406.2664 loss_cls: 141.7182 loss_bbox: 122.4185 loss_dfl: 142.1298 2024/03/17 19:05:19 - mmengine - INFO - Epoch(train) [11][900/925] lr: 1.7772e-04 eta: 11:24:49 time: 0.6349 data_time: 0.0026 memory: 11599 grad_norm: 640.4509 loss: 409.2094 loss_cls: 142.3066 loss_bbox: 123.8867 loss_dfl: 143.0161 2024/03/17 19:05:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:06:12 - mmengine - INFO - Epoch(train) [12][ 50/925] lr: 1.7525e-04 eta: 11:24:27 time: 0.7197 data_time: 0.0769 memory: 11506 grad_norm: 718.0167 loss: 408.4017 loss_cls: 141.8855 loss_bbox: 123.5942 loss_dfl: 142.9220 2024/03/17 19:06:44 - mmengine - INFO - Epoch(train) [12][100/925] lr: 1.7525e-04 eta: 11:23:55 time: 0.6419 data_time: 0.0026 memory: 11159 grad_norm: 717.6876 loss: 403.9036 loss_cls: 139.1251 loss_bbox: 122.9625 loss_dfl: 141.8160 2024/03/17 19:07:16 - mmengine - INFO - Epoch(train) [12][150/925] lr: 1.7525e-04 eta: 11:23:24 time: 0.6502 data_time: 0.0023 memory: 11372 grad_norm: inf loss: 416.0607 loss_cls: 148.4077 loss_bbox: 123.3299 loss_dfl: 144.3231 2024/03/17 19:07:49 - mmengine - INFO - Epoch(train) [12][200/925] lr: 1.7525e-04 eta: 11:22:52 time: 0.6452 data_time: 0.0023 memory: 11612 grad_norm: 718.5060 loss: 409.4845 loss_cls: 141.2681 loss_bbox: 125.0955 loss_dfl: 143.1209 2024/03/17 19:08:22 - mmengine - INFO - Epoch(train) [12][250/925] lr: 1.7525e-04 eta: 11:22:27 time: 0.6657 data_time: 0.0028 memory: 11399 grad_norm: 721.8952 loss: 406.9235 loss_cls: 142.5649 loss_bbox: 122.8530 loss_dfl: 141.5056 2024/03/17 19:08:55 - mmengine - INFO - Epoch(train) [12][300/925] lr: 1.7525e-04 eta: 11:22:00 time: 0.6617 data_time: 0.0027 memory: 11479 grad_norm: 638.2101 loss: 410.9885 loss_cls: 145.4233 loss_bbox: 122.8870 loss_dfl: 142.6782 2024/03/17 19:09:27 - mmengine - INFO - Epoch(train) [12][350/925] lr: 1.7525e-04 eta: 11:21:27 time: 0.6408 data_time: 0.0025 memory: 11412 grad_norm: 711.6702 loss: 409.3215 loss_cls: 143.3580 loss_bbox: 123.3744 loss_dfl: 142.5890 2024/03/17 19:10:00 - mmengine - INFO - Epoch(train) [12][400/925] lr: 1.7525e-04 eta: 11:20:58 time: 0.6541 data_time: 0.0024 memory: 11612 grad_norm: 669.8587 loss: 415.6879 loss_cls: 147.4241 loss_bbox: 124.0937 loss_dfl: 144.1701 2024/03/17 19:10:32 - mmengine - INFO - Epoch(train) [12][450/925] lr: 1.7525e-04 eta: 11:20:25 time: 0.6443 data_time: 0.0023 memory: 11479 grad_norm: 738.5342 loss: 407.0400 loss_cls: 141.8175 loss_bbox: 123.6203 loss_dfl: 141.6022 2024/03/17 19:11:06 - mmengine - INFO - Epoch(train) [12][500/925] lr: 1.7525e-04 eta: 11:20:01 time: 0.6717 data_time: 0.0029 memory: 11372 grad_norm: 731.9112 loss: 408.9565 loss_cls: 143.3090 loss_bbox: 123.0112 loss_dfl: 142.6364 2024/03/17 19:11:39 - mmengine - INFO - Epoch(train) [12][550/925] lr: 1.7525e-04 eta: 11:19:36 time: 0.6668 data_time: 0.0027 memory: 11519 grad_norm: 738.1420 loss: 415.9902 loss_cls: 146.4249 loss_bbox: 126.7340 loss_dfl: 142.8313 2024/03/17 19:12:11 - mmengine - INFO - Epoch(train) [12][600/925] lr: 1.7525e-04 eta: 11:19:03 time: 0.6426 data_time: 0.0025 memory: 11466 grad_norm: 742.2407 loss: 412.7261 loss_cls: 145.7443 loss_bbox: 123.8635 loss_dfl: 143.1183 2024/03/17 19:12:44 - mmengine - INFO - Epoch(train) [12][650/925] lr: 1.7525e-04 eta: 11:18:32 time: 0.6484 data_time: 0.0023 memory: 11319 grad_norm: 651.4190 loss: 410.7495 loss_cls: 144.1662 loss_bbox: 123.6792 loss_dfl: 142.9040 2024/03/17 19:13:16 - mmengine - INFO - Epoch(train) [12][700/925] lr: 1.7525e-04 eta: 11:17:58 time: 0.6392 data_time: 0.0023 memory: 11412 grad_norm: 765.8510 loss: 401.4576 loss_cls: 138.6877 loss_bbox: 120.9249 loss_dfl: 141.8449 2024/03/17 19:13:47 - mmengine - INFO - Epoch(train) [12][750/925] lr: 1.7525e-04 eta: 11:17:24 time: 0.6387 data_time: 0.0024 memory: 11186 grad_norm: 738.3455 loss: 411.5898 loss_cls: 144.9423 loss_bbox: 123.0437 loss_dfl: 143.6039 2024/03/17 19:14:20 - mmengine - INFO - Epoch(train) [12][800/925] lr: 1.7525e-04 eta: 11:16:53 time: 0.6460 data_time: 0.0022 memory: 11666 grad_norm: 725.2935 loss: 410.8956 loss_cls: 144.0641 loss_bbox: 123.9359 loss_dfl: 142.8956 2024/03/17 19:14:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:14:52 - mmengine - INFO - Epoch(train) [12][850/925] lr: 1.7525e-04 eta: 11:16:18 time: 0.6365 data_time: 0.0024 memory: 11292 grad_norm: 684.1236 loss: 405.8200 loss_cls: 139.7165 loss_bbox: 123.0209 loss_dfl: 143.0827 2024/03/17 19:15:24 - mmengine - INFO - Epoch(train) [12][900/925] lr: 1.7525e-04 eta: 11:15:44 time: 0.6384 data_time: 0.0023 memory: 11492 grad_norm: 702.0130 loss: 416.1397 loss_cls: 142.4371 loss_bbox: 128.3316 loss_dfl: 145.3710 2024/03/17 19:15:39 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:16:16 - mmengine - INFO - Epoch(train) [13][ 50/925] lr: 1.7278e-04 eta: 11:15:14 time: 0.7169 data_time: 0.0704 memory: 11346 grad_norm: 739.6306 loss: 414.5829 loss_cls: 145.8217 loss_bbox: 124.1773 loss_dfl: 144.5838 2024/03/17 19:16:48 - mmengine - INFO - Epoch(train) [13][100/925] lr: 1.7278e-04 eta: 11:14:43 time: 0.6501 data_time: 0.0022 memory: 11746 grad_norm: 763.9669 loss: 407.2077 loss_cls: 140.5042 loss_bbox: 123.3856 loss_dfl: 143.3179 2024/03/17 19:17:20 - mmengine - INFO - Epoch(train) [13][150/925] lr: 1.7278e-04 eta: 11:14:08 time: 0.6348 data_time: 0.0021 memory: 11506 grad_norm: 718.9285 loss: 399.8100 loss_cls: 138.6576 loss_bbox: 119.0507 loss_dfl: 142.1017 2024/03/17 19:17:53 - mmengine - INFO - Epoch(train) [13][200/925] lr: 1.7278e-04 eta: 11:13:39 time: 0.6548 data_time: 0.0022 memory: 11426 grad_norm: 696.8495 loss: 405.9107 loss_cls: 141.4546 loss_bbox: 122.2688 loss_dfl: 142.1873 2024/03/17 19:18:26 - mmengine - INFO - Epoch(train) [13][250/925] lr: 1.7278e-04 eta: 11:13:12 time: 0.6633 data_time: 0.0023 memory: 11239 grad_norm: 732.6378 loss: 408.8513 loss_cls: 142.8138 loss_bbox: 123.8239 loss_dfl: 142.2136 2024/03/17 19:18:58 - mmengine - INFO - Epoch(train) [13][300/925] lr: 1.7278e-04 eta: 11:12:41 time: 0.6488 data_time: 0.0024 memory: 11306 grad_norm: 672.6762 loss: 407.9611 loss_cls: 140.8427 loss_bbox: 124.1681 loss_dfl: 142.9503 2024/03/17 19:19:31 - mmengine - INFO - Epoch(train) [13][350/925] lr: 1.7278e-04 eta: 11:12:10 time: 0.6517 data_time: 0.0023 memory: 11252 grad_norm: 656.3577 loss: 403.8630 loss_cls: 139.9638 loss_bbox: 121.2647 loss_dfl: 142.6345 2024/03/17 19:20:04 - mmengine - INFO - Epoch(train) [13][400/925] lr: 1.7278e-04 eta: 11:11:42 time: 0.6572 data_time: 0.0023 memory: 11626 grad_norm: 708.3941 loss: 404.5831 loss_cls: 141.1365 loss_bbox: 121.5799 loss_dfl: 141.8667 2024/03/17 19:20:36 - mmengine - INFO - Epoch(train) [13][450/925] lr: 1.7278e-04 eta: 11:11:11 time: 0.6514 data_time: 0.0024 memory: 11212 grad_norm: 753.2800 loss: 399.9800 loss_cls: 139.7344 loss_bbox: 120.0545 loss_dfl: 140.1911 2024/03/17 19:21:09 - mmengine - INFO - Epoch(train) [13][500/925] lr: 1.7278e-04 eta: 11:10:40 time: 0.6488 data_time: 0.0023 memory: 11439 grad_norm: 699.0134 loss: 404.9525 loss_cls: 139.3587 loss_bbox: 123.4007 loss_dfl: 142.1931 2024/03/17 19:22:12 - mmengine - INFO - Epoch(train) [13][550/925] lr: 1.7278e-04 eta: 11:12:52 time: 1.2581 data_time: 0.0603 memory: 11692 grad_norm: 708.5573 loss: 410.0683 loss_cls: 142.6514 loss_bbox: 124.5606 loss_dfl: 142.8563 2024/03/17 19:22:45 - mmengine - INFO - Epoch(train) [13][600/925] lr: 1.7278e-04 eta: 11:12:24 time: 0.6641 data_time: 0.0025 memory: 11372 grad_norm: 771.0269 loss: 408.1313 loss_cls: 144.2173 loss_bbox: 121.1980 loss_dfl: 142.7160 2024/03/17 19:23:17 - mmengine - INFO - Epoch(train) [13][650/925] lr: 1.7278e-04 eta: 11:11:52 time: 0.6490 data_time: 0.0023 memory: 11706 grad_norm: 690.7005 loss: 408.6238 loss_cls: 142.2802 loss_bbox: 124.0629 loss_dfl: 142.2806 2024/03/17 19:23:50 - mmengine - INFO - Epoch(train) [13][700/925] lr: 1.7278e-04 eta: 11:11:23 time: 0.6598 data_time: 0.0023 memory: 11372 grad_norm: 735.3487 loss: 404.7700 loss_cls: 139.6341 loss_bbox: 122.8542 loss_dfl: 142.2817 2024/03/17 19:24:23 - mmengine - INFO - Epoch(train) [13][750/925] lr: 1.7278e-04 eta: 11:10:52 time: 0.6535 data_time: 0.0024 memory: 11412 grad_norm: 721.5367 loss: 410.3409 loss_cls: 143.6505 loss_bbox: 124.7651 loss_dfl: 141.9253 2024/03/17 19:24:55 - mmengine - INFO - Epoch(train) [13][800/925] lr: 1.7278e-04 eta: 11:10:16 time: 0.6364 data_time: 0.0024 memory: 11159 grad_norm: 730.6285 loss: 404.7357 loss_cls: 140.0583 loss_bbox: 123.0875 loss_dfl: 141.5899 2024/03/17 19:25:28 - mmengine - INFO - Epoch(train) [13][850/925] lr: 1.7278e-04 eta: 11:09:45 time: 0.6523 data_time: 0.0024 memory: 11372 grad_norm: 777.1551 loss: 408.2537 loss_cls: 142.1450 loss_bbox: 122.7945 loss_dfl: 143.3141 2024/03/17 19:26:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:26:01 - mmengine - INFO - Epoch(train) [13][900/925] lr: 1.7278e-04 eta: 11:09:16 time: 0.6615 data_time: 0.0024 memory: 11292 grad_norm: 665.9168 loss: 408.2157 loss_cls: 141.3558 loss_bbox: 123.1873 loss_dfl: 143.6725 2024/03/17 19:26:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:26:53 - mmengine - INFO - Epoch(train) [14][ 50/925] lr: 1.7030e-04 eta: 11:08:42 time: 0.7143 data_time: 0.0675 memory: 11306 grad_norm: 724.7229 loss: 405.8773 loss_cls: 140.7801 loss_bbox: 122.4116 loss_dfl: 142.6856 2024/03/17 19:27:25 - mmengine - INFO - Epoch(train) [14][100/925] lr: 1.7030e-04 eta: 11:08:13 time: 0.6583 data_time: 0.0024 memory: 11746 grad_norm: 730.6785 loss: 411.4328 loss_cls: 144.4793 loss_bbox: 124.4964 loss_dfl: 142.4571 2024/03/17 19:27:58 - mmengine - INFO - Epoch(train) [14][150/925] lr: 1.7030e-04 eta: 11:07:40 time: 0.6479 data_time: 0.0023 memory: 11119 grad_norm: 676.7317 loss: 406.9483 loss_cls: 142.2218 loss_bbox: 121.9544 loss_dfl: 142.7720 2024/03/17 19:28:30 - mmengine - INFO - Epoch(train) [14][200/925] lr: 1.7030e-04 eta: 11:07:08 time: 0.6493 data_time: 0.0165 memory: 11039 grad_norm: 658.7500 loss: 402.5623 loss_cls: 139.3381 loss_bbox: 121.7479 loss_dfl: 141.4763 2024/03/17 19:29:03 - mmengine - INFO - Epoch(train) [14][250/925] lr: 1.7030e-04 eta: 11:06:38 time: 0.6552 data_time: 0.0022 memory: 11399 grad_norm: 710.3395 loss: 408.0995 loss_cls: 141.0545 loss_bbox: 124.1259 loss_dfl: 142.9191 2024/03/17 19:29:36 - mmengine - INFO - Epoch(train) [14][300/925] lr: 1.7030e-04 eta: 11:06:07 time: 0.6544 data_time: 0.0022 memory: 11319 grad_norm: 681.3424 loss: 402.8796 loss_cls: 139.3163 loss_bbox: 121.0775 loss_dfl: 142.4858 2024/03/17 19:30:08 - mmengine - INFO - Epoch(train) [14][350/925] lr: 1.7030e-04 eta: 11:05:32 time: 0.6400 data_time: 0.0023 memory: 11292 grad_norm: 724.6538 loss: 406.2752 loss_cls: 141.1474 loss_bbox: 123.6511 loss_dfl: 141.4767 2024/03/17 19:30:40 - mmengine - INFO - Epoch(train) [14][400/925] lr: 1.7030e-04 eta: 11:04:59 time: 0.6443 data_time: 0.0023 memory: 11532 grad_norm: 668.6653 loss: 411.3382 loss_cls: 144.2548 loss_bbox: 124.2638 loss_dfl: 142.8196 2024/03/17 19:31:13 - mmengine - INFO - Epoch(train) [14][450/925] lr: 1.7030e-04 eta: 11:04:28 time: 0.6541 data_time: 0.0023 memory: 11306 grad_norm: 681.1250 loss: 408.4671 loss_cls: 142.4228 loss_bbox: 123.8947 loss_dfl: 142.1496 2024/03/17 19:31:45 - mmengine - INFO - Epoch(train) [14][500/925] lr: 1.7030e-04 eta: 11:03:52 time: 0.6346 data_time: 0.0024 memory: 11226 grad_norm: 719.3086 loss: 399.8542 loss_cls: 138.1137 loss_bbox: 120.6737 loss_dfl: 141.0668 2024/03/17 19:32:16 - mmengine - INFO - Epoch(train) [14][550/925] lr: 1.7030e-04 eta: 11:03:16 time: 0.6329 data_time: 0.0023 memory: 11386 grad_norm: 652.7637 loss: 410.4052 loss_cls: 142.6782 loss_bbox: 124.3967 loss_dfl: 143.3303 2024/03/17 19:32:49 - mmengine - INFO - Epoch(train) [14][600/925] lr: 1.7030e-04 eta: 11:02:44 time: 0.6490 data_time: 0.0023 memory: 11212 grad_norm: 680.6191 loss: 406.9360 loss_cls: 141.4686 loss_bbox: 123.0860 loss_dfl: 142.3814 2024/03/17 19:33:20 - mmengine - INFO - Epoch(train) [14][650/925] lr: 1.7030e-04 eta: 11:02:09 time: 0.6345 data_time: 0.0024 memory: 11546 grad_norm: inf loss: 403.8401 loss_cls: 140.3506 loss_bbox: 121.7299 loss_dfl: 141.7596 2024/03/17 19:33:52 - mmengine - INFO - Epoch(train) [14][700/925] lr: 1.7030e-04 eta: 11:01:31 time: 0.6270 data_time: 0.0023 memory: 11359 grad_norm: 663.9872 loss: 402.7741 loss_cls: 139.0257 loss_bbox: 122.0681 loss_dfl: 141.6803 2024/03/17 19:34:24 - mmengine - INFO - Epoch(train) [14][750/925] lr: 1.7030e-04 eta: 11:00:57 time: 0.6391 data_time: 0.0023 memory: 11372 grad_norm: 689.6331 loss: 406.6870 loss_cls: 141.8172 loss_bbox: 123.2440 loss_dfl: 141.6258 2024/03/17 19:34:56 - mmengine - INFO - Epoch(train) [14][800/925] lr: 1.7030e-04 eta: 11:00:22 time: 0.6381 data_time: 0.0023 memory: 11972 grad_norm: 729.1391 loss: 413.8972 loss_cls: 144.8532 loss_bbox: 125.9032 loss_dfl: 143.1407 2024/03/17 19:35:30 - mmengine - INFO - Epoch(train) [14][850/925] lr: 1.7030e-04 eta: 10:59:58 time: 0.6847 data_time: 0.0309 memory: 11559 grad_norm: 623.7638 loss: 404.2871 loss_cls: 138.9576 loss_bbox: 122.6415 loss_dfl: 142.6880 2024/03/17 19:36:02 - mmengine - INFO - Epoch(train) [14][900/925] lr: 1.7030e-04 eta: 10:59:23 time: 0.6340 data_time: 0.0023 memory: 11426 grad_norm: 804.0175 loss: 405.8395 loss_cls: 140.1249 loss_bbox: 122.4219 loss_dfl: 143.2927 2024/03/17 19:36:17 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:36:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:36:53 - mmengine - INFO - Epoch(train) [15][ 50/925] lr: 1.6783e-04 eta: 10:58:45 time: 0.7114 data_time: 0.0612 memory: 11479 grad_norm: 715.4679 loss: 402.0461 loss_cls: 138.2952 loss_bbox: 121.4191 loss_dfl: 142.3318 2024/03/17 19:37:24 - mmengine - INFO - Epoch(train) [15][100/925] lr: 1.6783e-04 eta: 10:58:07 time: 0.6278 data_time: 0.0023 memory: 11719 grad_norm: 691.9646 loss: 403.3349 loss_cls: 138.5524 loss_bbox: 122.7229 loss_dfl: 142.0595 2024/03/17 19:37:56 - mmengine - INFO - Epoch(train) [15][150/925] lr: 1.6783e-04 eta: 10:57:33 time: 0.6374 data_time: 0.0023 memory: 11252 grad_norm: 670.5608 loss: 409.2748 loss_cls: 142.4305 loss_bbox: 124.0141 loss_dfl: 142.8302 2024/03/17 19:38:29 - mmengine - INFO - Epoch(train) [15][200/925] lr: 1.6783e-04 eta: 10:57:00 time: 0.6451 data_time: 0.0025 memory: 11426 grad_norm: 715.5364 loss: 407.5680 loss_cls: 141.4195 loss_bbox: 124.0456 loss_dfl: 142.1030 2024/03/17 19:39:00 - mmengine - INFO - Epoch(train) [15][250/925] lr: 1.6783e-04 eta: 10:56:22 time: 0.6252 data_time: 0.0025 memory: 11359 grad_norm: 630.4125 loss: 404.8140 loss_cls: 139.5065 loss_bbox: 122.6934 loss_dfl: 142.6141 2024/03/17 19:39:32 - mmengine - INFO - Epoch(train) [15][300/925] lr: 1.6783e-04 eta: 10:55:47 time: 0.6360 data_time: 0.0021 memory: 11319 grad_norm: 687.0276 loss: 407.4749 loss_cls: 140.8771 loss_bbox: 123.6013 loss_dfl: 142.9965 2024/03/17 19:40:04 - mmengine - INFO - Epoch(train) [15][350/925] lr: 1.6783e-04 eta: 10:55:13 time: 0.6403 data_time: 0.0024 memory: 11479 grad_norm: 649.5895 loss: 404.6584 loss_cls: 142.3030 loss_bbox: 121.0483 loss_dfl: 141.3071 2024/03/17 19:40:36 - mmengine - INFO - Epoch(train) [15][400/925] lr: 1.6783e-04 eta: 10:54:38 time: 0.6359 data_time: 0.0026 memory: 11279 grad_norm: 725.3246 loss: 411.2997 loss_cls: 143.6856 loss_bbox: 124.0504 loss_dfl: 143.5637 2024/03/17 19:41:07 - mmengine - INFO - Epoch(train) [15][450/925] lr: 1.6783e-04 eta: 10:54:03 time: 0.6366 data_time: 0.0023 memory: 11452 grad_norm: 709.9119 loss: 394.1650 loss_cls: 134.2919 loss_bbox: 119.7198 loss_dfl: 140.1533 2024/03/17 19:41:40 - mmengine - INFO - Epoch(train) [15][500/925] lr: 1.6783e-04 eta: 10:53:29 time: 0.6415 data_time: 0.0025 memory: 11506 grad_norm: 689.7864 loss: 406.1494 loss_cls: 138.7219 loss_bbox: 125.0604 loss_dfl: 142.3671 2024/03/17 19:42:12 - mmengine - INFO - Epoch(train) [15][550/925] lr: 1.6783e-04 eta: 10:52:56 time: 0.6439 data_time: 0.0024 memory: 11506 grad_norm: 761.2789 loss: 401.7500 loss_cls: 138.3798 loss_bbox: 121.1219 loss_dfl: 142.2484 2024/03/17 19:42:43 - mmengine - INFO - Epoch(train) [15][600/925] lr: 1.6783e-04 eta: 10:52:20 time: 0.6294 data_time: 0.0024 memory: 11346 grad_norm: 726.8423 loss: 406.7925 loss_cls: 141.2805 loss_bbox: 123.4970 loss_dfl: 142.0150 2024/03/17 19:43:15 - mmengine - INFO - Epoch(train) [15][650/925] lr: 1.6783e-04 eta: 10:51:46 time: 0.6431 data_time: 0.0022 memory: 11372 grad_norm: 785.8752 loss: 398.1196 loss_cls: 136.1737 loss_bbox: 120.7312 loss_dfl: 141.2147 2024/03/17 19:43:48 - mmengine - INFO - Epoch(train) [15][700/925] lr: 1.6783e-04 eta: 10:51:13 time: 0.6457 data_time: 0.0024 memory: 11372 grad_norm: 640.1725 loss: 397.4448 loss_cls: 134.8657 loss_bbox: 122.5257 loss_dfl: 140.0535 2024/03/17 19:44:19 - mmengine - INFO - Epoch(train) [15][750/925] lr: 1.6783e-04 eta: 10:50:38 time: 0.6318 data_time: 0.0026 memory: 11372 grad_norm: 704.5155 loss: 404.4918 loss_cls: 139.2906 loss_bbox: 123.2791 loss_dfl: 141.9221 2024/03/17 19:44:51 - mmengine - INFO - Epoch(train) [15][800/925] lr: 1.6783e-04 eta: 10:50:03 time: 0.6373 data_time: 0.0023 memory: 11612 grad_norm: 688.2608 loss: 412.5074 loss_cls: 143.2517 loss_bbox: 126.0237 loss_dfl: 143.2320 2024/03/17 19:45:23 - mmengine - INFO - Epoch(train) [15][850/925] lr: 1.6783e-04 eta: 10:49:29 time: 0.6396 data_time: 0.0023 memory: 11279 grad_norm: 692.0373 loss: 408.2416 loss_cls: 141.6910 loss_bbox: 122.6802 loss_dfl: 143.8704 2024/03/17 19:45:54 - mmengine - INFO - Epoch(train) [15][900/925] lr: 1.6783e-04 eta: 10:48:52 time: 0.6259 data_time: 0.0024 memory: 11706 grad_norm: 730.7968 loss: 393.6704 loss_cls: 134.6596 loss_bbox: 119.0845 loss_dfl: 139.9263 2024/03/17 19:46:10 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:46:10 - mmengine - INFO - Saving checkpoint at 15 epochs 2024/03/17 19:46:19 - mmengine - INFO - Epoch(val) [15][ 50/625] eta: 0:00:15 time: 0.0276 data_time: 0.0015 memory: 11412 2024/03/17 19:46:20 - mmengine - INFO - Epoch(val) [15][100/625] eta: 0:00:14 time: 0.0278 data_time: 0.0013 memory: 1709 2024/03/17 19:46:21 - mmengine - INFO - Epoch(val) [15][150/625] eta: 0:00:12 time: 0.0246 data_time: 0.0014 memory: 1709 2024/03/17 19:46:23 - mmengine - INFO - Epoch(val) [15][200/625] eta: 0:00:11 time: 0.0238 data_time: 0.0016 memory: 1709 2024/03/17 19:46:24 - mmengine - INFO - Epoch(val) [15][250/625] eta: 0:00:09 time: 0.0235 data_time: 0.0019 memory: 1709 2024/03/17 19:46:25 - mmengine - INFO - Epoch(val) [15][300/625] eta: 0:00:08 time: 0.0231 data_time: 0.0012 memory: 1709 2024/03/17 19:46:26 - mmengine - INFO - Epoch(val) [15][350/625] eta: 0:00:06 time: 0.0215 data_time: 0.0006 memory: 1709 2024/03/17 19:46:27 - mmengine - INFO - Epoch(val) [15][400/625] eta: 0:00:05 time: 0.0253 data_time: 0.0020 memory: 1709 2024/03/17 19:46:28 - mmengine - INFO - Epoch(val) [15][450/625] eta: 0:00:04 time: 0.0250 data_time: 0.0006 memory: 1709 2024/03/17 19:46:30 - mmengine - INFO - Epoch(val) [15][500/625] eta: 0:00:03 time: 0.0250 data_time: 0.0004 memory: 1709 2024/03/17 19:46:31 - mmengine - INFO - Epoch(val) [15][550/625] eta: 0:00:01 time: 0.0251 data_time: 0.0002 memory: 1709 2024/03/17 19:46:32 - mmengine - INFO - Epoch(val) [15][600/625] eta: 0:00:00 time: 0.0252 data_time: 0.0007 memory: 1709 2024/03/17 19:46:45 - mmengine - INFO - Evaluating bbox... 2024/03/17 19:48:01 - mmengine - INFO - bbox_mAP_copypaste: 0.506 0.674 0.552 0.329 0.558 0.657 2024/03/17 19:48:03 - mmengine - INFO - Epoch(val) [15][625/625] coco/bbox_mAP: 0.5060 coco/bbox_mAP_50: 0.6740 coco/bbox_mAP_75: 0.5520 coco/bbox_mAP_s: 0.3290 coco/bbox_mAP_m: 0.5580 coco/bbox_mAP_l: 0.6570 data_time: 0.0008 time: 0.0254 2024/03/17 19:48:40 - mmengine - INFO - Epoch(train) [16][ 50/925] lr: 1.6535e-04 eta: 10:48:17 time: 0.7315 data_time: 0.0609 memory: 11412 grad_norm: 628.0169 loss: 404.8084 loss_cls: 140.5034 loss_bbox: 122.3611 loss_dfl: 141.9438 2024/03/17 19:49:12 - mmengine - INFO - Epoch(train) [16][100/925] lr: 1.6535e-04 eta: 10:47:44 time: 0.6412 data_time: 0.0024 memory: 11226 grad_norm: 652.9888 loss: 407.9893 loss_cls: 144.8031 loss_bbox: 121.5428 loss_dfl: 141.6434 2024/03/17 19:49:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:49:45 - mmengine - INFO - Epoch(train) [16][150/925] lr: 1.6535e-04 eta: 10:47:13 time: 0.6565 data_time: 0.0024 memory: 11332 grad_norm: 714.5694 loss: 404.3679 loss_cls: 140.6681 loss_bbox: 122.0793 loss_dfl: 141.6205 2024/03/17 19:50:18 - mmengine - INFO - Epoch(train) [16][200/925] lr: 1.6535e-04 eta: 10:46:44 time: 0.6610 data_time: 0.0023 memory: 11186 grad_norm: 737.7747 loss: 400.4826 loss_cls: 138.5699 loss_bbox: 120.1665 loss_dfl: 141.7463 2024/03/17 19:50:51 - mmengine - INFO - Epoch(train) [16][250/925] lr: 1.6535e-04 eta: 10:46:13 time: 0.6580 data_time: 0.0024 memory: 11412 grad_norm: 665.7679 loss: 398.0785 loss_cls: 136.6479 loss_bbox: 120.8707 loss_dfl: 140.5600 2024/03/17 19:51:23 - mmengine - INFO - Epoch(train) [16][300/925] lr: 1.6535e-04 eta: 10:45:39 time: 0.6393 data_time: 0.0023 memory: 11426 grad_norm: 672.9826 loss: 403.6643 loss_cls: 138.3067 loss_bbox: 122.8155 loss_dfl: 142.5422 2024/03/17 19:51:56 - mmengine - INFO - Epoch(train) [16][350/925] lr: 1.6535e-04 eta: 10:45:11 time: 0.6665 data_time: 0.0024 memory: 11412 grad_norm: 755.4679 loss: 406.7837 loss_cls: 140.3531 loss_bbox: 124.3658 loss_dfl: 142.0648 2024/03/17 19:52:30 - mmengine - INFO - Epoch(train) [16][400/925] lr: 1.6535e-04 eta: 10:44:42 time: 0.6637 data_time: 0.0024 memory: 11186 grad_norm: 683.1867 loss: 402.3627 loss_cls: 140.0503 loss_bbox: 120.8842 loss_dfl: 141.4281 2024/03/17 19:53:01 - mmengine - INFO - Epoch(train) [16][450/925] lr: 1.6535e-04 eta: 10:44:07 time: 0.6363 data_time: 0.0026 memory: 11106 grad_norm: 716.8419 loss: 401.0475 loss_cls: 138.6865 loss_bbox: 121.0099 loss_dfl: 141.3512 2024/03/17 19:53:35 - mmengine - INFO - Epoch(train) [16][500/925] lr: 1.6535e-04 eta: 10:43:38 time: 0.6631 data_time: 0.0024 memory: 11279 grad_norm: 707.2254 loss: 398.8047 loss_cls: 136.0209 loss_bbox: 121.0540 loss_dfl: 141.7298 2024/03/17 19:54:08 - mmengine - INFO - Epoch(train) [16][550/925] lr: 1.6535e-04 eta: 10:43:09 time: 0.6662 data_time: 0.0024 memory: 11399 grad_norm: 712.2616 loss: 401.6546 loss_cls: 138.5594 loss_bbox: 120.9617 loss_dfl: 142.1335 2024/03/17 19:54:40 - mmengine - INFO - Epoch(train) [16][600/925] lr: 1.6535e-04 eta: 10:42:38 time: 0.6514 data_time: 0.0025 memory: 11306 grad_norm: 693.6429 loss: 400.9076 loss_cls: 137.9635 loss_bbox: 120.8743 loss_dfl: 142.0698 2024/03/17 19:55:13 - mmengine - INFO - Epoch(train) [16][650/925] lr: 1.6535e-04 eta: 10:42:05 time: 0.6446 data_time: 0.0023 memory: 11119 grad_norm: 667.3634 loss: 400.1688 loss_cls: 139.3600 loss_bbox: 119.2359 loss_dfl: 141.5730 2024/03/17 19:55:46 - mmengine - INFO - Epoch(train) [16][700/925] lr: 1.6535e-04 eta: 10:41:36 time: 0.6679 data_time: 0.0024 memory: 11306 grad_norm: 663.6471 loss: 403.7444 loss_cls: 139.0932 loss_bbox: 121.6968 loss_dfl: 142.9544 2024/03/17 19:56:19 - mmengine - INFO - Epoch(train) [16][750/925] lr: 1.6535e-04 eta: 10:41:04 time: 0.6480 data_time: 0.0024 memory: 11426 grad_norm: 762.6080 loss: 404.7644 loss_cls: 138.7501 loss_bbox: 123.6797 loss_dfl: 142.3346 2024/03/17 19:56:51 - mmengine - INFO - Epoch(train) [16][800/925] lr: 1.6535e-04 eta: 10:40:31 time: 0.6449 data_time: 0.0023 memory: 11599 grad_norm: 725.1916 loss: 397.8110 loss_cls: 135.1312 loss_bbox: 121.6863 loss_dfl: 140.9934 2024/03/17 19:57:24 - mmengine - INFO - Epoch(train) [16][850/925] lr: 1.6535e-04 eta: 10:40:02 time: 0.6623 data_time: 0.0023 memory: 11279 grad_norm: 704.7466 loss: 397.8454 loss_cls: 135.2184 loss_bbox: 121.1094 loss_dfl: 141.5176 2024/03/17 19:57:57 - mmengine - INFO - Epoch(train) [16][900/925] lr: 1.6535e-04 eta: 10:39:31 time: 0.6544 data_time: 0.0024 memory: 11226 grad_norm: 657.7598 loss: 406.3274 loss_cls: 140.7222 loss_bbox: 123.3190 loss_dfl: 142.2861 2024/03/17 19:58:12 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 19:58:48 - mmengine - INFO - Epoch(train) [17][ 50/925] lr: 1.6287e-04 eta: 10:38:50 time: 0.7057 data_time: 0.0639 memory: 11426 grad_norm: 654.6159 loss: 403.8373 loss_cls: 139.7314 loss_bbox: 122.3270 loss_dfl: 141.7789 2024/03/17 19:59:21 - mmengine - INFO - Epoch(train) [17][100/925] lr: 1.6287e-04 eta: 10:38:20 time: 0.6566 data_time: 0.0025 memory: 11479 grad_norm: 690.9632 loss: 401.6364 loss_cls: 135.5802 loss_bbox: 123.8868 loss_dfl: 142.1694 2024/03/17 19:59:53 - mmengine - INFO - Epoch(train) [17][150/925] lr: 1.6287e-04 eta: 10:37:49 time: 0.6552 data_time: 0.0026 memory: 11412 grad_norm: 690.0019 loss: 401.5366 loss_cls: 138.0268 loss_bbox: 121.3936 loss_dfl: 142.1163 2024/03/17 20:00:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:00:26 - mmengine - INFO - Epoch(train) [17][200/925] lr: 1.6287e-04 eta: 10:37:17 time: 0.6502 data_time: 0.0025 memory: 11252 grad_norm: 637.5914 loss: 404.4178 loss_cls: 139.8305 loss_bbox: 123.1639 loss_dfl: 141.4233 2024/03/17 20:00:59 - mmengine - INFO - Epoch(train) [17][250/925] lr: 1.6287e-04 eta: 10:36:48 time: 0.6657 data_time: 0.0024 memory: 11412 grad_norm: 690.1645 loss: 402.8426 loss_cls: 139.6103 loss_bbox: 120.9097 loss_dfl: 142.3226 2024/03/17 20:01:32 - mmengine - INFO - Epoch(train) [17][300/925] lr: 1.6287e-04 eta: 10:36:17 time: 0.6551 data_time: 0.0025 memory: 11132 grad_norm: 731.0560 loss: 399.1807 loss_cls: 137.5070 loss_bbox: 120.4471 loss_dfl: 141.2266 2024/03/17 20:02:05 - mmengine - INFO - Epoch(train) [17][350/925] lr: 1.6287e-04 eta: 10:35:47 time: 0.6614 data_time: 0.0025 memory: 11439 grad_norm: 721.0597 loss: 403.6024 loss_cls: 139.6407 loss_bbox: 122.0493 loss_dfl: 141.9124 2024/03/17 20:02:38 - mmengine - INFO - Epoch(train) [17][400/925] lr: 1.6287e-04 eta: 10:35:16 time: 0.6567 data_time: 0.0022 memory: 11426 grad_norm: 690.9823 loss: 404.7506 loss_cls: 140.0386 loss_bbox: 122.6514 loss_dfl: 142.0606 2024/03/17 20:03:11 - mmengine - INFO - Epoch(train) [17][450/925] lr: 1.6287e-04 eta: 10:34:46 time: 0.6573 data_time: 0.0024 memory: 11426 grad_norm: inf loss: 400.1205 loss_cls: 138.7272 loss_bbox: 119.6381 loss_dfl: 141.7553 2024/03/17 20:03:43 - mmengine - INFO - Epoch(train) [17][500/925] lr: 1.6287e-04 eta: 10:34:13 time: 0.6503 data_time: 0.0023 memory: 11572 grad_norm: 721.3571 loss: 400.8944 loss_cls: 137.2456 loss_bbox: 121.5092 loss_dfl: 142.1396 2024/03/17 20:04:16 - mmengine - INFO - Epoch(train) [17][550/925] lr: 1.6287e-04 eta: 10:33:43 time: 0.6561 data_time: 0.0022 memory: 11466 grad_norm: 663.3543 loss: 403.1123 loss_cls: 141.0220 loss_bbox: 120.8232 loss_dfl: 141.2671 2024/03/17 20:04:50 - mmengine - INFO - Epoch(train) [17][600/925] lr: 1.6287e-04 eta: 10:33:14 time: 0.6660 data_time: 0.0024 memory: 11866 grad_norm: 679.1103 loss: 401.6508 loss_cls: 139.0893 loss_bbox: 122.1113 loss_dfl: 140.4502 2024/03/17 20:05:22 - mmengine - INFO - Epoch(train) [17][650/925] lr: 1.6287e-04 eta: 10:32:43 time: 0.6557 data_time: 0.0025 memory: 11212 grad_norm: 693.3694 loss: 402.3289 loss_cls: 138.3241 loss_bbox: 122.0930 loss_dfl: 141.9119 2024/03/17 20:05:55 - mmengine - INFO - Epoch(train) [17][700/925] lr: 1.6287e-04 eta: 10:32:10 time: 0.6488 data_time: 0.0023 memory: 11359 grad_norm: 693.7171 loss: 406.1358 loss_cls: 142.5125 loss_bbox: 121.4367 loss_dfl: 142.1866 2024/03/17 20:06:29 - mmengine - INFO - Epoch(train) [17][750/925] lr: 1.6287e-04 eta: 10:31:43 time: 0.6745 data_time: 0.0026 memory: 11506 grad_norm: 659.6132 loss: 399.5082 loss_cls: 137.0392 loss_bbox: 121.4310 loss_dfl: 141.0379 2024/03/17 20:07:01 - mmengine - INFO - Epoch(train) [17][800/925] lr: 1.6287e-04 eta: 10:31:12 time: 0.6571 data_time: 0.0023 memory: 11332 grad_norm: 734.3140 loss: 395.2133 loss_cls: 134.8542 loss_bbox: 119.7478 loss_dfl: 140.6112 2024/03/17 20:07:34 - mmengine - INFO - Epoch(train) [17][850/925] lr: 1.6287e-04 eta: 10:30:40 time: 0.6492 data_time: 0.0026 memory: 12052 grad_norm: 670.8811 loss: 398.9677 loss_cls: 135.9623 loss_bbox: 120.8627 loss_dfl: 142.1427 2024/03/17 20:08:07 - mmengine - INFO - Epoch(train) [17][900/925] lr: 1.6287e-04 eta: 10:30:10 time: 0.6641 data_time: 0.0025 memory: 11266 grad_norm: 710.2885 loss: 400.4544 loss_cls: 137.4287 loss_bbox: 121.9549 loss_dfl: 141.0709 2024/03/17 20:08:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:08:59 - mmengine - INFO - Epoch(train) [18][ 50/925] lr: 1.6040e-04 eta: 10:29:32 time: 0.7155 data_time: 0.0698 memory: 11386 grad_norm: 675.7844 loss: 399.4729 loss_cls: 137.2134 loss_bbox: 121.4527 loss_dfl: 140.8069 2024/03/17 20:09:32 - mmengine - INFO - Epoch(train) [18][100/925] lr: 1.6040e-04 eta: 10:29:02 time: 0.6657 data_time: 0.0024 memory: 11426 grad_norm: 695.3758 loss: 397.7193 loss_cls: 136.9847 loss_bbox: 119.6758 loss_dfl: 141.0588 2024/03/17 20:10:06 - mmengine - INFO - Epoch(train) [18][150/925] lr: 1.6040e-04 eta: 10:28:34 time: 0.6740 data_time: 0.0024 memory: 11266 grad_norm: 685.4827 loss: 396.1921 loss_cls: 135.8174 loss_bbox: 119.6943 loss_dfl: 140.6804 2024/03/17 20:10:39 - mmengine - INFO - Epoch(train) [18][200/925] lr: 1.6040e-04 eta: 10:28:02 time: 0.6482 data_time: 0.0024 memory: 11319 grad_norm: 703.8689 loss: 402.5996 loss_cls: 139.9844 loss_bbox: 121.5907 loss_dfl: 141.0245 2024/03/17 20:11:12 - mmengine - INFO - Epoch(train) [18][250/925] lr: 1.6040e-04 eta: 10:27:32 time: 0.6621 data_time: 0.0024 memory: 11412 grad_norm: 655.9208 loss: 406.0928 loss_cls: 141.1146 loss_bbox: 121.6054 loss_dfl: 143.3727 2024/03/17 20:11:28 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:11:45 - mmengine - INFO - Epoch(train) [18][300/925] lr: 1.6040e-04 eta: 10:27:03 time: 0.6683 data_time: 0.0024 memory: 11212 grad_norm: 745.6167 loss: 405.1709 loss_cls: 139.3509 loss_bbox: 123.0187 loss_dfl: 142.8012 2024/03/17 20:12:18 - mmengine - INFO - Epoch(train) [18][350/925] lr: 1.6040e-04 eta: 10:26:32 time: 0.6591 data_time: 0.0023 memory: 11186 grad_norm: 666.1911 loss: 404.7128 loss_cls: 139.8461 loss_bbox: 123.1787 loss_dfl: 141.6880 2024/03/17 20:12:51 - mmengine - INFO - Epoch(train) [18][400/925] lr: 1.6040e-04 eta: 10:26:00 time: 0.6510 data_time: 0.0024 memory: 11666 grad_norm: 703.7919 loss: 398.6276 loss_cls: 136.0809 loss_bbox: 122.7892 loss_dfl: 139.7575 2024/03/17 20:13:24 - mmengine - INFO - Epoch(train) [18][450/925] lr: 1.6040e-04 eta: 10:25:32 time: 0.6713 data_time: 0.0025 memory: 11892 grad_norm: 701.1788 loss: 401.8454 loss_cls: 138.3698 loss_bbox: 121.6071 loss_dfl: 141.8685 2024/03/17 20:13:58 - mmengine - INFO - Epoch(train) [18][500/925] lr: 1.6040e-04 eta: 10:25:03 time: 0.6686 data_time: 0.0025 memory: 11292 grad_norm: 696.0412 loss: 393.9392 loss_cls: 134.4346 loss_bbox: 119.4312 loss_dfl: 140.0735 2024/03/17 20:14:30 - mmengine - INFO - Epoch(train) [18][550/925] lr: 1.6040e-04 eta: 10:24:30 time: 0.6487 data_time: 0.0025 memory: 11639 grad_norm: 738.7568 loss: 398.1654 loss_cls: 137.4713 loss_bbox: 120.1999 loss_dfl: 140.4942 2024/03/17 20:15:03 - mmengine - INFO - Epoch(train) [18][600/925] lr: 1.6040e-04 eta: 10:24:00 time: 0.6613 data_time: 0.0025 memory: 11292 grad_norm: 786.8255 loss: 402.9058 loss_cls: 139.2359 loss_bbox: 122.4756 loss_dfl: 141.1942 2024/03/17 20:15:37 - mmengine - INFO - Epoch(train) [18][650/925] lr: 1.6040e-04 eta: 10:23:32 time: 0.6772 data_time: 0.0025 memory: 11546 grad_norm: 684.2135 loss: 403.5986 loss_cls: 139.7707 loss_bbox: 121.8259 loss_dfl: 142.0021 2024/03/17 20:16:10 - mmengine - INFO - Epoch(train) [18][700/925] lr: 1.6040e-04 eta: 10:23:02 time: 0.6594 data_time: 0.0024 memory: 11679 grad_norm: 726.2294 loss: 402.9493 loss_cls: 139.0538 loss_bbox: 120.9734 loss_dfl: 142.9221 2024/03/17 20:16:44 - mmengine - INFO - Epoch(train) [18][750/925] lr: 1.6040e-04 eta: 10:22:33 time: 0.6694 data_time: 0.0026 memory: 11306 grad_norm: 681.5114 loss: 406.2717 loss_cls: 140.3059 loss_bbox: 123.0614 loss_dfl: 142.9043 2024/03/17 20:17:17 - mmengine - INFO - Epoch(train) [18][800/925] lr: 1.6040e-04 eta: 10:22:04 time: 0.6720 data_time: 0.0025 memory: 11359 grad_norm: 724.5056 loss: 402.1396 loss_cls: 138.6208 loss_bbox: 121.2788 loss_dfl: 142.2400 2024/03/17 20:17:50 - mmengine - INFO - Epoch(train) [18][850/925] lr: 1.6040e-04 eta: 10:21:31 time: 0.6456 data_time: 0.0024 memory: 11359 grad_norm: 655.8056 loss: 401.3387 loss_cls: 137.4587 loss_bbox: 121.2204 loss_dfl: 142.6597 2024/03/17 20:18:23 - mmengine - INFO - Epoch(train) [18][900/925] lr: 1.6040e-04 eta: 10:21:00 time: 0.6599 data_time: 0.0025 memory: 11386 grad_norm: 649.1714 loss: 398.5171 loss_cls: 137.5609 loss_bbox: 120.0971 loss_dfl: 140.8592 2024/03/17 20:18:39 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:19:15 - mmengine - INFO - Epoch(train) [19][ 50/925] lr: 1.5793e-04 eta: 10:20:22 time: 0.7131 data_time: 0.0641 memory: 11426 grad_norm: 697.9828 loss: 398.1646 loss_cls: 136.0797 loss_bbox: 121.4218 loss_dfl: 140.6630 2024/03/17 20:19:47 - mmengine - INFO - Epoch(train) [19][100/925] lr: 1.5793e-04 eta: 10:19:48 time: 0.6372 data_time: 0.0023 memory: 11399 grad_norm: 686.3657 loss: 410.6165 loss_cls: 143.7766 loss_bbox: 123.9336 loss_dfl: 142.9063 2024/03/17 20:20:18 - mmengine - INFO - Epoch(train) [19][150/925] lr: 1.5793e-04 eta: 10:19:12 time: 0.6336 data_time: 0.0024 memory: 11519 grad_norm: 714.2844 loss: 403.1922 loss_cls: 138.5651 loss_bbox: 122.1483 loss_dfl: 142.4789 2024/03/17 20:20:51 - mmengine - INFO - Epoch(train) [19][200/925] lr: 1.5793e-04 eta: 10:18:39 time: 0.6412 data_time: 0.0024 memory: 11332 grad_norm: 671.1503 loss: 403.7300 loss_cls: 140.3891 loss_bbox: 122.2845 loss_dfl: 141.0563 2024/03/17 20:21:23 - mmengine - INFO - Epoch(train) [19][250/925] lr: 1.5793e-04 eta: 10:18:05 time: 0.6440 data_time: 0.0024 memory: 11639 grad_norm: 662.0524 loss: 402.4413 loss_cls: 139.2267 loss_bbox: 121.3348 loss_dfl: 141.8797 2024/03/17 20:21:56 - mmengine - INFO - Epoch(train) [19][300/925] lr: 1.5793e-04 eta: 10:17:34 time: 0.6565 data_time: 0.0028 memory: 11306 grad_norm: 728.9762 loss: 397.5880 loss_cls: 136.1928 loss_bbox: 120.7132 loss_dfl: 140.6819 2024/03/17 20:22:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:22:29 - mmengine - INFO - Epoch(train) [19][350/925] lr: 1.5793e-04 eta: 10:17:04 time: 0.6643 data_time: 0.0028 memory: 11319 grad_norm: 697.7720 loss: 399.2460 loss_cls: 137.9443 loss_bbox: 120.0920 loss_dfl: 141.2097 2024/03/17 20:23:01 - mmengine - INFO - Epoch(train) [19][400/925] lr: 1.5793e-04 eta: 10:16:31 time: 0.6456 data_time: 0.0026 memory: 11506 grad_norm: 653.9142 loss: 401.3923 loss_cls: 137.8894 loss_bbox: 121.4864 loss_dfl: 142.0165 2024/03/17 20:23:34 - mmengine - INFO - Epoch(train) [19][450/925] lr: 1.5793e-04 eta: 10:15:59 time: 0.6567 data_time: 0.0026 memory: 11426 grad_norm: 701.3545 loss: 399.7296 loss_cls: 136.1532 loss_bbox: 122.2422 loss_dfl: 141.3342 2024/03/17 20:24:07 - mmengine - INFO - Epoch(train) [19][500/925] lr: 1.5793e-04 eta: 10:15:27 time: 0.6511 data_time: 0.0026 memory: 11346 grad_norm: 668.7718 loss: 398.7437 loss_cls: 137.8414 loss_bbox: 119.8759 loss_dfl: 141.0264 2024/03/17 20:24:40 - mmengine - INFO - Epoch(train) [19][550/925] lr: 1.5793e-04 eta: 10:14:57 time: 0.6622 data_time: 0.0026 memory: 11692 grad_norm: 681.9404 loss: 403.7488 loss_cls: 139.1328 loss_bbox: 123.2094 loss_dfl: 141.4066 2024/03/17 20:25:12 - mmengine - INFO - Epoch(train) [19][600/925] lr: 1.5793e-04 eta: 10:14:24 time: 0.6443 data_time: 0.0027 memory: 11452 grad_norm: 737.8666 loss: 401.4925 loss_cls: 138.8287 loss_bbox: 121.4102 loss_dfl: 141.2537 2024/03/17 20:25:45 - mmengine - INFO - Epoch(train) [19][650/925] lr: 1.5793e-04 eta: 10:13:53 time: 0.6587 data_time: 0.0025 memory: 11439 grad_norm: 661.5136 loss: 398.2161 loss_cls: 136.6139 loss_bbox: 120.6588 loss_dfl: 140.9434 2024/03/17 20:26:18 - mmengine - INFO - Epoch(train) [19][700/925] lr: 1.5793e-04 eta: 10:13:23 time: 0.6673 data_time: 0.0026 memory: 11452 grad_norm: inf loss: 400.3896 loss_cls: 136.7460 loss_bbox: 121.7745 loss_dfl: 141.8691 2024/03/17 20:26:51 - mmengine - INFO - Epoch(train) [19][750/925] lr: 1.5793e-04 eta: 10:12:53 time: 0.6630 data_time: 0.0028 memory: 11346 grad_norm: 677.6981 loss: 400.4201 loss_cls: 138.2087 loss_bbox: 121.6057 loss_dfl: 140.6057 2024/03/17 20:27:24 - mmengine - INFO - Epoch(train) [19][800/925] lr: 1.5793e-04 eta: 10:12:21 time: 0.6526 data_time: 0.0027 memory: 11359 grad_norm: 678.7568 loss: 398.2883 loss_cls: 137.3716 loss_bbox: 120.6282 loss_dfl: 140.2884 2024/03/17 20:27:57 - mmengine - INFO - Epoch(train) [19][850/925] lr: 1.5793e-04 eta: 10:11:48 time: 0.6519 data_time: 0.0028 memory: 11372 grad_norm: 676.1121 loss: 394.5163 loss_cls: 134.4395 loss_bbox: 119.5117 loss_dfl: 140.5651 2024/03/17 20:28:30 - mmengine - INFO - Epoch(train) [19][900/925] lr: 1.5793e-04 eta: 10:11:17 time: 0.6567 data_time: 0.0027 memory: 11199 grad_norm: 660.2195 loss: 396.6075 loss_cls: 134.8533 loss_bbox: 120.8778 loss_dfl: 140.8763 2024/03/17 20:28:45 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:29:22 - mmengine - INFO - Epoch(train) [20][ 50/925] lr: 1.5545e-04 eta: 10:10:39 time: 0.7276 data_time: 0.0716 memory: 11732 grad_norm: 696.6123 loss: 400.4367 loss_cls: 137.4663 loss_bbox: 121.6485 loss_dfl: 141.3220 2024/03/17 20:29:56 - mmengine - INFO - Epoch(train) [20][100/925] lr: 1.5545e-04 eta: 10:10:09 time: 0.6684 data_time: 0.0026 memory: 11439 grad_norm: 713.9659 loss: 407.0420 loss_cls: 141.2279 loss_bbox: 122.8917 loss_dfl: 142.9224 2024/03/17 20:30:28 - mmengine - INFO - Epoch(train) [20][150/925] lr: 1.5545e-04 eta: 10:09:37 time: 0.6532 data_time: 0.0025 memory: 11812 grad_norm: 675.2738 loss: 399.0710 loss_cls: 137.3854 loss_bbox: 119.9437 loss_dfl: 141.7418 2024/03/17 20:31:01 - mmengine - INFO - Epoch(train) [20][200/925] lr: 1.5545e-04 eta: 10:09:06 time: 0.6576 data_time: 0.0025 memory: 11399 grad_norm: 714.5202 loss: 402.2008 loss_cls: 138.6244 loss_bbox: 121.7369 loss_dfl: 141.8395 2024/03/17 20:31:34 - mmengine - INFO - Epoch(train) [20][250/925] lr: 1.5545e-04 eta: 10:08:33 time: 0.6500 data_time: 0.0024 memory: 11239 grad_norm: 655.0880 loss: 391.7827 loss_cls: 132.2923 loss_bbox: 119.8081 loss_dfl: 139.6823 2024/03/17 20:32:06 - mmengine - INFO - Epoch(train) [20][300/925] lr: 1.5545e-04 eta: 10:08:01 time: 0.6480 data_time: 0.0025 memory: 11292 grad_norm: 710.7200 loss: 393.0544 loss_cls: 133.5909 loss_bbox: 119.0132 loss_dfl: 140.4503 2024/03/17 20:32:39 - mmengine - INFO - Epoch(train) [20][350/925] lr: 1.5545e-04 eta: 10:07:30 time: 0.6608 data_time: 0.0028 memory: 11692 grad_norm: 733.8043 loss: 401.5577 loss_cls: 137.9825 loss_bbox: 121.7607 loss_dfl: 141.8145 2024/03/17 20:33:12 - mmengine - INFO - Epoch(train) [20][400/925] lr: 1.5545e-04 eta: 10:07:00 time: 0.6647 data_time: 0.0030 memory: 11159 grad_norm: 683.7470 loss: 394.9425 loss_cls: 133.1526 loss_bbox: 120.5708 loss_dfl: 141.2191 2024/03/17 20:33:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:33:46 - mmengine - INFO - Epoch(train) [20][450/925] lr: 1.5545e-04 eta: 10:06:31 time: 0.6724 data_time: 0.0027 memory: 11119 grad_norm: 721.5142 loss: 392.7984 loss_cls: 132.9426 loss_bbox: 120.2768 loss_dfl: 139.5790 2024/03/17 20:34:18 - mmengine - INFO - Epoch(train) [20][500/925] lr: 1.5545e-04 eta: 10:05:55 time: 0.6336 data_time: 0.0024 memory: 11319 grad_norm: 692.3925 loss: 402.6686 loss_cls: 137.4397 loss_bbox: 123.0873 loss_dfl: 142.1416 2024/03/17 20:34:51 - mmengine - INFO - Epoch(train) [20][550/925] lr: 1.5545e-04 eta: 10:05:26 time: 0.6697 data_time: 0.0027 memory: 11466 grad_norm: 715.9030 loss: 392.1108 loss_cls: 133.2407 loss_bbox: 119.6839 loss_dfl: 139.1862 2024/03/17 20:35:24 - mmengine - INFO - Epoch(train) [20][600/925] lr: 1.5545e-04 eta: 10:04:55 time: 0.6573 data_time: 0.0024 memory: 11559 grad_norm: 671.3156 loss: 398.5390 loss_cls: 134.4457 loss_bbox: 122.6639 loss_dfl: 141.4293 2024/03/17 20:35:56 - mmengine - INFO - Epoch(train) [20][650/925] lr: 1.5545e-04 eta: 10:04:20 time: 0.6377 data_time: 0.0025 memory: 11599 grad_norm: 759.4176 loss: 395.1829 loss_cls: 135.5373 loss_bbox: 120.3317 loss_dfl: 139.3139 2024/03/17 20:36:29 - mmengine - INFO - Epoch(train) [20][700/925] lr: 1.5545e-04 eta: 10:03:48 time: 0.6497 data_time: 0.0026 memory: 11506 grad_norm: 630.8229 loss: 404.1684 loss_cls: 138.6151 loss_bbox: 123.5791 loss_dfl: 141.9742 2024/03/17 20:37:01 - mmengine - INFO - Epoch(train) [20][750/925] lr: 1.5545e-04 eta: 10:03:13 time: 0.6390 data_time: 0.0024 memory: 11372 grad_norm: 690.8919 loss: 395.9478 loss_cls: 134.9428 loss_bbox: 119.5047 loss_dfl: 141.5002 2024/03/17 20:37:33 - mmengine - INFO - Epoch(train) [20][800/925] lr: 1.5545e-04 eta: 10:02:40 time: 0.6432 data_time: 0.0026 memory: 11426 grad_norm: 707.3375 loss: 397.9571 loss_cls: 136.9651 loss_bbox: 120.6094 loss_dfl: 140.3826 2024/03/17 20:38:06 - mmengine - INFO - Epoch(train) [20][850/925] lr: 1.5545e-04 eta: 10:02:10 time: 0.6645 data_time: 0.0026 memory: 12026 grad_norm: 666.6304 loss: 396.9380 loss_cls: 135.8519 loss_bbox: 121.1507 loss_dfl: 139.9354 2024/03/17 20:38:40 - mmengine - INFO - Epoch(train) [20][900/925] lr: 1.5545e-04 eta: 10:01:40 time: 0.6711 data_time: 0.0027 memory: 11412 grad_norm: 653.1799 loss: 396.8501 loss_cls: 135.8604 loss_bbox: 120.1058 loss_dfl: 140.8839 2024/03/17 20:38:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:38:56 - mmengine - INFO - Saving checkpoint at 20 epochs 2024/03/17 20:39:06 - mmengine - INFO - Epoch(val) [20][ 50/625] eta: 0:00:13 time: 0.0240 data_time: 0.0009 memory: 11146 2024/03/17 20:39:07 - mmengine - INFO - Epoch(val) [20][100/625] eta: 0:00:12 time: 0.0233 data_time: 0.0004 memory: 1709 2024/03/17 20:39:08 - mmengine - INFO - Epoch(val) [20][150/625] eta: 0:00:11 time: 0.0243 data_time: 0.0004 memory: 1709 2024/03/17 20:39:09 - mmengine - INFO - Epoch(val) [20][200/625] eta: 0:00:10 time: 0.0239 data_time: 0.0004 memory: 1709 2024/03/17 20:39:11 - mmengine - INFO - Epoch(val) [20][250/625] eta: 0:00:09 time: 0.0248 data_time: 0.0004 memory: 1709 2024/03/17 20:39:12 - mmengine - INFO - Epoch(val) [20][300/625] eta: 0:00:07 time: 0.0233 data_time: 0.0003 memory: 1709 2024/03/17 20:39:13 - mmengine - INFO - Epoch(val) [20][350/625] eta: 0:00:06 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/17 20:39:14 - mmengine - INFO - Epoch(val) [20][400/625] eta: 0:00:05 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/17 20:39:15 - mmengine - INFO - Epoch(val) [20][450/625] eta: 0:00:04 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/17 20:39:16 - mmengine - INFO - Epoch(val) [20][500/625] eta: 0:00:02 time: 0.0226 data_time: 0.0003 memory: 1709 2024/03/17 20:39:18 - mmengine - INFO - Epoch(val) [20][550/625] eta: 0:00:01 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/17 20:39:19 - mmengine - INFO - Epoch(val) [20][600/625] eta: 0:00:00 time: 0.0263 data_time: 0.0039 memory: 1709 2024/03/17 20:39:31 - mmengine - INFO - Evaluating bbox... 2024/03/17 20:40:44 - mmengine - INFO - bbox_mAP_copypaste: 0.516 0.681 0.564 0.340 0.565 0.666 2024/03/17 20:40:46 - mmengine - INFO - Epoch(val) [20][625/625] coco/bbox_mAP: 0.5160 coco/bbox_mAP_50: 0.6810 coco/bbox_mAP_75: 0.5640 coco/bbox_mAP_s: 0.3400 coco/bbox_mAP_m: 0.5650 coco/bbox_mAP_l: 0.6660 data_time: 0.0039 time: 0.0262 2024/03/17 20:41:21 - mmengine - INFO - Epoch(train) [21][ 50/925] lr: 1.5297e-04 eta: 10:00:59 time: 0.7125 data_time: 0.0666 memory: 11679 grad_norm: 635.4144 loss: 396.1421 loss_cls: 134.1271 loss_bbox: 121.1800 loss_dfl: 140.8350 2024/03/17 20:41:55 - mmengine - INFO - Epoch(train) [21][100/925] lr: 1.5297e-04 eta: 10:00:29 time: 0.6670 data_time: 0.0026 memory: 11399 grad_norm: 682.4051 loss: 399.8852 loss_cls: 135.4150 loss_bbox: 122.0163 loss_dfl: 142.4538 2024/03/17 20:42:28 - mmengine - INFO - Epoch(train) [21][150/925] lr: 1.5297e-04 eta: 9:59:59 time: 0.6732 data_time: 0.0029 memory: 11772 grad_norm: 658.8928 loss: 395.2106 loss_cls: 134.1106 loss_bbox: 119.8956 loss_dfl: 141.2044 2024/03/17 20:43:02 - mmengine - INFO - Epoch(train) [21][200/925] lr: 1.5297e-04 eta: 9:59:30 time: 0.6740 data_time: 0.0026 memory: 11199 grad_norm: 694.3164 loss: 392.3180 loss_cls: 134.2188 loss_bbox: 118.3960 loss_dfl: 139.7032 2024/03/17 20:43:36 - mmengine - INFO - Epoch(train) [21][250/925] lr: 1.5297e-04 eta: 9:59:01 time: 0.6734 data_time: 0.0025 memory: 11492 grad_norm: 682.5274 loss: 397.2284 loss_cls: 135.7811 loss_bbox: 120.9499 loss_dfl: 140.4975 2024/03/17 20:44:09 - mmengine - INFO - Epoch(train) [21][300/925] lr: 1.5297e-04 eta: 9:58:30 time: 0.6579 data_time: 0.0026 memory: 11346 grad_norm: 700.3718 loss: 397.2990 loss_cls: 135.9384 loss_bbox: 120.5382 loss_dfl: 140.8224 2024/03/17 20:44:41 - mmengine - INFO - Epoch(train) [21][350/925] lr: 1.5297e-04 eta: 9:57:57 time: 0.6501 data_time: 0.0025 memory: 11532 grad_norm: 654.2080 loss: 399.3097 loss_cls: 136.8664 loss_bbox: 120.3757 loss_dfl: 142.0676 2024/03/17 20:45:15 - mmengine - INFO - Epoch(train) [21][400/925] lr: 1.5297e-04 eta: 9:57:27 time: 0.6664 data_time: 0.0025 memory: 11199 grad_norm: 693.1911 loss: 394.7285 loss_cls: 133.7666 loss_bbox: 121.6330 loss_dfl: 139.3289 2024/03/17 20:45:49 - mmengine - INFO - Epoch(train) [21][450/925] lr: 1.5297e-04 eta: 9:56:59 time: 0.6830 data_time: 0.0028 memory: 11412 grad_norm: 661.9972 loss: 400.4521 loss_cls: 136.3721 loss_bbox: 122.6362 loss_dfl: 141.4438 2024/03/17 20:46:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:46:24 - mmengine - INFO - Epoch(train) [21][500/925] lr: 1.5297e-04 eta: 9:56:34 time: 0.7032 data_time: 0.0030 memory: 11252 grad_norm: 704.9969 loss: 399.9231 loss_cls: 136.0654 loss_bbox: 122.0332 loss_dfl: 141.8244 2024/03/17 20:46:57 - mmengine - INFO - Epoch(train) [21][550/925] lr: 1.5297e-04 eta: 9:56:03 time: 0.6565 data_time: 0.0024 memory: 11732 grad_norm: 721.7570 loss: 400.5642 loss_cls: 138.0616 loss_bbox: 120.7786 loss_dfl: 141.7241 2024/03/17 20:47:31 - mmengine - INFO - Epoch(train) [21][600/925] lr: 1.5297e-04 eta: 9:55:35 time: 0.6867 data_time: 0.0027 memory: 11332 grad_norm: 713.2374 loss: 397.1986 loss_cls: 135.2207 loss_bbox: 121.8999 loss_dfl: 140.0780 2024/03/17 20:48:04 - mmengine - INFO - Epoch(train) [21][650/925] lr: 1.5297e-04 eta: 9:55:04 time: 0.6581 data_time: 0.0024 memory: 11426 grad_norm: 661.6384 loss: 389.5988 loss_cls: 131.7780 loss_bbox: 117.6796 loss_dfl: 140.1412 2024/03/17 20:48:37 - mmengine - INFO - Epoch(train) [21][700/925] lr: 1.5297e-04 eta: 9:54:32 time: 0.6543 data_time: 0.0026 memory: 11386 grad_norm: 702.6330 loss: 403.8335 loss_cls: 138.8641 loss_bbox: 122.8531 loss_dfl: 142.1162 2024/03/17 20:49:10 - mmengine - INFO - Epoch(train) [21][750/925] lr: 1.5297e-04 eta: 9:54:01 time: 0.6640 data_time: 0.0022 memory: 11519 grad_norm: 711.3703 loss: 400.1317 loss_cls: 137.6352 loss_bbox: 121.1701 loss_dfl: 141.3263 2024/03/17 20:49:43 - mmengine - INFO - Epoch(train) [21][800/925] lr: 1.5297e-04 eta: 9:53:31 time: 0.6663 data_time: 0.0024 memory: 11386 grad_norm: 675.5767 loss: 398.8789 loss_cls: 136.3813 loss_bbox: 121.6106 loss_dfl: 140.8870 2024/03/17 20:50:16 - mmengine - INFO - Epoch(train) [21][850/925] lr: 1.5297e-04 eta: 9:53:00 time: 0.6633 data_time: 0.0026 memory: 11479 grad_norm: 685.4451 loss: 389.6798 loss_cls: 131.7080 loss_bbox: 118.5857 loss_dfl: 139.3861 2024/03/17 20:50:50 - mmengine - INFO - Epoch(train) [21][900/925] lr: 1.5297e-04 eta: 9:52:29 time: 0.6662 data_time: 0.0030 memory: 11479 grad_norm: 647.5854 loss: 399.8413 loss_cls: 137.9488 loss_bbox: 121.1991 loss_dfl: 140.6934 2024/03/17 20:51:06 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:51:44 - mmengine - INFO - Epoch(train) [22][ 50/925] lr: 1.5050e-04 eta: 9:51:55 time: 0.7648 data_time: 0.0833 memory: 11839 grad_norm: 674.3870 loss: 395.7482 loss_cls: 134.7106 loss_bbox: 120.6460 loss_dfl: 140.3916 2024/03/17 20:52:17 - mmengine - INFO - Epoch(train) [22][100/925] lr: 1.5050e-04 eta: 9:51:21 time: 0.6438 data_time: 0.0025 memory: 11839 grad_norm: 707.3962 loss: 389.0475 loss_cls: 132.3072 loss_bbox: 116.8066 loss_dfl: 139.9337 2024/03/17 20:52:50 - mmengine - INFO - Epoch(train) [22][150/925] lr: 1.5050e-04 eta: 9:50:52 time: 0.6720 data_time: 0.0025 memory: 11612 grad_norm: 704.2777 loss: 402.4457 loss_cls: 137.7221 loss_bbox: 122.6513 loss_dfl: 142.0723 2024/03/17 20:53:23 - mmengine - INFO - Epoch(train) [22][200/925] lr: 1.5050e-04 eta: 9:50:21 time: 0.6623 data_time: 0.0024 memory: 11372 grad_norm: 671.0537 loss: 396.7262 loss_cls: 135.7263 loss_bbox: 120.0238 loss_dfl: 140.9761 2024/03/17 20:53:56 - mmengine - INFO - Epoch(train) [22][250/925] lr: 1.5050e-04 eta: 9:49:48 time: 0.6507 data_time: 0.0025 memory: 11386 grad_norm: 716.7706 loss: 397.0362 loss_cls: 135.5882 loss_bbox: 121.1766 loss_dfl: 140.2714 2024/03/17 20:54:30 - mmengine - INFO - Epoch(train) [22][300/925] lr: 1.5050e-04 eta: 9:49:18 time: 0.6716 data_time: 0.0026 memory: 11679 grad_norm: 702.9858 loss: 394.5501 loss_cls: 135.1243 loss_bbox: 119.1386 loss_dfl: 140.2872 2024/03/17 20:55:03 - mmengine - INFO - Epoch(train) [22][350/925] lr: 1.5050e-04 eta: 9:48:47 time: 0.6615 data_time: 0.0026 memory: 11559 grad_norm: 648.0183 loss: 395.5408 loss_cls: 135.6455 loss_bbox: 120.2391 loss_dfl: 139.6562 2024/03/17 20:55:37 - mmengine - INFO - Epoch(train) [22][400/925] lr: 1.5050e-04 eta: 9:48:19 time: 0.6854 data_time: 0.0028 memory: 11239 grad_norm: 745.3398 loss: 400.9085 loss_cls: 137.7330 loss_bbox: 121.8149 loss_dfl: 141.3606 2024/03/17 20:56:10 - mmengine - INFO - Epoch(train) [22][450/925] lr: 1.5050e-04 eta: 9:47:49 time: 0.6672 data_time: 0.0025 memory: 11959 grad_norm: 678.7627 loss: 392.1704 loss_cls: 132.7991 loss_bbox: 118.2478 loss_dfl: 141.1235 2024/03/17 20:56:44 - mmengine - INFO - Epoch(train) [22][500/925] lr: 1.5050e-04 eta: 9:47:18 time: 0.6664 data_time: 0.0027 memory: 11279 grad_norm: 757.1721 loss: 398.6712 loss_cls: 135.4769 loss_bbox: 121.6769 loss_dfl: 141.5174 2024/03/17 20:57:17 - mmengine - INFO - Epoch(train) [22][550/925] lr: 1.5050e-04 eta: 9:46:48 time: 0.6678 data_time: 0.0024 memory: 11359 grad_norm: 636.7416 loss: 399.0590 loss_cls: 136.8213 loss_bbox: 120.5301 loss_dfl: 141.7077 2024/03/17 20:57:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 20:57:50 - mmengine - INFO - Epoch(train) [22][600/925] lr: 1.5050e-04 eta: 9:46:16 time: 0.6573 data_time: 0.0024 memory: 11412 grad_norm: 675.1637 loss: 396.1258 loss_cls: 134.5887 loss_bbox: 120.9107 loss_dfl: 140.6264 2024/03/17 20:58:24 - mmengine - INFO - Epoch(train) [22][650/925] lr: 1.5050e-04 eta: 9:45:46 time: 0.6744 data_time: 0.0024 memory: 11252 grad_norm: 671.9970 loss: 406.5757 loss_cls: 139.7978 loss_bbox: 123.8182 loss_dfl: 142.9597 2024/03/17 20:58:59 - mmengine - INFO - Epoch(train) [22][700/925] lr: 1.5050e-04 eta: 9:45:20 time: 0.7001 data_time: 0.0028 memory: 11292 grad_norm: inf loss: 392.2949 loss_cls: 132.2257 loss_bbox: 120.3145 loss_dfl: 139.7547 2024/03/17 20:59:32 - mmengine - INFO - Epoch(train) [22][750/925] lr: 1.5050e-04 eta: 9:44:50 time: 0.6688 data_time: 0.0030 memory: 11412 grad_norm: 710.2923 loss: 397.6289 loss_cls: 135.7407 loss_bbox: 119.8299 loss_dfl: 142.0582 2024/03/17 21:00:07 - mmengine - INFO - Epoch(train) [22][800/925] lr: 1.5050e-04 eta: 9:44:22 time: 0.6876 data_time: 0.0027 memory: 11519 grad_norm: 721.0548 loss: 397.1186 loss_cls: 135.0769 loss_bbox: 121.0775 loss_dfl: 140.9642 2024/03/17 21:00:41 - mmengine - INFO - Epoch(train) [22][850/925] lr: 1.5050e-04 eta: 9:43:53 time: 0.6787 data_time: 0.0029 memory: 11359 grad_norm: 685.9914 loss: 395.7200 loss_cls: 135.2702 loss_bbox: 119.3622 loss_dfl: 141.0875 2024/03/17 21:01:14 - mmengine - INFO - Epoch(train) [22][900/925] lr: 1.5050e-04 eta: 9:43:22 time: 0.6634 data_time: 0.0026 memory: 11492 grad_norm: 726.3142 loss: 395.5138 loss_cls: 134.9468 loss_bbox: 120.7318 loss_dfl: 139.8352 2024/03/17 21:01:30 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:02:07 - mmengine - INFO - Epoch(train) [23][ 50/925] lr: 1.4803e-04 eta: 9:42:43 time: 0.7274 data_time: 0.0637 memory: 11479 grad_norm: 681.4766 loss: 392.7410 loss_cls: 132.1565 loss_bbox: 120.9804 loss_dfl: 139.6041 2024/03/17 21:02:40 - mmengine - INFO - Epoch(train) [23][100/925] lr: 1.4803e-04 eta: 9:42:10 time: 0.6550 data_time: 0.0026 memory: 11306 grad_norm: 723.3751 loss: 398.5123 loss_cls: 137.5055 loss_bbox: 121.0435 loss_dfl: 139.9633 2024/03/17 21:03:12 - mmengine - INFO - Epoch(train) [23][150/925] lr: 1.4803e-04 eta: 9:41:38 time: 0.6552 data_time: 0.0023 memory: 12146 grad_norm: 716.8725 loss: 391.4737 loss_cls: 132.8257 loss_bbox: 118.9011 loss_dfl: 139.7469 2024/03/17 21:03:46 - mmengine - INFO - Epoch(train) [23][200/925] lr: 1.4803e-04 eta: 9:41:07 time: 0.6661 data_time: 0.0025 memory: 11546 grad_norm: 711.0888 loss: 393.3195 loss_cls: 133.6415 loss_bbox: 119.4938 loss_dfl: 140.1842 2024/03/17 21:04:20 - mmengine - INFO - Epoch(train) [23][250/925] lr: 1.4803e-04 eta: 9:40:38 time: 0.6751 data_time: 0.0030 memory: 11173 grad_norm: 717.2175 loss: 396.7474 loss_cls: 135.6293 loss_bbox: 120.0858 loss_dfl: 141.0323 2024/03/17 21:04:53 - mmengine - INFO - Epoch(train) [23][300/925] lr: 1.4803e-04 eta: 9:40:06 time: 0.6597 data_time: 0.0027 memory: 11279 grad_norm: 674.9732 loss: 389.6834 loss_cls: 132.1683 loss_bbox: 118.4729 loss_dfl: 139.0422 2024/03/17 21:05:26 - mmengine - INFO - Epoch(train) [23][350/925] lr: 1.4803e-04 eta: 9:39:34 time: 0.6598 data_time: 0.0024 memory: 11679 grad_norm: 689.2687 loss: 395.1775 loss_cls: 134.6550 loss_bbox: 120.6270 loss_dfl: 139.8955 2024/03/17 21:05:59 - mmengine - INFO - Epoch(train) [23][400/925] lr: 1.4803e-04 eta: 9:39:03 time: 0.6590 data_time: 0.0021 memory: 11733 grad_norm: 687.3474 loss: 389.9340 loss_cls: 132.4190 loss_bbox: 118.6854 loss_dfl: 138.8296 2024/03/17 21:06:31 - mmengine - INFO - Epoch(train) [23][450/925] lr: 1.4803e-04 eta: 9:38:31 time: 0.6576 data_time: 0.0025 memory: 11466 grad_norm: 671.4288 loss: 391.4236 loss_cls: 134.0042 loss_bbox: 119.1523 loss_dfl: 138.2670 2024/03/17 21:07:04 - mmengine - INFO - Epoch(train) [23][500/925] lr: 1.4803e-04 eta: 9:37:57 time: 0.6481 data_time: 0.0024 memory: 11426 grad_norm: 720.1096 loss: 391.3559 loss_cls: 132.0296 loss_bbox: 119.5880 loss_dfl: 139.7383 2024/03/17 21:07:37 - mmengine - INFO - Epoch(train) [23][550/925] lr: 1.4803e-04 eta: 9:37:26 time: 0.6611 data_time: 0.0023 memory: 11399 grad_norm: 707.6544 loss: 389.4000 loss_cls: 132.4922 loss_bbox: 117.4286 loss_dfl: 139.4791 2024/03/17 21:08:10 - mmengine - INFO - Epoch(train) [23][600/925] lr: 1.4803e-04 eta: 9:36:55 time: 0.6628 data_time: 0.0025 memory: 11973 grad_norm: 694.0191 loss: 393.4773 loss_cls: 134.2448 loss_bbox: 118.9087 loss_dfl: 140.3237 2024/03/17 21:08:43 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:08:43 - mmengine - INFO - Epoch(train) [23][650/925] lr: 1.4803e-04 eta: 9:36:22 time: 0.6498 data_time: 0.0026 memory: 11186 grad_norm: 647.7622 loss: 390.3358 loss_cls: 133.2666 loss_bbox: 117.5533 loss_dfl: 139.5159 2024/03/17 21:09:17 - mmengine - INFO - Epoch(train) [23][700/925] lr: 1.4803e-04 eta: 9:35:54 time: 0.6893 data_time: 0.0027 memory: 11346 grad_norm: 686.8517 loss: 393.7092 loss_cls: 133.2370 loss_bbox: 120.4068 loss_dfl: 140.0654 2024/03/17 21:09:50 - mmengine - INFO - Epoch(train) [23][750/925] lr: 1.4803e-04 eta: 9:35:22 time: 0.6638 data_time: 0.0026 memory: 11346 grad_norm: 707.0118 loss: 400.4457 loss_cls: 137.5251 loss_bbox: 121.8151 loss_dfl: 141.1056 2024/03/17 21:10:23 - mmengine - INFO - Epoch(train) [23][800/925] lr: 1.4803e-04 eta: 9:34:49 time: 0.6462 data_time: 0.0027 memory: 11306 grad_norm: 668.2618 loss: 396.0094 loss_cls: 134.4118 loss_bbox: 121.1660 loss_dfl: 140.4317 2024/03/17 21:10:56 - mmengine - INFO - Epoch(train) [23][850/925] lr: 1.4803e-04 eta: 9:34:18 time: 0.6651 data_time: 0.0025 memory: 11466 grad_norm: 674.6898 loss: 397.2636 loss_cls: 134.3147 loss_bbox: 121.8576 loss_dfl: 141.0913 2024/03/17 21:11:29 - mmengine - INFO - Epoch(train) [23][900/925] lr: 1.4803e-04 eta: 9:33:45 time: 0.6512 data_time: 0.0023 memory: 11173 grad_norm: 713.0613 loss: 391.0025 loss_cls: 130.7143 loss_bbox: 119.7787 loss_dfl: 140.5095 2024/03/17 21:11:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:12:23 - mmengine - INFO - Epoch(train) [24][ 50/925] lr: 1.4555e-04 eta: 9:33:07 time: 0.7528 data_time: 0.0762 memory: 11266 grad_norm: 747.1587 loss: 398.2450 loss_cls: 135.6708 loss_bbox: 121.0962 loss_dfl: 141.4780 2024/03/17 21:12:57 - mmengine - INFO - Epoch(train) [24][100/925] lr: 1.4555e-04 eta: 9:32:40 time: 0.6960 data_time: 0.0029 memory: 11626 grad_norm: 705.7092 loss: 395.5552 loss_cls: 135.6626 loss_bbox: 119.5592 loss_dfl: 140.3334 2024/03/17 21:13:31 - mmengine - INFO - Epoch(train) [24][150/925] lr: 1.4555e-04 eta: 9:32:09 time: 0.6678 data_time: 0.0025 memory: 11479 grad_norm: 646.4756 loss: 393.3649 loss_cls: 132.5365 loss_bbox: 120.6763 loss_dfl: 140.1521 2024/03/17 21:14:04 - mmengine - INFO - Epoch(train) [24][200/925] lr: 1.4555e-04 eta: 9:31:38 time: 0.6657 data_time: 0.0028 memory: 11413 grad_norm: 669.5113 loss: 396.9529 loss_cls: 132.7288 loss_bbox: 122.8923 loss_dfl: 141.3319 2024/03/17 21:14:38 - mmengine - INFO - Epoch(train) [24][250/925] lr: 1.4555e-04 eta: 9:31:08 time: 0.6727 data_time: 0.0025 memory: 11666 grad_norm: 702.2344 loss: 392.6464 loss_cls: 133.3828 loss_bbox: 119.4691 loss_dfl: 139.7945 2024/03/17 21:15:11 - mmengine - INFO - Epoch(train) [24][300/925] lr: 1.4555e-04 eta: 9:30:37 time: 0.6668 data_time: 0.0025 memory: 11199 grad_norm: 643.7854 loss: 391.2855 loss_cls: 132.7096 loss_bbox: 118.3770 loss_dfl: 140.1988 2024/03/17 21:15:44 - mmengine - INFO - Epoch(train) [24][350/925] lr: 1.4555e-04 eta: 9:30:04 time: 0.6560 data_time: 0.0023 memory: 11466 grad_norm: 686.0787 loss: 395.7615 loss_cls: 134.4523 loss_bbox: 119.8862 loss_dfl: 141.4230 2024/03/17 21:16:17 - mmengine - INFO - Epoch(train) [24][400/925] lr: 1.4555e-04 eta: 9:29:32 time: 0.6543 data_time: 0.0024 memory: 11359 grad_norm: 729.7063 loss: 392.5537 loss_cls: 132.2316 loss_bbox: 119.8942 loss_dfl: 140.4280 2024/03/17 21:16:50 - mmengine - INFO - Epoch(train) [24][450/925] lr: 1.4555e-04 eta: 9:29:01 time: 0.6642 data_time: 0.0024 memory: 11119 grad_norm: 665.9214 loss: 395.5947 loss_cls: 135.5109 loss_bbox: 119.3886 loss_dfl: 140.6951 2024/03/17 21:17:23 - mmengine - INFO - Epoch(train) [24][500/925] lr: 1.4555e-04 eta: 9:28:29 time: 0.6584 data_time: 0.0027 memory: 11333 grad_norm: 658.7355 loss: 391.5580 loss_cls: 131.9756 loss_bbox: 119.6506 loss_dfl: 139.9319 2024/03/17 21:17:57 - mmengine - INFO - Epoch(train) [24][550/925] lr: 1.4555e-04 eta: 9:27:58 time: 0.6728 data_time: 0.0026 memory: 11466 grad_norm: 746.9834 loss: 395.2789 loss_cls: 135.3362 loss_bbox: 119.5139 loss_dfl: 140.4288 2024/03/17 21:18:30 - mmengine - INFO - Epoch(train) [24][600/925] lr: 1.4555e-04 eta: 9:27:27 time: 0.6666 data_time: 0.0026 memory: 11586 grad_norm: 665.6947 loss: 388.3455 loss_cls: 131.2106 loss_bbox: 118.4057 loss_dfl: 138.7292 2024/03/17 21:19:03 - mmengine - INFO - Epoch(train) [24][650/925] lr: 1.4555e-04 eta: 9:26:55 time: 0.6595 data_time: 0.0026 memory: 11399 grad_norm: 717.0155 loss: 394.5763 loss_cls: 134.2476 loss_bbox: 119.7876 loss_dfl: 140.5412 2024/03/17 21:19:35 - mmengine - INFO - Epoch(train) [24][700/925] lr: 1.4555e-04 eta: 9:26:22 time: 0.6471 data_time: 0.0024 memory: 11079 grad_norm: 709.4384 loss: 384.9604 loss_cls: 128.7484 loss_bbox: 116.9910 loss_dfl: 139.2210 2024/03/17 21:19:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:20:09 - mmengine - INFO - Epoch(train) [24][750/925] lr: 1.4555e-04 eta: 9:25:51 time: 0.6689 data_time: 0.0025 memory: 11626 grad_norm: 705.0923 loss: 391.7410 loss_cls: 132.0664 loss_bbox: 120.0944 loss_dfl: 139.5803 2024/03/17 21:20:42 - mmengine - INFO - Epoch(train) [24][800/925] lr: 1.4555e-04 eta: 9:25:19 time: 0.6559 data_time: 0.0024 memory: 11373 grad_norm: 680.2018 loss: 393.7692 loss_cls: 132.8373 loss_bbox: 121.5628 loss_dfl: 139.3692 2024/03/17 21:21:14 - mmengine - INFO - Epoch(train) [24][850/925] lr: 1.4555e-04 eta: 9:24:46 time: 0.6561 data_time: 0.0026 memory: 11493 grad_norm: 690.1814 loss: 393.6747 loss_cls: 133.8147 loss_bbox: 119.8517 loss_dfl: 140.0084 2024/03/17 21:21:47 - mmengine - INFO - Epoch(train) [24][900/925] lr: 1.4555e-04 eta: 9:24:14 time: 0.6575 data_time: 0.0025 memory: 11399 grad_norm: 650.0793 loss: 393.2580 loss_cls: 134.6108 loss_bbox: 119.1409 loss_dfl: 139.5062 2024/03/17 21:22:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:22:41 - mmengine - INFO - Epoch(train) [25][ 50/925] lr: 1.4307e-04 eta: 9:23:35 time: 0.7394 data_time: 0.0753 memory: 11626 grad_norm: 681.3977 loss: 398.3983 loss_cls: 135.8564 loss_bbox: 120.3386 loss_dfl: 142.2034 2024/03/17 21:23:14 - mmengine - INFO - Epoch(train) [25][100/925] lr: 1.4307e-04 eta: 9:23:04 time: 0.6682 data_time: 0.0026 memory: 12293 grad_norm: 668.3343 loss: 392.9560 loss_cls: 133.8326 loss_bbox: 118.0814 loss_dfl: 141.0420 2024/03/17 21:23:47 - mmengine - INFO - Epoch(train) [25][150/925] lr: 1.4307e-04 eta: 9:22:32 time: 0.6580 data_time: 0.0025 memory: 11359 grad_norm: 678.1648 loss: 393.1167 loss_cls: 134.7678 loss_bbox: 118.7203 loss_dfl: 139.6287 2024/03/17 21:24:20 - mmengine - INFO - Epoch(train) [25][200/925] lr: 1.4307e-04 eta: 9:21:59 time: 0.6539 data_time: 0.0025 memory: 11692 grad_norm: 677.1152 loss: 389.1006 loss_cls: 131.4724 loss_bbox: 118.6298 loss_dfl: 138.9985 2024/03/17 21:24:53 - mmengine - INFO - Epoch(train) [25][250/925] lr: 1.4307e-04 eta: 9:21:26 time: 0.6505 data_time: 0.0025 memory: 11212 grad_norm: 659.4906 loss: 394.8381 loss_cls: 134.2964 loss_bbox: 119.4111 loss_dfl: 141.1306 2024/03/17 21:25:27 - mmengine - INFO - Epoch(train) [25][300/925] lr: 1.4307e-04 eta: 9:20:57 time: 0.6860 data_time: 0.0031 memory: 11385 grad_norm: 688.9945 loss: 392.0067 loss_cls: 132.7071 loss_bbox: 119.8522 loss_dfl: 139.4473 2024/03/17 21:26:01 - mmengine - INFO - Epoch(train) [25][350/925] lr: 1.4307e-04 eta: 9:20:29 time: 0.6897 data_time: 0.0028 memory: 11532 grad_norm: 667.6536 loss: 394.8833 loss_cls: 135.4881 loss_bbox: 118.5823 loss_dfl: 140.8128 2024/03/17 21:26:35 - mmengine - INFO - Epoch(train) [25][400/925] lr: 1.4307e-04 eta: 9:19:57 time: 0.6633 data_time: 0.0027 memory: 11239 grad_norm: 646.2789 loss: 391.3313 loss_cls: 132.5434 loss_bbox: 118.5018 loss_dfl: 140.2861 2024/03/17 21:27:08 - mmengine - INFO - Epoch(train) [25][450/925] lr: 1.4307e-04 eta: 9:19:25 time: 0.6578 data_time: 0.0026 memory: 11559 grad_norm: 698.8333 loss: 390.2698 loss_cls: 131.4987 loss_bbox: 118.7830 loss_dfl: 139.9882 2024/03/17 21:27:41 - mmengine - INFO - Epoch(train) [25][500/925] lr: 1.4307e-04 eta: 9:18:53 time: 0.6664 data_time: 0.0027 memory: 11279 grad_norm: 676.5787 loss: 388.5723 loss_cls: 130.0175 loss_bbox: 119.1948 loss_dfl: 139.3600 2024/03/17 21:28:14 - mmengine - INFO - Epoch(train) [25][550/925] lr: 1.4307e-04 eta: 9:18:21 time: 0.6576 data_time: 0.0026 memory: 11732 grad_norm: 660.6784 loss: 394.2435 loss_cls: 134.8202 loss_bbox: 118.8923 loss_dfl: 140.5310 2024/03/17 21:28:46 - mmengine - INFO - Epoch(train) [25][600/925] lr: 1.4307e-04 eta: 9:17:48 time: 0.6512 data_time: 0.0025 memory: 11345 grad_norm: 739.7227 loss: 390.5342 loss_cls: 130.5117 loss_bbox: 119.7069 loss_dfl: 140.3155 2024/03/17 21:29:19 - mmengine - INFO - Epoch(train) [25][650/925] lr: 1.4307e-04 eta: 9:17:15 time: 0.6506 data_time: 0.0025 memory: 11465 grad_norm: 676.8873 loss: 397.3031 loss_cls: 134.4054 loss_bbox: 121.8656 loss_dfl: 141.0321 2024/03/17 21:29:52 - mmengine - INFO - Epoch(train) [25][700/925] lr: 1.4307e-04 eta: 9:16:43 time: 0.6545 data_time: 0.0025 memory: 11452 grad_norm: 673.3666 loss: 391.9484 loss_cls: 132.1567 loss_bbox: 119.1323 loss_dfl: 140.6594 2024/03/17 21:30:25 - mmengine - INFO - Epoch(train) [25][750/925] lr: 1.4307e-04 eta: 9:16:11 time: 0.6595 data_time: 0.0027 memory: 11345 grad_norm: 747.3420 loss: 385.8965 loss_cls: 128.9649 loss_bbox: 117.8353 loss_dfl: 139.0964 2024/03/17 21:30:58 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:30:58 - mmengine - INFO - Epoch(train) [25][800/925] lr: 1.4307e-04 eta: 9:15:40 time: 0.6725 data_time: 0.0027 memory: 11359 grad_norm: 741.3824 loss: 393.8964 loss_cls: 133.4883 loss_bbox: 119.3946 loss_dfl: 141.0135 2024/03/17 21:31:32 - mmengine - INFO - Epoch(train) [25][850/925] lr: 1.4307e-04 eta: 9:15:08 time: 0.6626 data_time: 0.0029 memory: 11239 grad_norm: 687.0638 loss: 393.3789 loss_cls: 132.4496 loss_bbox: 120.8347 loss_dfl: 140.0946 2024/03/17 21:32:04 - mmengine - INFO - Epoch(train) [25][900/925] lr: 1.4307e-04 eta: 9:14:35 time: 0.6442 data_time: 0.0026 memory: 11412 grad_norm: 676.4158 loss: 391.8534 loss_cls: 132.2225 loss_bbox: 119.8660 loss_dfl: 139.7649 2024/03/17 21:32:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:32:20 - mmengine - INFO - Saving checkpoint at 25 epochs 2024/03/17 21:32:28 - mmengine - INFO - Epoch(val) [25][ 50/625] eta: 0:00:14 time: 0.0256 data_time: 0.0007 memory: 11052 2024/03/17 21:32:30 - mmengine - INFO - Epoch(val) [25][100/625] eta: 0:00:13 time: 0.0260 data_time: 0.0003 memory: 1709 2024/03/17 21:32:31 - mmengine - INFO - Epoch(val) [25][150/625] eta: 0:00:12 time: 0.0266 data_time: 0.0003 memory: 1709 2024/03/17 21:32:32 - mmengine - INFO - Epoch(val) [25][200/625] eta: 0:00:11 time: 0.0258 data_time: 0.0003 memory: 1709 2024/03/17 21:32:33 - mmengine - INFO - Epoch(val) [25][250/625] eta: 0:00:09 time: 0.0262 data_time: 0.0003 memory: 1709 2024/03/17 21:32:35 - mmengine - INFO - Epoch(val) [25][300/625] eta: 0:00:08 time: 0.0251 data_time: 0.0002 memory: 1709 2024/03/17 21:32:36 - mmengine - INFO - Epoch(val) [25][350/625] eta: 0:00:07 time: 0.0246 data_time: 0.0002 memory: 1709 2024/03/17 21:32:37 - mmengine - INFO - Epoch(val) [25][400/625] eta: 0:00:05 time: 0.0229 data_time: 0.0002 memory: 1709 2024/03/17 21:32:38 - mmengine - INFO - Epoch(val) [25][450/625] eta: 0:00:04 time: 0.0206 data_time: 0.0002 memory: 1709 2024/03/17 21:32:39 - mmengine - INFO - Epoch(val) [25][500/625] eta: 0:00:03 time: 0.0215 data_time: 0.0002 memory: 1709 2024/03/17 21:32:40 - mmengine - INFO - Epoch(val) [25][550/625] eta: 0:00:01 time: 0.0221 data_time: 0.0002 memory: 1709 2024/03/17 21:32:41 - mmengine - INFO - Epoch(val) [25][600/625] eta: 0:00:00 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/17 21:32:51 - mmengine - INFO - Evaluating bbox... 2024/03/17 21:33:55 - mmengine - INFO - bbox_mAP_copypaste: 0.520 0.686 0.567 0.343 0.571 0.674 2024/03/17 21:33:56 - mmengine - INFO - Epoch(val) [25][625/625] coco/bbox_mAP: 0.5200 coco/bbox_mAP_50: 0.6860 coco/bbox_mAP_75: 0.5670 coco/bbox_mAP_s: 0.3430 coco/bbox_mAP_m: 0.5710 coco/bbox_mAP_l: 0.6740 data_time: 0.0002 time: 0.0216 2024/03/17 21:34:32 - mmengine - INFO - Epoch(train) [26][ 50/925] lr: 1.4060e-04 eta: 9:13:51 time: 0.7155 data_time: 0.0542 memory: 11252 grad_norm: 733.3824 loss: 395.2110 loss_cls: 132.9657 loss_bbox: 120.6938 loss_dfl: 141.5515 2024/03/17 21:35:05 - mmengine - INFO - Epoch(train) [26][100/925] lr: 1.4060e-04 eta: 9:13:19 time: 0.6582 data_time: 0.0026 memory: 11772 grad_norm: 677.2684 loss: 393.0062 loss_cls: 131.8649 loss_bbox: 120.5333 loss_dfl: 140.6080 2024/03/17 21:35:39 - mmengine - INFO - Epoch(train) [26][150/925] lr: 1.4060e-04 eta: 9:12:51 time: 0.6941 data_time: 0.0031 memory: 11359 grad_norm: 653.9078 loss: 391.6735 loss_cls: 133.8773 loss_bbox: 117.0278 loss_dfl: 140.7684 2024/03/17 21:36:13 - mmengine - INFO - Epoch(train) [26][200/925] lr: 1.4060e-04 eta: 9:12:21 time: 0.6807 data_time: 0.0027 memory: 11932 grad_norm: 666.2286 loss: 399.6561 loss_cls: 135.9947 loss_bbox: 121.9519 loss_dfl: 141.7096 2024/03/17 21:36:47 - mmengine - INFO - Epoch(train) [26][250/925] lr: 1.4060e-04 eta: 9:11:49 time: 0.6614 data_time: 0.0025 memory: 11799 grad_norm: 642.5412 loss: 393.6752 loss_cls: 133.2794 loss_bbox: 119.9891 loss_dfl: 140.4066 2024/03/17 21:37:19 - mmengine - INFO - Epoch(train) [26][300/925] lr: 1.4060e-04 eta: 9:11:17 time: 0.6542 data_time: 0.0025 memory: 11185 grad_norm: 677.3525 loss: 391.6852 loss_cls: 134.0966 loss_bbox: 117.5833 loss_dfl: 140.0053 2024/03/17 21:37:53 - mmengine - INFO - Epoch(train) [26][350/925] lr: 1.4060e-04 eta: 9:10:46 time: 0.6782 data_time: 0.0024 memory: 11065 grad_norm: 683.1195 loss: 393.8431 loss_cls: 133.6303 loss_bbox: 119.0524 loss_dfl: 141.1603 2024/03/17 21:38:27 - mmengine - INFO - Epoch(train) [26][400/925] lr: 1.4060e-04 eta: 9:10:16 time: 0.6743 data_time: 0.0026 memory: 11732 grad_norm: 678.5354 loss: 396.8517 loss_cls: 134.8012 loss_bbox: 120.8745 loss_dfl: 141.1760 2024/03/17 21:39:01 - mmengine - INFO - Epoch(train) [26][450/925] lr: 1.4060e-04 eta: 9:09:47 time: 0.6897 data_time: 0.0030 memory: 11225 grad_norm: 658.0313 loss: 387.8796 loss_cls: 130.1337 loss_bbox: 117.9723 loss_dfl: 139.7736 2024/03/17 21:39:36 - mmengine - INFO - Epoch(train) [26][500/925] lr: 1.4060e-04 eta: 9:09:17 time: 0.6836 data_time: 0.0030 memory: 11479 grad_norm: 659.2085 loss: 394.8139 loss_cls: 132.7879 loss_bbox: 120.9590 loss_dfl: 141.0669 2024/03/17 21:40:09 - mmengine - INFO - Epoch(train) [26][550/925] lr: 1.4060e-04 eta: 9:08:47 time: 0.6729 data_time: 0.0025 memory: 11372 grad_norm: 692.1660 loss: 389.3961 loss_cls: 130.3300 loss_bbox: 119.4583 loss_dfl: 139.6079 2024/03/17 21:40:43 - mmengine - INFO - Epoch(train) [26][600/925] lr: 1.4060e-04 eta: 9:08:16 time: 0.6762 data_time: 0.0027 memory: 11479 grad_norm: 700.2112 loss: 387.3849 loss_cls: 128.6638 loss_bbox: 118.7797 loss_dfl: 139.9414 2024/03/17 21:41:16 - mmengine - INFO - Epoch(train) [26][650/925] lr: 1.4060e-04 eta: 9:07:44 time: 0.6619 data_time: 0.0024 memory: 11545 grad_norm: 641.1082 loss: 386.4477 loss_cls: 129.9073 loss_bbox: 117.0839 loss_dfl: 139.4566 2024/03/17 21:41:50 - mmengine - INFO - Epoch(train) [26][700/925] lr: 1.4060e-04 eta: 9:07:13 time: 0.6664 data_time: 0.0026 memory: 11492 grad_norm: 691.0339 loss: 396.2674 loss_cls: 135.3332 loss_bbox: 120.4986 loss_dfl: 140.4355 2024/03/17 21:42:23 - mmengine - INFO - Epoch(train) [26][750/925] lr: 1.4060e-04 eta: 9:06:41 time: 0.6617 data_time: 0.0026 memory: 11652 grad_norm: 753.5998 loss: 391.9928 loss_cls: 131.8492 loss_bbox: 119.5586 loss_dfl: 140.5849 2024/03/17 21:42:56 - mmengine - INFO - Epoch(train) [26][800/925] lr: 1.4060e-04 eta: 9:06:09 time: 0.6635 data_time: 0.0024 memory: 11332 grad_norm: 679.4473 loss: 394.6178 loss_cls: 132.6241 loss_bbox: 121.0079 loss_dfl: 140.9858 2024/03/17 21:43:30 - mmengine - INFO - Epoch(train) [26][850/925] lr: 1.4060e-04 eta: 9:05:39 time: 0.6749 data_time: 0.0027 memory: 11332 grad_norm: 731.3875 loss: 392.5236 loss_cls: 133.0522 loss_bbox: 118.7096 loss_dfl: 140.7618 2024/03/17 21:43:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:44:04 - mmengine - INFO - Epoch(train) [26][900/925] lr: 1.4060e-04 eta: 9:05:09 time: 0.6810 data_time: 0.0028 memory: 11172 grad_norm: 731.0995 loss: 392.3873 loss_cls: 131.4399 loss_bbox: 120.3303 loss_dfl: 140.6171 2024/03/17 21:44:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:44:57 - mmengine - INFO - Epoch(train) [27][ 50/925] lr: 1.3813e-04 eta: 9:04:26 time: 0.7203 data_time: 0.0710 memory: 11319 grad_norm: 661.1463 loss: 390.5899 loss_cls: 130.4595 loss_bbox: 119.9389 loss_dfl: 140.1915 2024/03/17 21:45:29 - mmengine - INFO - Epoch(train) [27][100/925] lr: 1.3813e-04 eta: 9:03:53 time: 0.6471 data_time: 0.0026 memory: 11345 grad_norm: 697.6204 loss: 391.3458 loss_cls: 132.0354 loss_bbox: 118.9373 loss_dfl: 140.3731 2024/03/17 21:46:02 - mmengine - INFO - Epoch(train) [27][150/925] lr: 1.3813e-04 eta: 9:03:20 time: 0.6497 data_time: 0.0025 memory: 11559 grad_norm: 698.1904 loss: 392.5859 loss_cls: 131.5914 loss_bbox: 119.6878 loss_dfl: 141.3068 2024/03/17 21:46:34 - mmengine - INFO - Epoch(train) [27][200/925] lr: 1.3813e-04 eta: 9:02:46 time: 0.6434 data_time: 0.0025 memory: 11332 grad_norm: 743.9602 loss: 392.5318 loss_cls: 133.4580 loss_bbox: 118.9986 loss_dfl: 140.0753 2024/03/17 21:47:07 - mmengine - INFO - Epoch(train) [27][250/925] lr: 1.3813e-04 eta: 9:02:14 time: 0.6593 data_time: 0.0025 memory: 11159 grad_norm: 634.8083 loss: 386.4291 loss_cls: 129.8598 loss_bbox: 117.3072 loss_dfl: 139.2621 2024/03/17 21:47:39 - mmengine - INFO - Epoch(train) [27][300/925] lr: 1.3813e-04 eta: 9:01:40 time: 0.6452 data_time: 0.0027 memory: 11425 grad_norm: 675.1636 loss: 390.1519 loss_cls: 133.5235 loss_bbox: 117.1743 loss_dfl: 139.4542 2024/03/17 21:48:11 - mmengine - INFO - Epoch(train) [27][350/925] lr: 1.3813e-04 eta: 9:01:06 time: 0.6412 data_time: 0.0027 memory: 11345 grad_norm: 737.2565 loss: 388.3288 loss_cls: 129.9788 loss_bbox: 118.4665 loss_dfl: 139.8834 2024/03/17 21:48:45 - mmengine - INFO - Epoch(train) [27][400/925] lr: 1.3813e-04 eta: 9:00:35 time: 0.6723 data_time: 0.0027 memory: 11572 grad_norm: 705.5262 loss: 392.0259 loss_cls: 133.3617 loss_bbox: 118.3409 loss_dfl: 140.3233 2024/03/17 21:49:18 - mmengine - INFO - Epoch(train) [27][450/925] lr: 1.3813e-04 eta: 9:00:03 time: 0.6624 data_time: 0.0028 memory: 11079 grad_norm: 779.8089 loss: 389.7230 loss_cls: 128.9956 loss_bbox: 119.5743 loss_dfl: 141.1531 2024/03/17 21:49:50 - mmengine - INFO - Epoch(train) [27][500/925] lr: 1.3813e-04 eta: 8:59:30 time: 0.6489 data_time: 0.0026 memory: 11932 grad_norm: inf loss: 388.2549 loss_cls: 130.6165 loss_bbox: 117.7740 loss_dfl: 139.8644 2024/03/17 21:50:23 - mmengine - INFO - Epoch(train) [27][550/925] lr: 1.3813e-04 eta: 8:58:56 time: 0.6459 data_time: 0.0025 memory: 11159 grad_norm: 684.8668 loss: 396.8682 loss_cls: 134.8790 loss_bbox: 120.3495 loss_dfl: 141.6397 2024/03/17 21:50:56 - mmengine - INFO - Epoch(train) [27][600/925] lr: 1.3813e-04 eta: 8:58:23 time: 0.6559 data_time: 0.0026 memory: 11239 grad_norm: 670.4845 loss: 386.9355 loss_cls: 130.7540 loss_bbox: 117.6770 loss_dfl: 138.5046 2024/03/17 21:51:28 - mmengine - INFO - Epoch(train) [27][650/925] lr: 1.3813e-04 eta: 8:57:50 time: 0.6477 data_time: 0.0026 memory: 11572 grad_norm: 749.2230 loss: 395.4033 loss_cls: 132.7895 loss_bbox: 121.7814 loss_dfl: 140.8324 2024/03/17 21:52:01 - mmengine - INFO - Epoch(train) [27][700/925] lr: 1.3813e-04 eta: 8:57:17 time: 0.6506 data_time: 0.0027 memory: 11159 grad_norm: 730.9045 loss: 393.0526 loss_cls: 132.9151 loss_bbox: 118.4034 loss_dfl: 141.7342 2024/03/17 21:52:34 - mmengine - INFO - Epoch(train) [27][750/925] lr: 1.3813e-04 eta: 8:56:46 time: 0.6694 data_time: 0.0029 memory: 11305 grad_norm: 728.5807 loss: 392.4685 loss_cls: 132.0669 loss_bbox: 119.9524 loss_dfl: 140.4492 2024/03/17 21:53:08 - mmengine - INFO - Epoch(train) [27][800/925] lr: 1.3813e-04 eta: 8:56:15 time: 0.6759 data_time: 0.0030 memory: 11559 grad_norm: 675.5859 loss: 388.7701 loss_cls: 130.0092 loss_bbox: 118.6808 loss_dfl: 140.0801 2024/03/17 21:53:41 - mmengine - INFO - Epoch(train) [27][850/925] lr: 1.3813e-04 eta: 8:55:42 time: 0.6558 data_time: 0.0026 memory: 11372 grad_norm: 767.3740 loss: 390.1256 loss_cls: 131.1436 loss_bbox: 119.4550 loss_dfl: 139.5269 2024/03/17 21:54:15 - mmengine - INFO - Epoch(train) [27][900/925] lr: 1.3813e-04 eta: 8:55:13 time: 0.6847 data_time: 0.0029 memory: 11625 grad_norm: 718.9378 loss: 388.1500 loss_cls: 129.0864 loss_bbox: 118.7979 loss_dfl: 140.2656 2024/03/17 21:54:30 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:54:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 21:55:06 - mmengine - INFO - Epoch(train) [28][ 50/925] lr: 1.3565e-04 eta: 8:54:26 time: 0.7116 data_time: 0.0515 memory: 11412 grad_norm: 679.2460 loss: 386.1913 loss_cls: 129.9609 loss_bbox: 117.8239 loss_dfl: 138.4065 2024/03/17 21:55:38 - mmengine - INFO - Epoch(train) [28][100/925] lr: 1.3565e-04 eta: 8:53:53 time: 0.6444 data_time: 0.0025 memory: 11745 grad_norm: 670.5649 loss: 391.6324 loss_cls: 132.2042 loss_bbox: 118.7379 loss_dfl: 140.6903 2024/03/17 21:56:11 - mmengine - INFO - Epoch(train) [28][150/925] lr: 1.3565e-04 eta: 8:53:21 time: 0.6609 data_time: 0.0025 memory: 11719 grad_norm: 698.7658 loss: 392.9948 loss_cls: 132.0005 loss_bbox: 120.2254 loss_dfl: 140.7689 2024/03/17 21:56:44 - mmengine - INFO - Epoch(train) [28][200/925] lr: 1.3565e-04 eta: 8:52:48 time: 0.6529 data_time: 0.0024 memory: 11399 grad_norm: 756.4381 loss: 394.6437 loss_cls: 133.7776 loss_bbox: 120.2517 loss_dfl: 140.6144 2024/03/17 21:57:17 - mmengine - INFO - Epoch(train) [28][250/925] lr: 1.3565e-04 eta: 8:52:16 time: 0.6633 data_time: 0.0026 memory: 11185 grad_norm: 660.4488 loss: 392.0185 loss_cls: 130.4415 loss_bbox: 121.3214 loss_dfl: 140.2555 2024/03/17 21:57:51 - mmengine - INFO - Epoch(train) [28][300/925] lr: 1.3565e-04 eta: 8:51:45 time: 0.6774 data_time: 0.0029 memory: 11465 grad_norm: 626.7546 loss: 391.9344 loss_cls: 131.3935 loss_bbox: 120.5280 loss_dfl: 140.0128 2024/03/17 21:58:24 - mmengine - INFO - Epoch(train) [28][350/925] lr: 1.3565e-04 eta: 8:51:12 time: 0.6522 data_time: 0.0029 memory: 11572 grad_norm: 661.5386 loss: 396.5787 loss_cls: 134.3865 loss_bbox: 120.8193 loss_dfl: 141.3730 2024/03/17 21:58:56 - mmengine - INFO - Epoch(train) [28][400/925] lr: 1.3565e-04 eta: 8:50:38 time: 0.6416 data_time: 0.0026 memory: 11279 grad_norm: 697.6830 loss: 391.0323 loss_cls: 133.3062 loss_bbox: 117.1877 loss_dfl: 140.5383 2024/03/17 21:59:28 - mmengine - INFO - Epoch(train) [28][450/925] lr: 1.3565e-04 eta: 8:50:05 time: 0.6500 data_time: 0.0024 memory: 11385 grad_norm: 708.0003 loss: 388.1511 loss_cls: 130.6188 loss_bbox: 118.3373 loss_dfl: 139.1950 2024/03/17 22:00:01 - mmengine - INFO - Epoch(train) [28][500/925] lr: 1.3565e-04 eta: 8:49:31 time: 0.6454 data_time: 0.0024 memory: 11319 grad_norm: 673.6558 loss: 390.1654 loss_cls: 131.1554 loss_bbox: 120.3294 loss_dfl: 138.6806 2024/03/17 22:00:33 - mmengine - INFO - Epoch(train) [28][550/925] lr: 1.3565e-04 eta: 8:48:58 time: 0.6501 data_time: 0.0024 memory: 11532 grad_norm: 670.7758 loss: 394.9323 loss_cls: 133.9576 loss_bbox: 120.1094 loss_dfl: 140.8653 2024/03/17 22:01:06 - mmengine - INFO - Epoch(train) [28][600/925] lr: 1.3565e-04 eta: 8:48:26 time: 0.6575 data_time: 0.0025 memory: 11345 grad_norm: 718.3822 loss: 391.5087 loss_cls: 132.0769 loss_bbox: 120.2749 loss_dfl: 139.1569 2024/03/17 22:01:39 - mmengine - INFO - Epoch(train) [28][650/925] lr: 1.3565e-04 eta: 8:47:54 time: 0.6603 data_time: 0.0028 memory: 11292 grad_norm: 665.8296 loss: 390.1291 loss_cls: 131.4678 loss_bbox: 119.9885 loss_dfl: 138.6727 2024/03/17 22:02:13 - mmengine - INFO - Epoch(train) [28][700/925] lr: 1.3565e-04 eta: 8:47:22 time: 0.6713 data_time: 0.0026 memory: 11412 grad_norm: 649.5329 loss: 387.5042 loss_cls: 128.3401 loss_bbox: 119.1307 loss_dfl: 140.0334 2024/03/17 22:02:46 - mmengine - INFO - Epoch(train) [28][750/925] lr: 1.3565e-04 eta: 8:46:50 time: 0.6566 data_time: 0.0030 memory: 11465 grad_norm: 725.8803 loss: 390.0801 loss_cls: 132.4625 loss_bbox: 118.5064 loss_dfl: 139.1112 2024/03/17 22:03:19 - mmengine - INFO - Epoch(train) [28][800/925] lr: 1.3565e-04 eta: 8:46:18 time: 0.6692 data_time: 0.0025 memory: 11305 grad_norm: 628.6756 loss: 390.2674 loss_cls: 133.2710 loss_bbox: 117.5252 loss_dfl: 139.4713 2024/03/17 22:03:52 - mmengine - INFO - Epoch(train) [28][850/925] lr: 1.3565e-04 eta: 8:45:45 time: 0.6521 data_time: 0.0025 memory: 11265 grad_norm: 767.9887 loss: 385.5340 loss_cls: 128.6186 loss_bbox: 117.6704 loss_dfl: 139.2450 2024/03/17 22:04:24 - mmengine - INFO - Epoch(train) [28][900/925] lr: 1.3565e-04 eta: 8:45:12 time: 0.6447 data_time: 0.0024 memory: 11892 grad_norm: 646.3477 loss: 393.7887 loss_cls: 130.8666 loss_bbox: 122.6528 loss_dfl: 140.2693 2024/03/17 22:04:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:05:16 - mmengine - INFO - Epoch(train) [29][ 50/925] lr: 1.3317e-04 eta: 8:44:26 time: 0.7090 data_time: 0.0604 memory: 11505 grad_norm: 677.5024 loss: 392.5900 loss_cls: 131.8846 loss_bbox: 120.0540 loss_dfl: 140.6514 2024/03/17 22:05:49 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:05:49 - mmengine - INFO - Epoch(train) [29][100/925] lr: 1.3317e-04 eta: 8:43:54 time: 0.6576 data_time: 0.0026 memory: 12052 grad_norm: 683.7220 loss: 386.7351 loss_cls: 130.3258 loss_bbox: 117.1590 loss_dfl: 139.2502 2024/03/17 22:06:21 - mmengine - INFO - Epoch(train) [29][150/925] lr: 1.3317e-04 eta: 8:43:21 time: 0.6545 data_time: 0.0027 memory: 11319 grad_norm: 724.4239 loss: 389.9674 loss_cls: 131.1298 loss_bbox: 118.4498 loss_dfl: 140.3878 2024/03/17 22:06:54 - mmengine - INFO - Epoch(train) [29][200/925] lr: 1.3317e-04 eta: 8:42:48 time: 0.6552 data_time: 0.0025 memory: 11359 grad_norm: 666.8077 loss: 391.8019 loss_cls: 132.6911 loss_bbox: 119.2357 loss_dfl: 139.8751 2024/03/17 22:07:26 - mmengine - INFO - Epoch(train) [29][250/925] lr: 1.3317e-04 eta: 8:42:15 time: 0.6443 data_time: 0.0025 memory: 11132 grad_norm: 755.0258 loss: 396.8978 loss_cls: 133.6512 loss_bbox: 121.6321 loss_dfl: 141.6146 2024/03/17 22:07:58 - mmengine - INFO - Epoch(train) [29][300/925] lr: 1.3317e-04 eta: 8:41:40 time: 0.6361 data_time: 0.0024 memory: 11252 grad_norm: 737.8897 loss: 386.3173 loss_cls: 127.3293 loss_bbox: 119.7488 loss_dfl: 139.2391 2024/03/17 22:08:31 - mmengine - INFO - Epoch(train) [29][350/925] lr: 1.3317e-04 eta: 8:41:07 time: 0.6507 data_time: 0.0026 memory: 11292 grad_norm: 670.9212 loss: 389.1272 loss_cls: 130.8529 loss_bbox: 117.9381 loss_dfl: 140.3362 2024/03/17 22:09:03 - mmengine - INFO - Epoch(train) [29][400/925] lr: 1.3317e-04 eta: 8:40:33 time: 0.6458 data_time: 0.0025 memory: 11652 grad_norm: 726.0369 loss: 387.9610 loss_cls: 128.1959 loss_bbox: 120.1686 loss_dfl: 139.5966 2024/03/17 22:09:36 - mmengine - INFO - Epoch(train) [29][450/925] lr: 1.3317e-04 eta: 8:40:01 time: 0.6606 data_time: 0.0025 memory: 11652 grad_norm: 677.8205 loss: 387.5559 loss_cls: 130.0284 loss_bbox: 119.2346 loss_dfl: 138.2930 2024/03/17 22:10:08 - mmengine - INFO - Epoch(train) [29][500/925] lr: 1.3317e-04 eta: 8:39:28 time: 0.6457 data_time: 0.0027 memory: 11545 grad_norm: 659.5880 loss: 384.6951 loss_cls: 128.8388 loss_bbox: 117.2608 loss_dfl: 138.5956 2024/03/17 22:10:41 - mmengine - INFO - Epoch(train) [29][550/925] lr: 1.3317e-04 eta: 8:38:54 time: 0.6447 data_time: 0.0027 memory: 11359 grad_norm: 623.3372 loss: 390.0323 loss_cls: 130.5741 loss_bbox: 119.4985 loss_dfl: 139.9597 2024/03/17 22:11:13 - mmengine - INFO - Epoch(train) [29][600/925] lr: 1.3317e-04 eta: 8:38:20 time: 0.6450 data_time: 0.0025 memory: 11665 grad_norm: 675.2259 loss: 389.8945 loss_cls: 131.5530 loss_bbox: 118.8911 loss_dfl: 139.4505 2024/03/17 22:11:45 - mmengine - INFO - Epoch(train) [29][650/925] lr: 1.3317e-04 eta: 8:37:46 time: 0.6399 data_time: 0.0025 memory: 11212 grad_norm: 681.6887 loss: 394.1317 loss_cls: 133.9694 loss_bbox: 119.9643 loss_dfl: 140.1980 2024/03/17 22:12:18 - mmengine - INFO - Epoch(train) [29][700/925] lr: 1.3317e-04 eta: 8:37:14 time: 0.6548 data_time: 0.0027 memory: 11359 grad_norm: 655.4025 loss: 386.3457 loss_cls: 129.6403 loss_bbox: 117.3979 loss_dfl: 139.3075 2024/03/17 22:12:51 - mmengine - INFO - Epoch(train) [29][750/925] lr: 1.3317e-04 eta: 8:36:41 time: 0.6584 data_time: 0.0027 memory: 11439 grad_norm: 623.4735 loss: 392.2841 loss_cls: 132.8540 loss_bbox: 119.0363 loss_dfl: 140.3939 2024/03/17 22:13:23 - mmengine - INFO - Epoch(train) [29][800/925] lr: 1.3317e-04 eta: 8:36:07 time: 0.6383 data_time: 0.0026 memory: 11639 grad_norm: 671.0254 loss: 388.9128 loss_cls: 129.5772 loss_bbox: 119.7442 loss_dfl: 139.5914 2024/03/17 22:13:56 - mmengine - INFO - Epoch(train) [29][850/925] lr: 1.3317e-04 eta: 8:35:35 time: 0.6612 data_time: 0.0027 memory: 11359 grad_norm: 674.0950 loss: 390.5311 loss_cls: 131.7059 loss_bbox: 119.6212 loss_dfl: 139.2040 2024/03/17 22:14:28 - mmengine - INFO - Epoch(train) [29][900/925] lr: 1.3317e-04 eta: 8:35:01 time: 0.6457 data_time: 0.0026 memory: 11999 grad_norm: 676.6040 loss: 388.7239 loss_cls: 131.3062 loss_bbox: 117.8440 loss_dfl: 139.5737 2024/03/17 22:14:43 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:15:20 - mmengine - INFO - Epoch(train) [30][ 50/925] lr: 1.3070e-04 eta: 8:34:16 time: 0.7211 data_time: 0.0662 memory: 11185 grad_norm: inf loss: 386.0528 loss_cls: 130.8180 loss_bbox: 115.9287 loss_dfl: 139.3061 2024/03/17 22:15:53 - mmengine - INFO - Epoch(train) [30][100/925] lr: 1.3070e-04 eta: 8:33:43 time: 0.6545 data_time: 0.0025 memory: 11439 grad_norm: 731.1356 loss: 392.1840 loss_cls: 131.4086 loss_bbox: 120.2170 loss_dfl: 140.5584 2024/03/17 22:16:26 - mmengine - INFO - Epoch(train) [30][150/925] lr: 1.3070e-04 eta: 8:33:12 time: 0.6656 data_time: 0.0027 memory: 11532 grad_norm: 653.6622 loss: 389.0437 loss_cls: 129.3882 loss_bbox: 120.2607 loss_dfl: 139.3949 2024/03/17 22:16:42 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:16:59 - mmengine - INFO - Epoch(train) [30][200/925] lr: 1.3070e-04 eta: 8:32:38 time: 0.6487 data_time: 0.0025 memory: 11225 grad_norm: 681.7919 loss: 390.7295 loss_cls: 130.8249 loss_bbox: 120.4838 loss_dfl: 139.4209 2024/03/17 22:17:31 - mmengine - INFO - Epoch(train) [30][250/925] lr: 1.3070e-04 eta: 8:32:06 time: 0.6556 data_time: 0.0026 memory: 11332 grad_norm: 686.1488 loss: 387.6024 loss_cls: 129.9591 loss_bbox: 117.8891 loss_dfl: 139.7542 2024/03/17 22:18:04 - mmengine - INFO - Epoch(train) [30][300/925] lr: 1.3070e-04 eta: 8:31:33 time: 0.6559 data_time: 0.0025 memory: 11505 grad_norm: 656.1296 loss: 382.7943 loss_cls: 126.2530 loss_bbox: 117.6889 loss_dfl: 138.8524 2024/03/17 22:18:36 - mmengine - INFO - Epoch(train) [30][350/925] lr: 1.3070e-04 eta: 8:30:59 time: 0.6457 data_time: 0.0026 memory: 11305 grad_norm: 698.1992 loss: 387.1390 loss_cls: 128.6280 loss_bbox: 118.8847 loss_dfl: 139.6263 2024/03/17 22:19:09 - mmengine - INFO - Epoch(train) [30][400/925] lr: 1.3070e-04 eta: 8:30:27 time: 0.6585 data_time: 0.0026 memory: 11292 grad_norm: 709.5348 loss: 391.7225 loss_cls: 129.4208 loss_bbox: 121.1890 loss_dfl: 141.1127 2024/03/17 22:19:42 - mmengine - INFO - Epoch(train) [30][450/925] lr: 1.3070e-04 eta: 8:29:54 time: 0.6486 data_time: 0.0025 memory: 11785 grad_norm: 674.4751 loss: 386.3578 loss_cls: 128.8284 loss_bbox: 118.1772 loss_dfl: 139.3521 2024/03/17 22:20:15 - mmengine - INFO - Epoch(train) [30][500/925] lr: 1.3070e-04 eta: 8:29:22 time: 0.6623 data_time: 0.0025 memory: 11385 grad_norm: 718.9979 loss: 391.1296 loss_cls: 130.2195 loss_bbox: 120.7114 loss_dfl: 140.1987 2024/03/17 22:20:47 - mmengine - INFO - Epoch(train) [30][550/925] lr: 1.3070e-04 eta: 8:28:48 time: 0.6458 data_time: 0.0026 memory: 11159 grad_norm: 720.3634 loss: 389.2640 loss_cls: 130.6394 loss_bbox: 120.3963 loss_dfl: 138.2283 2024/03/17 22:21:20 - mmengine - INFO - Epoch(train) [30][600/925] lr: 1.3070e-04 eta: 8:28:16 time: 0.6613 data_time: 0.0024 memory: 11945 grad_norm: 664.5677 loss: 390.9942 loss_cls: 131.9304 loss_bbox: 119.3916 loss_dfl: 139.6721 2024/03/17 22:21:53 - mmengine - INFO - Epoch(train) [30][650/925] lr: 1.3070e-04 eta: 8:27:43 time: 0.6547 data_time: 0.0025 memory: 11412 grad_norm: 672.6631 loss: 385.9478 loss_cls: 129.7421 loss_bbox: 118.2852 loss_dfl: 137.9204 2024/03/17 22:22:26 - mmengine - INFO - Epoch(train) [30][700/925] lr: 1.3070e-04 eta: 8:27:11 time: 0.6588 data_time: 0.0024 memory: 11625 grad_norm: 658.3298 loss: 384.1471 loss_cls: 126.9301 loss_bbox: 118.0029 loss_dfl: 139.2142 2024/03/17 22:22:59 - mmengine - INFO - Epoch(train) [30][750/925] lr: 1.3070e-04 eta: 8:26:39 time: 0.6644 data_time: 0.0025 memory: 11332 grad_norm: 686.8698 loss: 393.3349 loss_cls: 132.0544 loss_bbox: 119.8315 loss_dfl: 141.4490 2024/03/17 22:23:32 - mmengine - INFO - Epoch(train) [30][800/925] lr: 1.3070e-04 eta: 8:26:07 time: 0.6590 data_time: 0.0026 memory: 11452 grad_norm: 612.8666 loss: 392.1423 loss_cls: 130.6954 loss_bbox: 120.3386 loss_dfl: 141.1083 2024/03/17 22:24:05 - mmengine - INFO - Epoch(train) [30][850/925] lr: 1.3070e-04 eta: 8:25:33 time: 0.6474 data_time: 0.0025 memory: 11532 grad_norm: 697.6548 loss: 388.9712 loss_cls: 130.3686 loss_bbox: 119.5089 loss_dfl: 139.0937 2024/03/17 22:24:38 - mmengine - INFO - Epoch(train) [30][900/925] lr: 1.3070e-04 eta: 8:25:01 time: 0.6556 data_time: 0.0026 memory: 11332 grad_norm: 645.2647 loss: 387.5688 loss_cls: 129.7091 loss_bbox: 118.6471 loss_dfl: 139.2126 2024/03/17 22:24:54 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:24:54 - mmengine - INFO - Saving checkpoint at 30 epochs 2024/03/17 22:25:04 - mmengine - INFO - Epoch(val) [30][ 50/625] eta: 0:00:24 time: 0.0421 data_time: 0.0196 memory: 11425 2024/03/17 22:25:05 - mmengine - INFO - Epoch(val) [30][100/625] eta: 0:00:16 time: 0.0219 data_time: 0.0002 memory: 1709 2024/03/17 22:25:06 - mmengine - INFO - Epoch(val) [30][150/625] eta: 0:00:13 time: 0.0220 data_time: 0.0003 memory: 1709 2024/03/17 22:25:07 - mmengine - INFO - Epoch(val) [30][200/625] eta: 0:00:11 time: 0.0219 data_time: 0.0003 memory: 1709 2024/03/17 22:25:08 - mmengine - INFO - Epoch(val) [30][250/625] eta: 0:00:09 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/17 22:25:09 - mmengine - INFO - Epoch(val) [30][300/625] eta: 0:00:08 time: 0.0221 data_time: 0.0003 memory: 1709 2024/03/17 22:25:10 - mmengine - INFO - Epoch(val) [30][350/625] eta: 0:00:06 time: 0.0216 data_time: 0.0002 memory: 1709 2024/03/17 22:25:11 - mmengine - INFO - Epoch(val) [30][400/625] eta: 0:00:05 time: 0.0219 data_time: 0.0002 memory: 1709 2024/03/17 22:25:12 - mmengine - INFO - Epoch(val) [30][450/625] eta: 0:00:04 time: 0.0219 data_time: 0.0002 memory: 1709 2024/03/17 22:25:13 - mmengine - INFO - Epoch(val) [30][500/625] eta: 0:00:02 time: 0.0217 data_time: 0.0002 memory: 1709 2024/03/17 22:25:15 - mmengine - INFO - Epoch(val) [30][550/625] eta: 0:00:01 time: 0.0216 data_time: 0.0002 memory: 1709 2024/03/17 22:25:16 - mmengine - INFO - Epoch(val) [30][600/625] eta: 0:00:00 time: 0.0214 data_time: 0.0002 memory: 1709 2024/03/17 22:25:27 - mmengine - INFO - Evaluating bbox... 2024/03/17 22:26:37 - mmengine - INFO - bbox_mAP_copypaste: 0.523 0.689 0.569 0.347 0.573 0.677 2024/03/17 22:26:39 - mmengine - INFO - Epoch(val) [30][625/625] coco/bbox_mAP: 0.5230 coco/bbox_mAP_50: 0.6890 coco/bbox_mAP_75: 0.5690 coco/bbox_mAP_s: 0.3470 coco/bbox_mAP_m: 0.5730 coco/bbox_mAP_l: 0.6770 data_time: 0.0002 time: 0.0216 2024/03/17 22:27:14 - mmengine - INFO - Epoch(train) [31][ 50/925] lr: 1.2822e-04 eta: 8:24:15 time: 0.7016 data_time: 0.0639 memory: 11265 grad_norm: 692.6557 loss: 381.5177 loss_cls: 127.4347 loss_bbox: 115.5628 loss_dfl: 138.5203 2024/03/17 22:27:47 - mmengine - INFO - Epoch(train) [31][100/925] lr: 1.2822e-04 eta: 8:23:43 time: 0.6637 data_time: 0.0026 memory: 11865 grad_norm: 675.6372 loss: 388.8865 loss_cls: 130.2004 loss_bbox: 119.0048 loss_dfl: 139.6813 2024/03/17 22:28:20 - mmengine - INFO - Epoch(train) [31][150/925] lr: 1.2822e-04 eta: 8:23:10 time: 0.6564 data_time: 0.0026 memory: 11265 grad_norm: 637.9213 loss: 394.1267 loss_cls: 134.1372 loss_bbox: 119.7178 loss_dfl: 140.2717 2024/03/17 22:28:52 - mmengine - INFO - Epoch(train) [31][200/925] lr: 1.2822e-04 eta: 8:22:36 time: 0.6442 data_time: 0.0025 memory: 11385 grad_norm: 668.7294 loss: 381.6329 loss_cls: 126.8328 loss_bbox: 116.0048 loss_dfl: 138.7952 2024/03/17 22:29:26 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:29:26 - mmengine - INFO - Epoch(train) [31][250/925] lr: 1.2822e-04 eta: 8:22:04 time: 0.6643 data_time: 0.0027 memory: 11199 grad_norm: 714.4284 loss: 390.8448 loss_cls: 132.3537 loss_bbox: 118.7022 loss_dfl: 139.7888 2024/03/17 22:29:58 - mmengine - INFO - Epoch(train) [31][300/925] lr: 1.2822e-04 eta: 8:21:32 time: 0.6538 data_time: 0.0026 memory: 11185 grad_norm: 685.8634 loss: 384.1766 loss_cls: 127.7715 loss_bbox: 116.5897 loss_dfl: 139.8153 2024/03/17 22:30:31 - mmengine - INFO - Epoch(train) [31][350/925] lr: 1.2822e-04 eta: 8:20:59 time: 0.6531 data_time: 0.0027 memory: 11372 grad_norm: 662.9152 loss: 387.8606 loss_cls: 130.0752 loss_bbox: 118.4497 loss_dfl: 139.3357 2024/03/17 22:31:04 - mmengine - INFO - Epoch(train) [31][400/925] lr: 1.2822e-04 eta: 8:20:27 time: 0.6630 data_time: 0.0026 memory: 11452 grad_norm: 723.5305 loss: 391.3802 loss_cls: 131.4079 loss_bbox: 119.7703 loss_dfl: 140.2020 2024/03/17 22:31:37 - mmengine - INFO - Epoch(train) [31][450/925] lr: 1.2822e-04 eta: 8:19:54 time: 0.6525 data_time: 0.0026 memory: 11279 grad_norm: 724.5705 loss: 384.4250 loss_cls: 127.6821 loss_bbox: 117.4973 loss_dfl: 139.2456 2024/03/17 22:32:10 - mmengine - INFO - Epoch(train) [31][500/925] lr: 1.2822e-04 eta: 8:19:22 time: 0.6640 data_time: 0.0026 memory: 11505 grad_norm: 733.5624 loss: 386.5147 loss_cls: 129.6379 loss_bbox: 117.8074 loss_dfl: 139.0693 2024/03/17 22:32:43 - mmengine - INFO - Epoch(train) [31][550/925] lr: 1.2822e-04 eta: 8:18:48 time: 0.6485 data_time: 0.0026 memory: 11265 grad_norm: 656.7694 loss: 397.7961 loss_cls: 134.1055 loss_bbox: 122.2408 loss_dfl: 141.4498 2024/03/17 22:33:16 - mmengine - INFO - Epoch(train) [31][600/925] lr: 1.2822e-04 eta: 8:18:16 time: 0.6602 data_time: 0.0026 memory: 11799 grad_norm: 707.1654 loss: 387.4922 loss_cls: 128.9754 loss_bbox: 118.6781 loss_dfl: 139.8387 2024/03/17 22:33:49 - mmengine - INFO - Epoch(train) [31][650/925] lr: 1.2822e-04 eta: 8:17:44 time: 0.6613 data_time: 0.0024 memory: 11585 grad_norm: 663.0150 loss: 383.3061 loss_cls: 127.4877 loss_bbox: 116.5621 loss_dfl: 139.2563 2024/03/17 22:34:21 - mmengine - INFO - Epoch(train) [31][700/925] lr: 1.2822e-04 eta: 8:17:10 time: 0.6396 data_time: 0.0026 memory: 11252 grad_norm: 662.8191 loss: 384.7963 loss_cls: 127.9037 loss_bbox: 117.5070 loss_dfl: 139.3857 2024/03/17 22:34:54 - mmengine - INFO - Epoch(train) [31][750/925] lr: 1.2822e-04 eta: 8:16:38 time: 0.6633 data_time: 0.0025 memory: 11425 grad_norm: 667.6888 loss: 394.0569 loss_cls: 132.9588 loss_bbox: 120.6498 loss_dfl: 140.4483 2024/03/17 22:35:27 - mmengine - INFO - Epoch(train) [31][800/925] lr: 1.2822e-04 eta: 8:16:05 time: 0.6573 data_time: 0.0026 memory: 11399 grad_norm: 681.6692 loss: 391.2197 loss_cls: 131.2396 loss_bbox: 120.6553 loss_dfl: 139.3248 2024/03/17 22:35:59 - mmengine - INFO - Epoch(train) [31][850/925] lr: 1.2822e-04 eta: 8:15:32 time: 0.6443 data_time: 0.0026 memory: 11145 grad_norm: 659.2524 loss: 383.3936 loss_cls: 127.2113 loss_bbox: 116.5649 loss_dfl: 139.6174 2024/03/17 22:36:32 - mmengine - INFO - Epoch(train) [31][900/925] lr: 1.2822e-04 eta: 8:15:00 time: 0.6623 data_time: 0.0027 memory: 11279 grad_norm: 685.9354 loss: 387.5428 loss_cls: 128.9928 loss_bbox: 119.7730 loss_dfl: 138.7770 2024/03/17 22:36:48 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:37:24 - mmengine - INFO - Epoch(train) [32][ 50/925] lr: 1.2575e-04 eta: 8:14:14 time: 0.7151 data_time: 0.0649 memory: 11385 grad_norm: 671.3795 loss: 380.2653 loss_cls: 125.2229 loss_bbox: 116.8273 loss_dfl: 138.2150 2024/03/17 22:37:56 - mmengine - INFO - Epoch(train) [32][100/925] lr: 1.2575e-04 eta: 8:13:40 time: 0.6447 data_time: 0.0025 memory: 11519 grad_norm: 702.5479 loss: 392.4697 loss_cls: 131.2359 loss_bbox: 120.7050 loss_dfl: 140.5289 2024/03/17 22:38:29 - mmengine - INFO - Epoch(train) [32][150/925] lr: 1.2575e-04 eta: 8:13:07 time: 0.6484 data_time: 0.0025 memory: 11785 grad_norm: 737.1417 loss: 389.7923 loss_cls: 130.2514 loss_bbox: 118.9865 loss_dfl: 140.5544 2024/03/17 22:39:02 - mmengine - INFO - Epoch(train) [32][200/925] lr: 1.2575e-04 eta: 8:12:35 time: 0.6640 data_time: 0.0024 memory: 11385 grad_norm: inf loss: 389.6582 loss_cls: 129.9504 loss_bbox: 119.0912 loss_dfl: 140.6165 2024/03/17 22:39:34 - mmengine - INFO - Epoch(train) [32][250/925] lr: 1.2575e-04 eta: 8:12:02 time: 0.6440 data_time: 0.0026 memory: 11239 grad_norm: 699.4667 loss: 384.7765 loss_cls: 127.2822 loss_bbox: 118.6837 loss_dfl: 138.8106 2024/03/17 22:40:07 - mmengine - INFO - Epoch(train) [32][300/925] lr: 1.2575e-04 eta: 8:11:28 time: 0.6485 data_time: 0.0024 memory: 11279 grad_norm: 751.6835 loss: 376.7868 loss_cls: 123.2326 loss_bbox: 115.7342 loss_dfl: 137.8200 2024/03/17 22:40:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:40:40 - mmengine - INFO - Epoch(train) [32][350/925] lr: 1.2575e-04 eta: 8:10:56 time: 0.6561 data_time: 0.0025 memory: 11559 grad_norm: 719.1861 loss: 388.0325 loss_cls: 130.8084 loss_bbox: 117.6368 loss_dfl: 139.5873 2024/03/17 22:41:12 - mmengine - INFO - Epoch(train) [32][400/925] lr: 1.2575e-04 eta: 8:10:23 time: 0.6512 data_time: 0.0025 memory: 11439 grad_norm: 637.7576 loss: 389.9080 loss_cls: 131.6495 loss_bbox: 118.8450 loss_dfl: 139.4134 2024/03/17 22:41:45 - mmengine - INFO - Epoch(train) [32][450/925] lr: 1.2575e-04 eta: 8:09:50 time: 0.6525 data_time: 0.0025 memory: 11465 grad_norm: 682.3075 loss: 388.2071 loss_cls: 130.2740 loss_bbox: 118.0092 loss_dfl: 139.9239 2024/03/17 22:42:17 - mmengine - INFO - Epoch(train) [32][500/925] lr: 1.2575e-04 eta: 8:09:16 time: 0.6418 data_time: 0.0025 memory: 11359 grad_norm: 698.3350 loss: 381.0620 loss_cls: 125.7658 loss_bbox: 117.1432 loss_dfl: 138.1530 2024/03/17 22:42:50 - mmengine - INFO - Epoch(train) [32][550/925] lr: 1.2575e-04 eta: 8:08:44 time: 0.6604 data_time: 0.0027 memory: 11425 grad_norm: 672.3910 loss: 385.3006 loss_cls: 128.6660 loss_bbox: 117.5254 loss_dfl: 139.1091 2024/03/17 22:43:22 - mmengine - INFO - Epoch(train) [32][600/925] lr: 1.2575e-04 eta: 8:08:09 time: 0.6369 data_time: 0.0026 memory: 11439 grad_norm: 719.9390 loss: 390.9228 loss_cls: 132.8585 loss_bbox: 118.6252 loss_dfl: 139.4391 2024/03/17 22:43:55 - mmengine - INFO - Epoch(train) [32][650/925] lr: 1.2575e-04 eta: 8:07:37 time: 0.6545 data_time: 0.0025 memory: 11665 grad_norm: 667.6071 loss: 385.6053 loss_cls: 128.6695 loss_bbox: 118.1548 loss_dfl: 138.7810 2024/03/17 22:44:28 - mmengine - INFO - Epoch(train) [32][700/925] lr: 1.2575e-04 eta: 8:07:04 time: 0.6585 data_time: 0.0026 memory: 11345 grad_norm: 707.8542 loss: 384.8749 loss_cls: 127.8132 loss_bbox: 118.3806 loss_dfl: 138.6811 2024/03/17 22:44:59 - mmengine - INFO - Epoch(train) [32][750/925] lr: 1.2575e-04 eta: 8:06:30 time: 0.6380 data_time: 0.0025 memory: 11172 grad_norm: 680.5005 loss: 392.3088 loss_cls: 131.9872 loss_bbox: 120.1919 loss_dfl: 140.1297 2024/03/17 22:45:32 - mmengine - INFO - Epoch(train) [32][800/925] lr: 1.2575e-04 eta: 8:05:58 time: 0.6568 data_time: 0.0026 memory: 11452 grad_norm: 695.2261 loss: 385.8353 loss_cls: 128.6531 loss_bbox: 116.5465 loss_dfl: 140.6357 2024/03/17 22:46:05 - mmengine - INFO - Epoch(train) [32][850/925] lr: 1.2575e-04 eta: 8:05:24 time: 0.6504 data_time: 0.0025 memory: 11945 grad_norm: 690.5910 loss: 389.1692 loss_cls: 131.1295 loss_bbox: 117.6578 loss_dfl: 140.3819 2024/03/17 22:46:37 - mmengine - INFO - Epoch(train) [32][900/925] lr: 1.2575e-04 eta: 8:04:51 time: 0.6437 data_time: 0.0025 memory: 11372 grad_norm: 720.1447 loss: 382.2905 loss_cls: 127.4811 loss_bbox: 116.5402 loss_dfl: 138.2692 2024/03/17 22:46:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:47:30 - mmengine - INFO - Epoch(train) [33][ 50/925] lr: 1.2328e-04 eta: 8:04:07 time: 0.7263 data_time: 0.0621 memory: 11239 grad_norm: 736.2993 loss: 383.4775 loss_cls: 127.1224 loss_bbox: 117.7402 loss_dfl: 138.6149 2024/03/17 22:48:03 - mmengine - INFO - Epoch(train) [33][100/925] lr: 1.2328e-04 eta: 8:03:34 time: 0.6594 data_time: 0.0027 memory: 11252 grad_norm: 758.4461 loss: 387.0073 loss_cls: 129.7091 loss_bbox: 118.2799 loss_dfl: 139.0183 2024/03/17 22:48:36 - mmengine - INFO - Epoch(train) [33][150/925] lr: 1.2328e-04 eta: 8:03:01 time: 0.6505 data_time: 0.0028 memory: 11559 grad_norm: 670.4874 loss: 391.9234 loss_cls: 131.5790 loss_bbox: 120.4956 loss_dfl: 139.8487 2024/03/17 22:49:10 - mmengine - INFO - Epoch(train) [33][200/925] lr: 1.2328e-04 eta: 8:02:30 time: 0.6767 data_time: 0.0027 memory: 11679 grad_norm: 700.7277 loss: 384.1274 loss_cls: 128.6625 loss_bbox: 116.9618 loss_dfl: 138.5031 2024/03/17 22:49:43 - mmengine - INFO - Epoch(train) [33][250/925] lr: 1.2328e-04 eta: 8:01:58 time: 0.6624 data_time: 0.0028 memory: 11319 grad_norm: 731.3177 loss: 383.3374 loss_cls: 128.7496 loss_bbox: 116.3399 loss_dfl: 138.2478 2024/03/17 22:50:16 - mmengine - INFO - Epoch(train) [33][300/925] lr: 1.2328e-04 eta: 8:01:26 time: 0.6621 data_time: 0.0027 memory: 11639 grad_norm: 671.0675 loss: 380.7459 loss_cls: 125.7900 loss_bbox: 116.0244 loss_dfl: 138.9314 2024/03/17 22:50:49 - mmengine - INFO - Epoch(train) [33][350/925] lr: 1.2328e-04 eta: 8:00:54 time: 0.6673 data_time: 0.0027 memory: 11479 grad_norm: 650.1833 loss: 387.8144 loss_cls: 128.8022 loss_bbox: 118.7090 loss_dfl: 140.3032 2024/03/17 22:51:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:51:23 - mmengine - INFO - Epoch(train) [33][400/925] lr: 1.2328e-04 eta: 8:00:22 time: 0.6690 data_time: 0.0026 memory: 11345 grad_norm: 762.1955 loss: 386.6273 loss_cls: 130.9625 loss_bbox: 116.2266 loss_dfl: 139.4382 2024/03/17 22:51:56 - mmengine - INFO - Epoch(train) [33][450/925] lr: 1.2328e-04 eta: 7:59:50 time: 0.6602 data_time: 0.0027 memory: 11759 grad_norm: 629.9368 loss: 385.0856 loss_cls: 128.6506 loss_bbox: 117.0878 loss_dfl: 139.3472 2024/03/17 22:52:29 - mmengine - INFO - Epoch(train) [33][500/925] lr: 1.2328e-04 eta: 7:59:18 time: 0.6636 data_time: 0.0028 memory: 11159 grad_norm: 735.6199 loss: 385.0633 loss_cls: 128.9211 loss_bbox: 116.5141 loss_dfl: 139.6281 2024/03/17 22:53:02 - mmengine - INFO - Epoch(train) [33][550/925] lr: 1.2328e-04 eta: 7:58:46 time: 0.6662 data_time: 0.0027 memory: 11519 grad_norm: 713.6840 loss: 388.9351 loss_cls: 129.8507 loss_bbox: 119.0054 loss_dfl: 140.0791 2024/03/17 22:53:36 - mmengine - INFO - Epoch(train) [33][600/925] lr: 1.2328e-04 eta: 7:58:14 time: 0.6730 data_time: 0.0027 memory: 11359 grad_norm: 667.4600 loss: 385.2153 loss_cls: 128.7559 loss_bbox: 118.6901 loss_dfl: 137.7694 2024/03/17 22:54:09 - mmengine - INFO - Epoch(train) [33][650/925] lr: 1.2328e-04 eta: 7:57:41 time: 0.6524 data_time: 0.0026 memory: 11345 grad_norm: 672.8294 loss: 383.5768 loss_cls: 126.8712 loss_bbox: 118.5114 loss_dfl: 138.1943 2024/03/17 22:54:42 - mmengine - INFO - Epoch(train) [33][700/925] lr: 1.2328e-04 eta: 7:57:10 time: 0.6685 data_time: 0.0024 memory: 11372 grad_norm: 750.1024 loss: 387.9671 loss_cls: 129.5361 loss_bbox: 118.2171 loss_dfl: 140.2139 2024/03/17 22:55:15 - mmengine - INFO - Epoch(train) [33][750/925] lr: 1.2328e-04 eta: 7:56:38 time: 0.6678 data_time: 0.0025 memory: 11305 grad_norm: 724.7452 loss: 390.5358 loss_cls: 131.2673 loss_bbox: 119.3279 loss_dfl: 139.9407 2024/03/17 22:55:48 - mmengine - INFO - Epoch(train) [33][800/925] lr: 1.2328e-04 eta: 7:56:05 time: 0.6525 data_time: 0.0027 memory: 11492 grad_norm: 678.9679 loss: 384.4630 loss_cls: 126.0291 loss_bbox: 119.8130 loss_dfl: 138.6210 2024/03/17 22:56:21 - mmengine - INFO - Epoch(train) [33][850/925] lr: 1.2328e-04 eta: 7:55:33 time: 0.6646 data_time: 0.0028 memory: 11265 grad_norm: 736.9336 loss: 381.5960 loss_cls: 127.4765 loss_bbox: 115.7089 loss_dfl: 138.4106 2024/03/17 22:56:55 - mmengine - INFO - Epoch(train) [33][900/925] lr: 1.2328e-04 eta: 7:55:01 time: 0.6690 data_time: 0.0029 memory: 11452 grad_norm: 640.5558 loss: 385.4681 loss_cls: 128.0982 loss_bbox: 118.5281 loss_dfl: 138.8418 2024/03/17 22:57:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 22:57:48 - mmengine - INFO - Epoch(train) [34][ 50/925] lr: 1.2080e-04 eta: 7:54:16 time: 0.7209 data_time: 0.0530 memory: 11932 grad_norm: 662.8418 loss: 386.9235 loss_cls: 129.1400 loss_bbox: 118.4556 loss_dfl: 139.3279 2024/03/17 22:58:22 - mmengine - INFO - Epoch(train) [34][100/925] lr: 1.2080e-04 eta: 7:53:45 time: 0.6790 data_time: 0.0026 memory: 11505 grad_norm: 651.7926 loss: 384.1843 loss_cls: 127.7174 loss_bbox: 117.8964 loss_dfl: 138.5705 2024/03/17 22:58:55 - mmengine - INFO - Epoch(train) [34][150/925] lr: 1.2080e-04 eta: 7:53:14 time: 0.6772 data_time: 0.0024 memory: 11225 grad_norm: 674.7327 loss: 382.3056 loss_cls: 126.4548 loss_bbox: 117.2704 loss_dfl: 138.5804 2024/03/17 22:59:28 - mmengine - INFO - Epoch(train) [34][200/925] lr: 1.2080e-04 eta: 7:52:41 time: 0.6550 data_time: 0.0026 memory: 11359 grad_norm: 704.6722 loss: 383.2998 loss_cls: 128.8991 loss_bbox: 115.9099 loss_dfl: 138.4909 2024/03/17 23:00:02 - mmengine - INFO - Epoch(train) [34][250/925] lr: 1.2080e-04 eta: 7:52:09 time: 0.6729 data_time: 0.0026 memory: 11465 grad_norm: 679.4977 loss: 386.4855 loss_cls: 129.2872 loss_bbox: 118.7767 loss_dfl: 138.4216 2024/03/17 23:00:36 - mmengine - INFO - Epoch(train) [34][300/925] lr: 1.2080e-04 eta: 7:51:38 time: 0.6783 data_time: 0.0028 memory: 11505 grad_norm: 674.6046 loss: 381.5854 loss_cls: 126.2390 loss_bbox: 116.4914 loss_dfl: 138.8550 2024/03/17 23:01:09 - mmengine - INFO - Epoch(train) [34][350/925] lr: 1.2080e-04 eta: 7:51:06 time: 0.6602 data_time: 0.0026 memory: 11359 grad_norm: 701.1665 loss: 381.8127 loss_cls: 126.1560 loss_bbox: 117.7723 loss_dfl: 137.8845 2024/03/17 23:01:42 - mmengine - INFO - Epoch(train) [34][400/925] lr: 1.2080e-04 eta: 7:50:34 time: 0.6723 data_time: 0.0025 memory: 11505 grad_norm: 697.2039 loss: 381.5784 loss_cls: 126.3758 loss_bbox: 117.2247 loss_dfl: 137.9779 2024/03/17 23:02:16 - mmengine - INFO - Epoch(train) [34][450/925] lr: 1.2080e-04 eta: 7:50:03 time: 0.6727 data_time: 0.0025 memory: 11212 grad_norm: 703.9648 loss: 383.0948 loss_cls: 127.6709 loss_bbox: 115.7864 loss_dfl: 139.6376 2024/03/17 23:02:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:02:49 - mmengine - INFO - Epoch(train) [34][500/925] lr: 1.2080e-04 eta: 7:49:31 time: 0.6641 data_time: 0.0025 memory: 11319 grad_norm: 700.6674 loss: 387.9764 loss_cls: 129.4503 loss_bbox: 119.0809 loss_dfl: 139.4451 2024/03/17 23:03:23 - mmengine - INFO - Epoch(train) [34][550/925] lr: 1.2080e-04 eta: 7:48:59 time: 0.6731 data_time: 0.0027 memory: 11359 grad_norm: 736.5618 loss: 387.7592 loss_cls: 129.2371 loss_bbox: 119.6280 loss_dfl: 138.8941 2024/03/17 23:03:57 - mmengine - INFO - Epoch(train) [34][600/925] lr: 1.2080e-04 eta: 7:48:27 time: 0.6715 data_time: 0.0026 memory: 11545 grad_norm: 720.5546 loss: 386.1973 loss_cls: 128.7012 loss_bbox: 118.3677 loss_dfl: 139.1285 2024/03/17 23:04:30 - mmengine - INFO - Epoch(train) [34][650/925] lr: 1.2080e-04 eta: 7:47:55 time: 0.6676 data_time: 0.0025 memory: 11412 grad_norm: 652.7096 loss: 380.8266 loss_cls: 126.6639 loss_bbox: 116.0557 loss_dfl: 138.1069 2024/03/17 23:05:03 - mmengine - INFO - Epoch(train) [34][700/925] lr: 1.2080e-04 eta: 7:47:23 time: 0.6658 data_time: 0.0028 memory: 11172 grad_norm: 695.3805 loss: 387.6059 loss_cls: 129.6123 loss_bbox: 117.9677 loss_dfl: 140.0260 2024/03/17 23:05:37 - mmengine - INFO - Epoch(train) [34][750/925] lr: 1.2080e-04 eta: 7:46:51 time: 0.6685 data_time: 0.0026 memory: 11212 grad_norm: 667.0800 loss: 393.1463 loss_cls: 133.0451 loss_bbox: 119.6054 loss_dfl: 140.4958 2024/03/17 23:06:11 - mmengine - INFO - Epoch(train) [34][800/925] lr: 1.2080e-04 eta: 7:46:20 time: 0.6728 data_time: 0.0025 memory: 11559 grad_norm: 635.8874 loss: 386.5960 loss_cls: 129.2608 loss_bbox: 118.8709 loss_dfl: 138.4644 2024/03/17 23:06:44 - mmengine - INFO - Epoch(train) [34][850/925] lr: 1.2080e-04 eta: 7:45:47 time: 0.6636 data_time: 0.0026 memory: 11332 grad_norm: 700.6401 loss: 379.2580 loss_cls: 123.7903 loss_bbox: 117.3809 loss_dfl: 138.0868 2024/03/17 23:07:17 - mmengine - INFO - Epoch(train) [34][900/925] lr: 1.2080e-04 eta: 7:45:15 time: 0.6663 data_time: 0.0026 memory: 11399 grad_norm: 713.5874 loss: 381.7803 loss_cls: 126.5401 loss_bbox: 117.6322 loss_dfl: 137.6081 2024/03/17 23:07:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:08:12 - mmengine - INFO - Epoch(train) [35][ 50/925] lr: 1.1833e-04 eta: 7:44:33 time: 0.7450 data_time: 0.0680 memory: 11385 grad_norm: 723.8218 loss: 387.2200 loss_cls: 128.3764 loss_bbox: 118.6187 loss_dfl: 140.2249 2024/03/17 23:08:45 - mmengine - INFO - Epoch(train) [35][100/925] lr: 1.1833e-04 eta: 7:44:01 time: 0.6770 data_time: 0.0029 memory: 11705 grad_norm: 697.9747 loss: 384.3144 loss_cls: 126.5461 loss_bbox: 118.3531 loss_dfl: 139.4152 2024/03/17 23:09:19 - mmengine - INFO - Epoch(train) [35][150/925] lr: 1.1833e-04 eta: 7:43:29 time: 0.6663 data_time: 0.0028 memory: 11652 grad_norm: 647.8622 loss: 384.3469 loss_cls: 128.1118 loss_bbox: 117.3824 loss_dfl: 138.8527 2024/03/17 23:09:52 - mmengine - INFO - Epoch(train) [35][200/925] lr: 1.1833e-04 eta: 7:42:57 time: 0.6603 data_time: 0.0029 memory: 11465 grad_norm: 687.2084 loss: 379.1985 loss_cls: 126.0292 loss_bbox: 116.5178 loss_dfl: 136.6516 2024/03/17 23:10:25 - mmengine - INFO - Epoch(train) [35][250/925] lr: 1.1833e-04 eta: 7:42:24 time: 0.6629 data_time: 0.0028 memory: 11452 grad_norm: 707.5511 loss: 383.6838 loss_cls: 126.9176 loss_bbox: 117.5340 loss_dfl: 139.2322 2024/03/17 23:10:59 - mmengine - INFO - Epoch(train) [35][300/925] lr: 1.1833e-04 eta: 7:41:53 time: 0.6713 data_time: 0.0029 memory: 11185 grad_norm: 669.0398 loss: 383.4062 loss_cls: 127.6671 loss_bbox: 116.8009 loss_dfl: 138.9382 2024/03/17 23:11:32 - mmengine - INFO - Epoch(train) [35][350/925] lr: 1.1833e-04 eta: 7:41:20 time: 0.6649 data_time: 0.0031 memory: 11492 grad_norm: 693.1685 loss: 388.5592 loss_cls: 130.5686 loss_bbox: 119.0200 loss_dfl: 138.9707 2024/03/17 23:12:05 - mmengine - INFO - Epoch(train) [35][400/925] lr: 1.1833e-04 eta: 7:40:48 time: 0.6631 data_time: 0.0029 memory: 11385 grad_norm: 734.1501 loss: 386.5832 loss_cls: 129.4739 loss_bbox: 117.7819 loss_dfl: 139.3275 2024/03/17 23:12:39 - mmengine - INFO - Epoch(train) [35][450/925] lr: 1.1833e-04 eta: 7:40:17 time: 0.6809 data_time: 0.0031 memory: 11825 grad_norm: 659.0850 loss: 379.8508 loss_cls: 126.5880 loss_bbox: 115.5272 loss_dfl: 137.7355 2024/03/17 23:13:13 - mmengine - INFO - Epoch(train) [35][500/925] lr: 1.1833e-04 eta: 7:39:45 time: 0.6700 data_time: 0.0027 memory: 11292 grad_norm: 655.2117 loss: 384.9001 loss_cls: 127.3321 loss_bbox: 118.5607 loss_dfl: 139.0073 2024/03/17 23:13:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:13:46 - mmengine - INFO - Epoch(train) [35][550/925] lr: 1.1833e-04 eta: 7:39:12 time: 0.6590 data_time: 0.0028 memory: 11372 grad_norm: 748.3853 loss: 387.5550 loss_cls: 130.0547 loss_bbox: 119.2866 loss_dfl: 138.2138 2024/03/17 23:14:19 - mmengine - INFO - Epoch(train) [35][600/925] lr: 1.1833e-04 eta: 7:38:40 time: 0.6640 data_time: 0.0027 memory: 11425 grad_norm: 751.7095 loss: 383.1387 loss_cls: 126.5613 loss_bbox: 117.7029 loss_dfl: 138.8745 2024/03/17 23:14:53 - mmengine - INFO - Epoch(train) [35][650/925] lr: 1.1833e-04 eta: 7:38:09 time: 0.6763 data_time: 0.0025 memory: 11319 grad_norm: 665.3621 loss: 385.1204 loss_cls: 127.3610 loss_bbox: 118.4963 loss_dfl: 139.2631 2024/03/17 23:15:26 - mmengine - INFO - Epoch(train) [35][700/925] lr: 1.1833e-04 eta: 7:37:36 time: 0.6604 data_time: 0.0026 memory: 11599 grad_norm: 696.2471 loss: 383.4940 loss_cls: 127.8452 loss_bbox: 117.9291 loss_dfl: 137.7198 2024/03/17 23:15:59 - mmengine - INFO - Epoch(train) [35][750/925] lr: 1.1833e-04 eta: 7:37:04 time: 0.6654 data_time: 0.0029 memory: 11612 grad_norm: 703.6727 loss: 383.5590 loss_cls: 126.0740 loss_bbox: 118.7914 loss_dfl: 138.6936 2024/03/17 23:16:32 - mmengine - INFO - Epoch(train) [35][800/925] lr: 1.1833e-04 eta: 7:36:32 time: 0.6633 data_time: 0.0027 memory: 11199 grad_norm: 675.4916 loss: 387.7189 loss_cls: 128.8012 loss_bbox: 120.3146 loss_dfl: 138.6031 2024/03/17 23:17:06 - mmengine - INFO - Epoch(train) [35][850/925] lr: 1.1833e-04 eta: 7:36:00 time: 0.6674 data_time: 0.0029 memory: 11252 grad_norm: 717.5174 loss: 381.3647 loss_cls: 124.9537 loss_bbox: 117.6345 loss_dfl: 138.7765 2024/03/17 23:17:39 - mmengine - INFO - Epoch(train) [35][900/925] lr: 1.1833e-04 eta: 7:35:27 time: 0.6598 data_time: 0.0029 memory: 11465 grad_norm: 664.8205 loss: 384.1477 loss_cls: 127.0168 loss_bbox: 117.9825 loss_dfl: 139.1483 2024/03/17 23:17:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:17:55 - mmengine - INFO - Saving checkpoint at 35 epochs 2024/03/17 23:18:04 - mmengine - INFO - Epoch(val) [35][ 50/625] eta: 0:00:24 time: 0.0426 data_time: 0.0206 memory: 11185 2024/03/17 23:18:06 - mmengine - INFO - Epoch(val) [35][100/625] eta: 0:00:16 time: 0.0221 data_time: 0.0002 memory: 1709 2024/03/17 23:18:07 - mmengine - INFO - Epoch(val) [35][150/625] eta: 0:00:13 time: 0.0224 data_time: 0.0002 memory: 1709 2024/03/17 23:18:08 - mmengine - INFO - Epoch(val) [35][200/625] eta: 0:00:11 time: 0.0214 data_time: 0.0002 memory: 1709 2024/03/17 23:18:09 - mmengine - INFO - Epoch(val) [35][250/625] eta: 0:00:09 time: 0.0215 data_time: 0.0002 memory: 1709 2024/03/17 23:18:10 - mmengine - INFO - Epoch(val) [35][300/625] eta: 0:00:08 time: 0.0216 data_time: 0.0002 memory: 1709 2024/03/17 23:18:11 - mmengine - INFO - Epoch(val) [35][350/625] eta: 0:00:06 time: 0.0217 data_time: 0.0002 memory: 1709 2024/03/17 23:18:12 - mmengine - INFO - Epoch(val) [35][400/625] eta: 0:00:05 time: 0.0212 data_time: 0.0002 memory: 1709 2024/03/17 23:18:13 - mmengine - INFO - Epoch(val) [35][450/625] eta: 0:00:04 time: 0.0214 data_time: 0.0002 memory: 1709 2024/03/17 23:18:14 - mmengine - INFO - Epoch(val) [35][500/625] eta: 0:00:02 time: 0.0211 data_time: 0.0002 memory: 1709 2024/03/17 23:18:15 - mmengine - INFO - Epoch(val) [35][550/625] eta: 0:00:01 time: 0.0213 data_time: 0.0002 memory: 1709 2024/03/17 23:18:16 - mmengine - INFO - Epoch(val) [35][600/625] eta: 0:00:00 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/17 23:18:28 - mmengine - INFO - Evaluating bbox... 2024/03/17 23:19:40 - mmengine - INFO - bbox_mAP_copypaste: 0.525 0.692 0.572 0.350 0.575 0.680 2024/03/17 23:19:41 - mmengine - INFO - Epoch(val) [35][625/625] coco/bbox_mAP: 0.5250 coco/bbox_mAP_50: 0.6920 coco/bbox_mAP_75: 0.5720 coco/bbox_mAP_s: 0.3500 coco/bbox_mAP_m: 0.5750 coco/bbox_mAP_l: 0.6800 data_time: 0.0002 time: 0.0210 2024/03/17 23:20:18 - mmengine - INFO - Epoch(train) [36][ 50/925] lr: 1.1585e-04 eta: 7:34:42 time: 0.7257 data_time: 0.0594 memory: 11105 grad_norm: 724.1533 loss: 379.9211 loss_cls: 125.4936 loss_bbox: 116.2552 loss_dfl: 138.1723 2024/03/17 23:20:50 - mmengine - INFO - Epoch(train) [36][100/925] lr: 1.1585e-04 eta: 7:34:09 time: 0.6567 data_time: 0.0029 memory: 11425 grad_norm: 702.9402 loss: 378.7609 loss_cls: 124.4585 loss_bbox: 117.2723 loss_dfl: 137.0301 2024/03/17 23:21:24 - mmengine - INFO - Epoch(train) [36][150/925] lr: 1.1585e-04 eta: 7:33:38 time: 0.6791 data_time: 0.0028 memory: 11452 grad_norm: 625.7185 loss: 382.7167 loss_cls: 126.8940 loss_bbox: 117.1432 loss_dfl: 138.6795 2024/03/17 23:21:57 - mmengine - INFO - Epoch(train) [36][200/925] lr: 1.1585e-04 eta: 7:33:04 time: 0.6505 data_time: 0.0026 memory: 11425 grad_norm: 640.4926 loss: 384.0643 loss_cls: 128.9879 loss_bbox: 116.7436 loss_dfl: 138.3328 2024/03/17 23:22:30 - mmengine - INFO - Epoch(train) [36][250/925] lr: 1.1585e-04 eta: 7:32:32 time: 0.6609 data_time: 0.0027 memory: 11705 grad_norm: 660.5128 loss: 388.7218 loss_cls: 130.9579 loss_bbox: 118.8239 loss_dfl: 138.9400 2024/03/17 23:23:04 - mmengine - INFO - Epoch(train) [36][300/925] lr: 1.1585e-04 eta: 7:32:00 time: 0.6740 data_time: 0.0027 memory: 11225 grad_norm: 708.6118 loss: 378.6194 loss_cls: 124.9359 loss_bbox: 115.8977 loss_dfl: 137.7858 2024/03/17 23:23:37 - mmengine - INFO - Epoch(train) [36][350/925] lr: 1.1585e-04 eta: 7:31:27 time: 0.6552 data_time: 0.0026 memory: 11359 grad_norm: 694.1175 loss: 382.5743 loss_cls: 127.2789 loss_bbox: 117.4539 loss_dfl: 137.8416 2024/03/17 23:24:11 - mmengine - INFO - Epoch(train) [36][400/925] lr: 1.1585e-04 eta: 7:30:57 time: 0.6937 data_time: 0.0030 memory: 11239 grad_norm: 706.6665 loss: 380.3025 loss_cls: 127.0013 loss_bbox: 114.6526 loss_dfl: 138.6486 2024/03/17 23:24:46 - mmengine - INFO - Epoch(train) [36][450/925] lr: 1.1585e-04 eta: 7:30:26 time: 0.6830 data_time: 0.0028 memory: 11959 grad_norm: 724.8504 loss: 387.5857 loss_cls: 128.6553 loss_bbox: 119.4738 loss_dfl: 139.4566 2024/03/17 23:25:19 - mmengine - INFO - Epoch(train) [36][500/925] lr: 1.1585e-04 eta: 7:29:54 time: 0.6720 data_time: 0.0029 memory: 11292 grad_norm: 681.1579 loss: 385.6864 loss_cls: 127.6704 loss_bbox: 118.5120 loss_dfl: 139.5040 2024/03/17 23:25:53 - mmengine - INFO - Epoch(train) [36][550/925] lr: 1.1585e-04 eta: 7:29:23 time: 0.6796 data_time: 0.0028 memory: 11132 grad_norm: 677.7608 loss: 380.0753 loss_cls: 125.2476 loss_bbox: 117.0610 loss_dfl: 137.7667 2024/03/17 23:26:26 - mmengine - INFO - Epoch(train) [36][600/925] lr: 1.1585e-04 eta: 7:28:49 time: 0.6520 data_time: 0.0026 memory: 11399 grad_norm: 718.3431 loss: 391.8646 loss_cls: 132.7383 loss_bbox: 119.5863 loss_dfl: 139.5400 2024/03/17 23:26:43 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:27:00 - mmengine - INFO - Epoch(train) [36][650/925] lr: 1.1585e-04 eta: 7:28:18 time: 0.6765 data_time: 0.0024 memory: 11492 grad_norm: 697.0840 loss: 382.9226 loss_cls: 127.1095 loss_bbox: 116.6627 loss_dfl: 139.1505 2024/03/17 23:27:33 - mmengine - INFO - Epoch(train) [36][700/925] lr: 1.1585e-04 eta: 7:27:45 time: 0.6621 data_time: 0.0024 memory: 11265 grad_norm: 687.2954 loss: 374.8321 loss_cls: 123.4087 loss_bbox: 114.4296 loss_dfl: 136.9937 2024/03/17 23:28:06 - mmengine - INFO - Epoch(train) [36][750/925] lr: 1.1585e-04 eta: 7:27:13 time: 0.6685 data_time: 0.0029 memory: 11732 grad_norm: 687.7356 loss: 380.2233 loss_cls: 124.3957 loss_bbox: 116.7437 loss_dfl: 139.0839 2024/03/17 23:28:40 - mmengine - INFO - Epoch(train) [36][800/925] lr: 1.1585e-04 eta: 7:26:42 time: 0.6793 data_time: 0.0026 memory: 11532 grad_norm: 682.5356 loss: 378.7366 loss_cls: 124.9629 loss_bbox: 115.4122 loss_dfl: 138.3614 2024/03/17 23:29:14 - mmengine - INFO - Epoch(train) [36][850/925] lr: 1.1585e-04 eta: 7:26:10 time: 0.6743 data_time: 0.0029 memory: 11279 grad_norm: 656.1225 loss: 375.2509 loss_cls: 121.9138 loss_bbox: 115.3814 loss_dfl: 137.9557 2024/03/17 23:29:47 - mmengine - INFO - Epoch(train) [36][900/925] lr: 1.1585e-04 eta: 7:25:38 time: 0.6630 data_time: 0.0027 memory: 11265 grad_norm: 705.4288 loss: 382.6430 loss_cls: 127.4886 loss_bbox: 116.8549 loss_dfl: 138.2994 2024/03/17 23:30:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:30:41 - mmengine - INFO - Epoch(train) [37][ 50/925] lr: 1.1338e-04 eta: 7:24:54 time: 0.7438 data_time: 0.0727 memory: 11185 grad_norm: inf loss: 380.5491 loss_cls: 124.5921 loss_bbox: 116.9308 loss_dfl: 139.0262 2024/03/17 23:31:13 - mmengine - INFO - Epoch(train) [37][100/925] lr: 1.1338e-04 eta: 7:24:20 time: 0.6466 data_time: 0.0023 memory: 11452 grad_norm: 656.5272 loss: 381.5713 loss_cls: 125.7894 loss_bbox: 116.9126 loss_dfl: 138.8692 2024/03/17 23:31:47 - mmengine - INFO - Epoch(train) [37][150/925] lr: 1.1338e-04 eta: 7:23:48 time: 0.6762 data_time: 0.0030 memory: 11585 grad_norm: 701.3508 loss: 384.5195 loss_cls: 127.9355 loss_bbox: 116.8121 loss_dfl: 139.7719 2024/03/17 23:32:22 - mmengine - INFO - Epoch(train) [37][200/925] lr: 1.1338e-04 eta: 7:23:18 time: 0.6963 data_time: 0.0032 memory: 11359 grad_norm: 663.7470 loss: 382.4319 loss_cls: 126.1552 loss_bbox: 117.3003 loss_dfl: 138.9764 2024/03/17 23:32:56 - mmengine - INFO - Epoch(train) [37][250/925] lr: 1.1338e-04 eta: 7:22:46 time: 0.6751 data_time: 0.0031 memory: 11412 grad_norm: 740.1489 loss: 383.6376 loss_cls: 127.4657 loss_bbox: 117.6250 loss_dfl: 138.5468 2024/03/17 23:33:29 - mmengine - INFO - Epoch(train) [37][300/925] lr: 1.1338e-04 eta: 7:22:13 time: 0.6537 data_time: 0.0029 memory: 11545 grad_norm: 672.7943 loss: 378.9949 loss_cls: 126.0121 loss_bbox: 115.6789 loss_dfl: 137.3038 2024/03/17 23:34:03 - mmengine - INFO - Epoch(train) [37][350/925] lr: 1.1338e-04 eta: 7:21:42 time: 0.6847 data_time: 0.0029 memory: 11105 grad_norm: 757.2955 loss: 384.9355 loss_cls: 128.2321 loss_bbox: 117.6456 loss_dfl: 139.0578 2024/03/17 23:34:36 - mmengine - INFO - Epoch(train) [37][400/925] lr: 1.1338e-04 eta: 7:21:09 time: 0.6587 data_time: 0.0029 memory: 11159 grad_norm: 774.8666 loss: 379.7724 loss_cls: 125.3001 loss_bbox: 116.5656 loss_dfl: 137.9068 2024/03/17 23:35:09 - mmengine - INFO - Epoch(train) [37][450/925] lr: 1.1338e-04 eta: 7:20:37 time: 0.6587 data_time: 0.0026 memory: 11252 grad_norm: 630.4706 loss: 383.5550 loss_cls: 127.4952 loss_bbox: 116.8018 loss_dfl: 139.2579 2024/03/17 23:35:42 - mmengine - INFO - Epoch(train) [37][500/925] lr: 1.1338e-04 eta: 7:20:04 time: 0.6603 data_time: 0.0027 memory: 11465 grad_norm: 769.9445 loss: 378.3588 loss_cls: 124.7033 loss_bbox: 115.7406 loss_dfl: 137.9149 2024/03/17 23:36:15 - mmengine - INFO - Epoch(train) [37][550/925] lr: 1.1338e-04 eta: 7:19:32 time: 0.6671 data_time: 0.0026 memory: 11652 grad_norm: 723.9885 loss: 380.4865 loss_cls: 125.3441 loss_bbox: 116.2350 loss_dfl: 138.9074 2024/03/17 23:36:49 - mmengine - INFO - Epoch(train) [37][600/925] lr: 1.1338e-04 eta: 7:19:00 time: 0.6682 data_time: 0.0029 memory: 11292 grad_norm: 698.7113 loss: 380.1156 loss_cls: 125.1886 loss_bbox: 116.4453 loss_dfl: 138.4817 2024/03/17 23:37:22 - mmengine - INFO - Epoch(train) [37][650/925] lr: 1.1338e-04 eta: 7:18:27 time: 0.6599 data_time: 0.0029 memory: 11505 grad_norm: 644.5694 loss: 381.9711 loss_cls: 127.2703 loss_bbox: 116.4921 loss_dfl: 138.2087 2024/03/17 23:37:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:37:55 - mmengine - INFO - Epoch(train) [37][700/925] lr: 1.1338e-04 eta: 7:17:55 time: 0.6718 data_time: 0.0029 memory: 11159 grad_norm: 670.5474 loss: 381.5570 loss_cls: 126.4056 loss_bbox: 116.3737 loss_dfl: 138.7778 2024/03/17 23:38:29 - mmengine - INFO - Epoch(train) [37][750/925] lr: 1.1338e-04 eta: 7:17:22 time: 0.6622 data_time: 0.0027 memory: 11345 grad_norm: 697.9213 loss: 384.5413 loss_cls: 127.6197 loss_bbox: 119.0939 loss_dfl: 137.8277 2024/03/17 23:39:02 - mmengine - INFO - Epoch(train) [37][800/925] lr: 1.1338e-04 eta: 7:16:50 time: 0.6595 data_time: 0.0028 memory: 11785 grad_norm: 677.8573 loss: 388.2006 loss_cls: 130.1576 loss_bbox: 118.8564 loss_dfl: 139.1865 2024/03/17 23:39:34 - mmengine - INFO - Epoch(train) [37][850/925] lr: 1.1338e-04 eta: 7:16:17 time: 0.6515 data_time: 0.0027 memory: 11719 grad_norm: 670.2189 loss: 382.1382 loss_cls: 126.1927 loss_bbox: 118.0335 loss_dfl: 137.9120 2024/03/17 23:40:07 - mmengine - INFO - Epoch(train) [37][900/925] lr: 1.1338e-04 eta: 7:15:43 time: 0.6505 data_time: 0.0026 memory: 11465 grad_norm: 706.7256 loss: 380.8077 loss_cls: 125.6616 loss_bbox: 116.7233 loss_dfl: 138.4228 2024/03/17 23:40:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:40:59 - mmengine - INFO - Epoch(train) [38][ 50/925] lr: 1.1090e-04 eta: 7:14:57 time: 0.7130 data_time: 0.0672 memory: 11359 grad_norm: 697.1102 loss: 382.2319 loss_cls: 127.0547 loss_bbox: 117.4456 loss_dfl: 137.7316 2024/03/17 23:41:33 - mmengine - INFO - Epoch(train) [38][100/925] lr: 1.1090e-04 eta: 7:14:25 time: 0.6690 data_time: 0.0028 memory: 11332 grad_norm: 635.8462 loss: 377.0133 loss_cls: 123.4363 loss_bbox: 115.9140 loss_dfl: 137.6630 2024/03/17 23:42:06 - mmengine - INFO - Epoch(train) [38][150/925] lr: 1.1090e-04 eta: 7:13:53 time: 0.6708 data_time: 0.0030 memory: 11692 grad_norm: 673.2557 loss: 383.4089 loss_cls: 126.8123 loss_bbox: 117.5808 loss_dfl: 139.0158 2024/03/17 23:42:39 - mmengine - INFO - Epoch(train) [38][200/925] lr: 1.1090e-04 eta: 7:13:20 time: 0.6478 data_time: 0.0027 memory: 11332 grad_norm: 677.0032 loss: 383.6707 loss_cls: 126.7446 loss_bbox: 118.7174 loss_dfl: 138.2088 2024/03/17 23:43:12 - mmengine - INFO - Epoch(train) [38][250/925] lr: 1.1090e-04 eta: 7:12:47 time: 0.6645 data_time: 0.0027 memory: 11639 grad_norm: 681.4355 loss: 378.2004 loss_cls: 123.7103 loss_bbox: 116.8820 loss_dfl: 137.6082 2024/03/17 23:43:45 - mmengine - INFO - Epoch(train) [38][300/925] lr: 1.1090e-04 eta: 7:12:15 time: 0.6644 data_time: 0.0029 memory: 11399 grad_norm: 712.2457 loss: 378.4003 loss_cls: 124.2355 loss_bbox: 116.3355 loss_dfl: 137.8293 2024/03/17 23:44:17 - mmengine - INFO - Epoch(train) [38][350/925] lr: 1.1090e-04 eta: 7:11:41 time: 0.6381 data_time: 0.0026 memory: 11572 grad_norm: 665.7468 loss: 378.6498 loss_cls: 125.5150 loss_bbox: 115.2901 loss_dfl: 137.8446 2024/03/17 23:44:51 - mmengine - INFO - Epoch(train) [38][400/925] lr: 1.1090e-04 eta: 7:11:09 time: 0.6759 data_time: 0.0032 memory: 11572 grad_norm: 675.0925 loss: 385.9477 loss_cls: 127.2372 loss_bbox: 119.6724 loss_dfl: 139.0381 2024/03/17 23:45:25 - mmengine - INFO - Epoch(train) [38][450/925] lr: 1.1090e-04 eta: 7:10:37 time: 0.6804 data_time: 0.0031 memory: 11452 grad_norm: 713.7293 loss: 383.6905 loss_cls: 126.6674 loss_bbox: 119.1669 loss_dfl: 137.8562 2024/03/17 23:45:58 - mmengine - INFO - Epoch(train) [38][500/925] lr: 1.1090e-04 eta: 7:10:05 time: 0.6640 data_time: 0.0029 memory: 11545 grad_norm: 707.7772 loss: 379.2203 loss_cls: 124.8936 loss_bbox: 116.4459 loss_dfl: 137.8808 2024/03/17 23:46:32 - mmengine - INFO - Epoch(train) [38][550/925] lr: 1.1090e-04 eta: 7:09:33 time: 0.6707 data_time: 0.0030 memory: 11492 grad_norm: 718.5611 loss: 382.9699 loss_cls: 128.6567 loss_bbox: 115.9203 loss_dfl: 138.3929 2024/03/17 23:47:05 - mmengine - INFO - Epoch(train) [38][600/925] lr: 1.1090e-04 eta: 7:09:01 time: 0.6697 data_time: 0.0029 memory: 11519 grad_norm: 695.8021 loss: 382.0709 loss_cls: 126.1893 loss_bbox: 118.9665 loss_dfl: 136.9151 2024/03/17 23:47:38 - mmengine - INFO - Epoch(train) [38][650/925] lr: 1.1090e-04 eta: 7:08:28 time: 0.6505 data_time: 0.0026 memory: 11172 grad_norm: 664.9325 loss: 380.2843 loss_cls: 126.3233 loss_bbox: 116.7053 loss_dfl: 137.2557 2024/03/17 23:48:10 - mmengine - INFO - Epoch(train) [38][700/925] lr: 1.1090e-04 eta: 7:07:54 time: 0.6430 data_time: 0.0026 memory: 11265 grad_norm: 674.8505 loss: 381.4271 loss_cls: 124.8400 loss_bbox: 117.3621 loss_dfl: 139.2250 2024/03/17 23:48:43 - mmengine - INFO - Epoch(train) [38][750/925] lr: 1.1090e-04 eta: 7:07:22 time: 0.6657 data_time: 0.0028 memory: 11359 grad_norm: 654.6068 loss: 381.6617 loss_cls: 126.3031 loss_bbox: 117.0185 loss_dfl: 138.3401 2024/03/17 23:49:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:49:16 - mmengine - INFO - Epoch(train) [38][800/925] lr: 1.1090e-04 eta: 7:06:48 time: 0.6447 data_time: 0.0026 memory: 11225 grad_norm: 744.6444 loss: 374.3309 loss_cls: 122.9214 loss_bbox: 114.7580 loss_dfl: 136.6515 2024/03/17 23:49:49 - mmengine - INFO - Epoch(train) [38][850/925] lr: 1.1090e-04 eta: 7:06:15 time: 0.6613 data_time: 0.0028 memory: 11239 grad_norm: 680.4901 loss: 381.3798 loss_cls: 125.9759 loss_bbox: 116.7738 loss_dfl: 138.6301 2024/03/17 23:50:22 - mmengine - INFO - Epoch(train) [38][900/925] lr: 1.1090e-04 eta: 7:05:44 time: 0.6745 data_time: 0.0030 memory: 11372 grad_norm: 750.6878 loss: 371.6108 loss_cls: 120.9451 loss_bbox: 113.2006 loss_dfl: 137.4651 2024/03/17 23:50:38 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/17 23:51:15 - mmengine - INFO - Epoch(train) [39][ 50/925] lr: 1.0842e-04 eta: 7:04:57 time: 0.7196 data_time: 0.0578 memory: 11439 grad_norm: 679.4676 loss: 376.7147 loss_cls: 123.4347 loss_bbox: 116.1413 loss_dfl: 137.1387 2024/03/17 23:51:48 - mmengine - INFO - Epoch(train) [39][100/925] lr: 1.0842e-04 eta: 7:04:24 time: 0.6531 data_time: 0.0029 memory: 11385 grad_norm: 661.0825 loss: 381.3080 loss_cls: 123.6418 loss_bbox: 119.4592 loss_dfl: 138.2070 2024/03/17 23:52:20 - mmengine - INFO - Epoch(train) [39][150/925] lr: 1.0842e-04 eta: 7:03:51 time: 0.6533 data_time: 0.0027 memory: 11345 grad_norm: 679.1841 loss: 383.1048 loss_cls: 126.5119 loss_bbox: 118.1264 loss_dfl: 138.4665 2024/03/17 23:52:53 - mmengine - INFO - Epoch(train) [39][200/925] lr: 1.0842e-04 eta: 7:03:18 time: 0.6522 data_time: 0.0025 memory: 11399 grad_norm: 644.6924 loss: 372.9607 loss_cls: 120.8802 loss_bbox: 115.2645 loss_dfl: 136.8160 2024/03/17 23:53:26 - mmengine - INFO - Epoch(train) [39][250/925] lr: 1.0842e-04 eta: 7:02:45 time: 0.6493 data_time: 0.0025 memory: 11412 grad_norm: 672.7425 loss: 381.3551 loss_cls: 127.2856 loss_bbox: 115.6699 loss_dfl: 138.3996 2024/03/17 23:53:59 - mmengine - INFO - Epoch(train) [39][300/925] lr: 1.0842e-04 eta: 7:02:12 time: 0.6681 data_time: 0.0031 memory: 11425 grad_norm: 677.4866 loss: 378.3006 loss_cls: 122.7118 loss_bbox: 117.3847 loss_dfl: 138.2041 2024/03/17 23:54:31 - mmengine - INFO - Epoch(train) [39][350/925] lr: 1.0842e-04 eta: 7:01:39 time: 0.6486 data_time: 0.0027 memory: 11465 grad_norm: 705.4020 loss: 380.5239 loss_cls: 124.3118 loss_bbox: 117.5429 loss_dfl: 138.6692 2024/03/17 23:55:04 - mmengine - INFO - Epoch(train) [39][400/925] lr: 1.0842e-04 eta: 7:01:06 time: 0.6519 data_time: 0.0029 memory: 11279 grad_norm: 731.2781 loss: 384.0131 loss_cls: 126.4058 loss_bbox: 117.4410 loss_dfl: 140.1663 2024/03/17 23:55:38 - mmengine - INFO - Epoch(train) [39][450/925] lr: 1.0842e-04 eta: 7:00:34 time: 0.6728 data_time: 0.0029 memory: 11372 grad_norm: 722.7392 loss: 379.5280 loss_cls: 124.9381 loss_bbox: 116.0292 loss_dfl: 138.5607 2024/03/17 23:56:11 - mmengine - INFO - Epoch(train) [39][500/925] lr: 1.0842e-04 eta: 7:00:01 time: 0.6623 data_time: 0.0027 memory: 11519 grad_norm: 725.3054 loss: 381.6051 loss_cls: 125.2438 loss_bbox: 117.6723 loss_dfl: 138.6890 2024/03/17 23:56:44 - mmengine - INFO - Epoch(train) [39][550/925] lr: 1.0842e-04 eta: 6:59:28 time: 0.6561 data_time: 0.0026 memory: 11639 grad_norm: 711.0022 loss: 386.2837 loss_cls: 127.8566 loss_bbox: 118.7726 loss_dfl: 139.6546 2024/03/17 23:57:16 - mmengine - INFO - Epoch(train) [39][600/925] lr: 1.0842e-04 eta: 6:58:55 time: 0.6513 data_time: 0.0027 memory: 11372 grad_norm: 724.0823 loss: 379.2004 loss_cls: 124.4711 loss_bbox: 116.7417 loss_dfl: 137.9876 2024/03/17 23:57:49 - mmengine - INFO - Epoch(train) [39][650/925] lr: 1.0842e-04 eta: 6:58:22 time: 0.6531 data_time: 0.0027 memory: 11692 grad_norm: 686.4609 loss: 377.3626 loss_cls: 124.0097 loss_bbox: 115.7899 loss_dfl: 137.5630 2024/03/17 23:58:24 - mmengine - INFO - Epoch(train) [39][700/925] lr: 1.0842e-04 eta: 6:57:51 time: 0.6893 data_time: 0.0031 memory: 11345 grad_norm: 680.7911 loss: 378.6367 loss_cls: 125.0480 loss_bbox: 115.8461 loss_dfl: 137.7427 2024/03/17 23:58:57 - mmengine - INFO - Epoch(train) [39][750/925] lr: 1.0842e-04 eta: 6:57:18 time: 0.6642 data_time: 0.0032 memory: 11199 grad_norm: inf loss: 386.8790 loss_cls: 128.8314 loss_bbox: 119.4280 loss_dfl: 138.6196 2024/03/17 23:59:30 - mmengine - INFO - Epoch(train) [39][800/925] lr: 1.0842e-04 eta: 6:56:46 time: 0.6668 data_time: 0.0030 memory: 11212 grad_norm: 720.9496 loss: 375.5561 loss_cls: 122.3668 loss_bbox: 115.3643 loss_dfl: 137.8251 2024/03/18 00:00:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:00:04 - mmengine - INFO - Epoch(train) [39][850/925] lr: 1.0842e-04 eta: 6:56:14 time: 0.6736 data_time: 0.0030 memory: 11412 grad_norm: 731.4895 loss: 382.4085 loss_cls: 126.4192 loss_bbox: 117.3724 loss_dfl: 138.6169 2024/03/18 00:00:36 - mmengine - INFO - Epoch(train) [39][900/925] lr: 1.0842e-04 eta: 6:55:40 time: 0.6356 data_time: 0.0024 memory: 11385 grad_norm: 684.7592 loss: 376.9548 loss_cls: 122.8054 loss_bbox: 116.5304 loss_dfl: 137.6189 2024/03/18 00:00:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:01:28 - mmengine - INFO - Epoch(train) [40][ 50/925] lr: 1.0595e-04 eta: 6:54:54 time: 0.7154 data_time: 0.0591 memory: 11665 grad_norm: 670.7349 loss: 376.2039 loss_cls: 124.0350 loss_bbox: 115.4183 loss_dfl: 136.7507 2024/03/18 00:02:02 - mmengine - INFO - Epoch(train) [40][100/925] lr: 1.0595e-04 eta: 6:54:22 time: 0.6675 data_time: 0.0027 memory: 11239 grad_norm: 657.7337 loss: 381.1986 loss_cls: 125.7520 loss_bbox: 116.9731 loss_dfl: 138.4734 2024/03/18 00:02:34 - mmengine - INFO - Epoch(train) [40][150/925] lr: 1.0595e-04 eta: 6:53:48 time: 0.6419 data_time: 0.0027 memory: 11225 grad_norm: 708.1096 loss: 379.8606 loss_cls: 125.5553 loss_bbox: 115.6657 loss_dfl: 138.6396 2024/03/18 00:03:08 - mmengine - INFO - Epoch(train) [40][200/925] lr: 1.0595e-04 eta: 6:53:16 time: 0.6726 data_time: 0.0027 memory: 11412 grad_norm: 696.4948 loss: 380.4974 loss_cls: 124.0660 loss_bbox: 116.8932 loss_dfl: 139.5382 2024/03/18 00:03:42 - mmengine - INFO - Epoch(train) [40][250/925] lr: 1.0595e-04 eta: 6:52:45 time: 0.6881 data_time: 0.0029 memory: 11505 grad_norm: 729.4700 loss: 377.9013 loss_cls: 124.6877 loss_bbox: 115.2667 loss_dfl: 137.9469 2024/03/18 00:04:15 - mmengine - INFO - Epoch(train) [40][300/925] lr: 1.0595e-04 eta: 6:52:12 time: 0.6629 data_time: 0.0028 memory: 11492 grad_norm: 696.5840 loss: 376.5776 loss_cls: 123.3886 loss_bbox: 116.3113 loss_dfl: 136.8777 2024/03/18 00:04:48 - mmengine - INFO - Epoch(train) [40][350/925] lr: 1.0595e-04 eta: 6:51:39 time: 0.6582 data_time: 0.0027 memory: 11305 grad_norm: 688.1795 loss: 388.0055 loss_cls: 129.7701 loss_bbox: 118.3952 loss_dfl: 139.8403 2024/03/18 00:05:21 - mmengine - INFO - Epoch(train) [40][400/925] lr: 1.0595e-04 eta: 6:51:07 time: 0.6612 data_time: 0.0026 memory: 11452 grad_norm: 717.3771 loss: 386.9328 loss_cls: 128.0373 loss_bbox: 118.9836 loss_dfl: 139.9119 2024/03/18 00:05:54 - mmengine - INFO - Epoch(train) [40][450/925] lr: 1.0595e-04 eta: 6:50:33 time: 0.6526 data_time: 0.0025 memory: 11465 grad_norm: 681.8039 loss: 381.8387 loss_cls: 126.3551 loss_bbox: 117.3220 loss_dfl: 138.1616 2024/03/18 00:06:27 - mmengine - INFO - Epoch(train) [40][500/925] lr: 1.0595e-04 eta: 6:50:01 time: 0.6566 data_time: 0.0026 memory: 11519 grad_norm: 732.2243 loss: 379.8611 loss_cls: 123.3694 loss_bbox: 117.6912 loss_dfl: 138.8005 2024/03/18 00:07:00 - mmengine - INFO - Epoch(train) [40][550/925] lr: 1.0595e-04 eta: 6:49:28 time: 0.6663 data_time: 0.0026 memory: 11225 grad_norm: 657.4551 loss: 378.8094 loss_cls: 123.8220 loss_bbox: 117.3053 loss_dfl: 137.6820 2024/03/18 00:07:33 - mmengine - INFO - Epoch(train) [40][600/925] lr: 1.0595e-04 eta: 6:48:55 time: 0.6544 data_time: 0.0028 memory: 11359 grad_norm: 672.3620 loss: 383.1547 loss_cls: 127.4803 loss_bbox: 117.0199 loss_dfl: 138.6545 2024/03/18 00:08:05 - mmengine - INFO - Epoch(train) [40][650/925] lr: 1.0595e-04 eta: 6:48:22 time: 0.6495 data_time: 0.0027 memory: 11479 grad_norm: 710.8075 loss: 379.2454 loss_cls: 124.3010 loss_bbox: 117.1496 loss_dfl: 137.7948 2024/03/18 00:08:39 - mmengine - INFO - Epoch(train) [40][700/925] lr: 1.0595e-04 eta: 6:47:50 time: 0.6764 data_time: 0.0029 memory: 11359 grad_norm: 703.5122 loss: 382.4569 loss_cls: 126.0159 loss_bbox: 117.8277 loss_dfl: 138.6133 2024/03/18 00:09:14 - mmengine - INFO - Epoch(train) [40][750/925] lr: 1.0595e-04 eta: 6:47:19 time: 0.6883 data_time: 0.0028 memory: 11599 grad_norm: 729.0088 loss: 379.3810 loss_cls: 125.0152 loss_bbox: 116.8380 loss_dfl: 137.5278 2024/03/18 00:09:46 - mmengine - INFO - Epoch(train) [40][800/925] lr: 1.0595e-04 eta: 6:46:45 time: 0.6445 data_time: 0.0027 memory: 11425 grad_norm: 704.6170 loss: 377.5272 loss_cls: 123.2518 loss_bbox: 116.8138 loss_dfl: 137.4616 2024/03/18 00:10:20 - mmengine - INFO - Epoch(train) [40][850/925] lr: 1.0595e-04 eta: 6:46:13 time: 0.6732 data_time: 0.0027 memory: 11705 grad_norm: 676.8094 loss: 382.9458 loss_cls: 126.7159 loss_bbox: 117.9006 loss_dfl: 138.3293 2024/03/18 00:10:53 - mmengine - INFO - Epoch(train) [40][900/925] lr: 1.0595e-04 eta: 6:45:41 time: 0.6707 data_time: 0.0027 memory: 11492 grad_norm: 713.1174 loss: 379.6155 loss_cls: 124.1242 loss_bbox: 116.4642 loss_dfl: 139.0271 2024/03/18 00:11:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:11:10 - mmengine - INFO - Saving checkpoint at 40 epochs 2024/03/18 00:11:18 - mmengine - INFO - Epoch(val) [40][ 50/625] eta: 0:00:13 time: 0.0233 data_time: 0.0008 memory: 11105 2024/03/18 00:11:20 - mmengine - INFO - Epoch(val) [40][100/625] eta: 0:00:12 time: 0.0230 data_time: 0.0003 memory: 1709 2024/03/18 00:11:21 - mmengine - INFO - Epoch(val) [40][150/625] eta: 0:00:10 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 00:11:22 - mmengine - INFO - Epoch(val) [40][200/625] eta: 0:00:09 time: 0.0227 data_time: 0.0003 memory: 1709 2024/03/18 00:11:23 - mmengine - INFO - Epoch(val) [40][250/625] eta: 0:00:08 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 00:11:24 - mmengine - INFO - Epoch(val) [40][300/625] eta: 0:00:07 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 00:11:25 - mmengine - INFO - Epoch(val) [40][350/625] eta: 0:00:06 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 00:11:26 - mmengine - INFO - Epoch(val) [40][400/625] eta: 0:00:05 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 00:11:27 - mmengine - INFO - Epoch(val) [40][450/625] eta: 0:00:03 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 00:11:28 - mmengine - INFO - Epoch(val) [40][500/625] eta: 0:00:02 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 00:11:29 - mmengine - INFO - Epoch(val) [40][550/625] eta: 0:00:01 time: 0.0213 data_time: 0.0002 memory: 1709 2024/03/18 00:11:31 - mmengine - INFO - Epoch(val) [40][600/625] eta: 0:00:00 time: 0.0241 data_time: 0.0003 memory: 1709 2024/03/18 00:11:42 - mmengine - INFO - Evaluating bbox... 2024/03/18 00:12:58 - mmengine - INFO - bbox_mAP_copypaste: 0.527 0.694 0.575 0.353 0.577 0.685 2024/03/18 00:13:00 - mmengine - INFO - Epoch(val) [40][625/625] coco/bbox_mAP: 0.5270 coco/bbox_mAP_50: 0.6940 coco/bbox_mAP_75: 0.5750 coco/bbox_mAP_s: 0.3530 coco/bbox_mAP_m: 0.5770 coco/bbox_mAP_l: 0.6850 data_time: 0.0004 time: 0.0244 2024/03/18 00:13:35 - mmengine - INFO - Epoch(train) [41][ 50/925] lr: 1.0347e-04 eta: 6:44:53 time: 0.7043 data_time: 0.0643 memory: 11825 grad_norm: 676.8358 loss: 378.5347 loss_cls: 123.4339 loss_bbox: 116.4440 loss_dfl: 138.6567 2024/03/18 00:14:09 - mmengine - INFO - Epoch(train) [41][100/925] lr: 1.0347e-04 eta: 6:44:21 time: 0.6715 data_time: 0.0028 memory: 11652 grad_norm: 655.1620 loss: 383.4931 loss_cls: 126.5542 loss_bbox: 118.2206 loss_dfl: 138.7183 2024/03/18 00:14:43 - mmengine - INFO - Epoch(train) [41][150/925] lr: 1.0347e-04 eta: 6:43:49 time: 0.6748 data_time: 0.0028 memory: 11412 grad_norm: 735.5540 loss: 380.8048 loss_cls: 125.8879 loss_bbox: 116.5947 loss_dfl: 138.3222 2024/03/18 00:15:15 - mmengine - INFO - Epoch(train) [41][200/925] lr: 1.0347e-04 eta: 6:43:16 time: 0.6455 data_time: 0.0027 memory: 11412 grad_norm: 714.3372 loss: 380.7992 loss_cls: 125.8767 loss_bbox: 116.8541 loss_dfl: 138.0684 2024/03/18 00:15:49 - mmengine - INFO - Epoch(train) [41][250/925] lr: 1.0347e-04 eta: 6:42:43 time: 0.6734 data_time: 0.0028 memory: 11359 grad_norm: 696.9937 loss: 380.9431 loss_cls: 124.4000 loss_bbox: 118.1335 loss_dfl: 138.4096 2024/03/18 00:16:22 - mmengine - INFO - Epoch(train) [41][300/925] lr: 1.0347e-04 eta: 6:42:11 time: 0.6676 data_time: 0.0027 memory: 11225 grad_norm: 717.4567 loss: 376.5911 loss_cls: 123.0672 loss_bbox: 115.4210 loss_dfl: 138.1030 2024/03/18 00:16:56 - mmengine - INFO - Epoch(train) [41][350/925] lr: 1.0347e-04 eta: 6:41:39 time: 0.6682 data_time: 0.0030 memory: 11479 grad_norm: 680.0717 loss: 378.2671 loss_cls: 123.4251 loss_bbox: 117.0133 loss_dfl: 137.8287 2024/03/18 00:17:30 - mmengine - INFO - Epoch(train) [41][400/925] lr: 1.0347e-04 eta: 6:41:07 time: 0.6884 data_time: 0.0028 memory: 11185 grad_norm: 694.6285 loss: 381.8195 loss_cls: 126.7475 loss_bbox: 117.5083 loss_dfl: 137.5638 2024/03/18 00:18:03 - mmengine - INFO - Epoch(train) [41][450/925] lr: 1.0347e-04 eta: 6:40:35 time: 0.6670 data_time: 0.0027 memory: 11519 grad_norm: 679.4768 loss: 380.6485 loss_cls: 124.3577 loss_bbox: 118.3738 loss_dfl: 137.9170 2024/03/18 00:18:37 - mmengine - INFO - Epoch(train) [41][500/925] lr: 1.0347e-04 eta: 6:40:03 time: 0.6749 data_time: 0.0029 memory: 11505 grad_norm: 692.1047 loss: 380.1835 loss_cls: 125.2910 loss_bbox: 117.6291 loss_dfl: 137.2633 2024/03/18 00:19:10 - mmengine - INFO - Epoch(train) [41][550/925] lr: 1.0347e-04 eta: 6:39:30 time: 0.6575 data_time: 0.0027 memory: 11172 grad_norm: 681.9189 loss: 384.3463 loss_cls: 128.6295 loss_bbox: 116.5001 loss_dfl: 139.2167 2024/03/18 00:19:44 - mmengine - INFO - Epoch(train) [41][600/925] lr: 1.0347e-04 eta: 6:38:58 time: 0.6685 data_time: 0.0027 memory: 11692 grad_norm: 687.8550 loss: 379.3876 loss_cls: 124.6913 loss_bbox: 117.3382 loss_dfl: 137.3581 2024/03/18 00:20:17 - mmengine - INFO - Epoch(train) [41][650/925] lr: 1.0347e-04 eta: 6:38:25 time: 0.6697 data_time: 0.0026 memory: 11265 grad_norm: 670.5471 loss: 381.4483 loss_cls: 125.8333 loss_bbox: 117.0734 loss_dfl: 138.5416 2024/03/18 00:20:49 - mmengine - INFO - Epoch(train) [41][700/925] lr: 1.0347e-04 eta: 6:37:52 time: 0.6459 data_time: 0.0029 memory: 11532 grad_norm: 705.1955 loss: 384.2731 loss_cls: 125.9101 loss_bbox: 119.2923 loss_dfl: 139.0708 2024/03/18 00:21:23 - mmengine - INFO - Epoch(train) [41][750/925] lr: 1.0347e-04 eta: 6:37:20 time: 0.6748 data_time: 0.0027 memory: 11705 grad_norm: 704.6037 loss: 378.5365 loss_cls: 123.8557 loss_bbox: 117.7065 loss_dfl: 136.9743 2024/03/18 00:21:58 - mmengine - INFO - Epoch(train) [41][800/925] lr: 1.0347e-04 eta: 6:36:49 time: 0.6919 data_time: 0.0029 memory: 11159 grad_norm: 701.9230 loss: 381.5673 loss_cls: 126.0542 loss_bbox: 116.7548 loss_dfl: 138.7584 2024/03/18 00:22:31 - mmengine - INFO - Epoch(train) [41][850/925] lr: 1.0347e-04 eta: 6:36:16 time: 0.6650 data_time: 0.0027 memory: 11279 grad_norm: 699.4807 loss: 373.0485 loss_cls: 120.6607 loss_bbox: 115.1401 loss_dfl: 137.2477 2024/03/18 00:23:05 - mmengine - INFO - Epoch(train) [41][900/925] lr: 1.0347e-04 eta: 6:35:44 time: 0.6739 data_time: 0.0027 memory: 11892 grad_norm: 741.1043 loss: 377.2948 loss_cls: 122.4545 loss_bbox: 117.1012 loss_dfl: 137.7391 2024/03/18 00:23:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:23:58 - mmengine - INFO - Epoch(train) [42][ 50/925] lr: 1.0100e-04 eta: 6:34:58 time: 0.7379 data_time: 0.0715 memory: 11372 grad_norm: 720.4219 loss: 373.5634 loss_cls: 121.0885 loss_bbox: 115.8259 loss_dfl: 136.6491 2024/03/18 00:24:13 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:24:30 - mmengine - INFO - Epoch(train) [42][100/925] lr: 1.0100e-04 eta: 6:34:24 time: 0.6340 data_time: 0.0027 memory: 11265 grad_norm: 635.2655 loss: 382.3624 loss_cls: 127.2045 loss_bbox: 117.3871 loss_dfl: 137.7708 2024/03/18 00:25:04 - mmengine - INFO - Epoch(train) [42][150/925] lr: 1.0100e-04 eta: 6:33:52 time: 0.6787 data_time: 0.0029 memory: 11745 grad_norm: 750.0897 loss: 382.3846 loss_cls: 127.7257 loss_bbox: 116.2477 loss_dfl: 138.4112 2024/03/18 00:25:39 - mmengine - INFO - Epoch(train) [42][200/925] lr: 1.0100e-04 eta: 6:33:21 time: 0.7025 data_time: 0.0033 memory: 11625 grad_norm: 678.3868 loss: 371.3519 loss_cls: 120.8875 loss_bbox: 113.7126 loss_dfl: 136.7518 2024/03/18 00:26:12 - mmengine - INFO - Epoch(train) [42][250/925] lr: 1.0100e-04 eta: 6:32:48 time: 0.6607 data_time: 0.0028 memory: 11239 grad_norm: 725.5176 loss: 377.9326 loss_cls: 124.7323 loss_bbox: 115.4012 loss_dfl: 137.7990 2024/03/18 00:26:45 - mmengine - INFO - Epoch(train) [42][300/925] lr: 1.0100e-04 eta: 6:32:16 time: 0.6673 data_time: 0.0029 memory: 11185 grad_norm: 654.2195 loss: 375.7791 loss_cls: 122.3834 loss_bbox: 115.7804 loss_dfl: 137.6153 2024/03/18 00:27:19 - mmengine - INFO - Epoch(train) [42][350/925] lr: 1.0100e-04 eta: 6:31:43 time: 0.6657 data_time: 0.0029 memory: 11425 grad_norm: 666.1640 loss: 375.1010 loss_cls: 123.2682 loss_bbox: 114.0489 loss_dfl: 137.7839 2024/03/18 00:27:51 - mmengine - INFO - Epoch(train) [42][400/925] lr: 1.0100e-04 eta: 6:31:10 time: 0.6525 data_time: 0.0029 memory: 11492 grad_norm: 688.8837 loss: 378.8191 loss_cls: 124.3841 loss_bbox: 116.8448 loss_dfl: 137.5901 2024/03/18 00:28:24 - mmengine - INFO - Epoch(train) [42][450/925] lr: 1.0100e-04 eta: 6:30:37 time: 0.6561 data_time: 0.0027 memory: 11439 grad_norm: 725.1287 loss: 378.5006 loss_cls: 123.4745 loss_bbox: 115.9014 loss_dfl: 139.1247 2024/03/18 00:28:57 - mmengine - INFO - Epoch(train) [42][500/925] lr: 1.0100e-04 eta: 6:30:04 time: 0.6520 data_time: 0.0028 memory: 11665 grad_norm: 662.1252 loss: 374.8304 loss_cls: 121.4555 loss_bbox: 114.8870 loss_dfl: 138.4879 2024/03/18 00:29:30 - mmengine - INFO - Epoch(train) [42][550/925] lr: 1.0100e-04 eta: 6:29:31 time: 0.6580 data_time: 0.0026 memory: 11572 grad_norm: 703.1831 loss: 378.2665 loss_cls: 123.0811 loss_bbox: 117.1309 loss_dfl: 138.0545 2024/03/18 00:30:02 - mmengine - INFO - Epoch(train) [42][600/925] lr: 1.0100e-04 eta: 6:28:58 time: 0.6486 data_time: 0.0024 memory: 11679 grad_norm: 698.6156 loss: 373.3517 loss_cls: 122.0262 loss_bbox: 114.9938 loss_dfl: 136.3317 2024/03/18 00:30:36 - mmengine - INFO - Epoch(train) [42][650/925] lr: 1.0100e-04 eta: 6:28:25 time: 0.6680 data_time: 0.0030 memory: 11185 grad_norm: 670.1438 loss: 374.8679 loss_cls: 122.0265 loss_bbox: 115.6991 loss_dfl: 137.1423 2024/03/18 00:31:09 - mmengine - INFO - Epoch(train) [42][700/925] lr: 1.0100e-04 eta: 6:27:53 time: 0.6728 data_time: 0.0028 memory: 11372 grad_norm: 697.8175 loss: 377.3339 loss_cls: 124.1645 loss_bbox: 114.5710 loss_dfl: 138.5984 2024/03/18 00:31:41 - mmengine - INFO - Epoch(train) [42][750/925] lr: 1.0100e-04 eta: 6:27:19 time: 0.6291 data_time: 0.0028 memory: 11532 grad_norm: 728.8258 loss: 380.7286 loss_cls: 125.5628 loss_bbox: 116.0173 loss_dfl: 139.1485 2024/03/18 00:32:14 - mmengine - INFO - Epoch(train) [42][800/925] lr: 1.0100e-04 eta: 6:26:46 time: 0.6600 data_time: 0.0024 memory: 11892 grad_norm: 673.3827 loss: 383.6894 loss_cls: 127.7662 loss_bbox: 118.4797 loss_dfl: 137.4435 2024/03/18 00:32:47 - mmengine - INFO - Epoch(train) [42][850/925] lr: 1.0100e-04 eta: 6:26:13 time: 0.6580 data_time: 0.0028 memory: 11492 grad_norm: 691.7397 loss: 377.3108 loss_cls: 122.7882 loss_bbox: 116.1064 loss_dfl: 138.4163 2024/03/18 00:33:19 - mmengine - INFO - Epoch(train) [42][900/925] lr: 1.0100e-04 eta: 6:25:40 time: 0.6505 data_time: 0.0026 memory: 11412 grad_norm: 711.8059 loss: 374.0833 loss_cls: 122.6773 loss_bbox: 114.8162 loss_dfl: 136.5898 2024/03/18 00:33:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:34:12 - mmengine - INFO - Epoch(train) [43][ 50/925] lr: 9.8525e-05 eta: 6:24:53 time: 0.7226 data_time: 0.0693 memory: 11305 grad_norm: 693.6763 loss: 375.4751 loss_cls: 123.6418 loss_bbox: 114.9879 loss_dfl: 136.8454 2024/03/18 00:34:46 - mmengine - INFO - Epoch(train) [43][100/925] lr: 9.8525e-05 eta: 6:24:21 time: 0.6749 data_time: 0.0028 memory: 11692 grad_norm: 772.3498 loss: 380.7322 loss_cls: 126.1393 loss_bbox: 116.2135 loss_dfl: 138.3794 2024/03/18 00:35:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:35:19 - mmengine - INFO - Epoch(train) [43][150/925] lr: 9.8525e-05 eta: 6:23:48 time: 0.6655 data_time: 0.0030 memory: 11359 grad_norm: 709.8362 loss: 377.5388 loss_cls: 124.8014 loss_bbox: 115.2066 loss_dfl: 137.5308 2024/03/18 00:35:52 - mmengine - INFO - Epoch(train) [43][200/925] lr: 9.8525e-05 eta: 6:23:16 time: 0.6717 data_time: 0.0028 memory: 11465 grad_norm: 672.1466 loss: 379.7780 loss_cls: 124.5717 loss_bbox: 117.2559 loss_dfl: 137.9504 2024/03/18 00:36:27 - mmengine - INFO - Epoch(train) [43][250/925] lr: 9.8525e-05 eta: 6:22:44 time: 0.6863 data_time: 0.0028 memory: 11439 grad_norm: 709.4635 loss: 379.4299 loss_cls: 124.5617 loss_bbox: 116.8487 loss_dfl: 138.0196 2024/03/18 00:37:00 - mmengine - INFO - Epoch(train) [43][300/925] lr: 9.8525e-05 eta: 6:22:11 time: 0.6533 data_time: 0.0027 memory: 11399 grad_norm: inf loss: 371.9438 loss_cls: 120.7375 loss_bbox: 114.8949 loss_dfl: 136.3115 2024/03/18 00:37:33 - mmengine - INFO - Epoch(train) [43][350/925] lr: 9.8525e-05 eta: 6:21:39 time: 0.6742 data_time: 0.0027 memory: 11225 grad_norm: 673.1589 loss: 376.7100 loss_cls: 123.0785 loss_bbox: 116.2230 loss_dfl: 137.4085 2024/03/18 00:38:06 - mmengine - INFO - Epoch(train) [43][400/925] lr: 9.8525e-05 eta: 6:21:06 time: 0.6611 data_time: 0.0033 memory: 11252 grad_norm: 644.5802 loss: 384.1213 loss_cls: 126.4454 loss_bbox: 118.1755 loss_dfl: 139.5003 2024/03/18 00:38:40 - mmengine - INFO - Epoch(train) [43][450/925] lr: 9.8525e-05 eta: 6:20:34 time: 0.6749 data_time: 0.0031 memory: 11945 grad_norm: 751.2266 loss: 374.2017 loss_cls: 121.5622 loss_bbox: 115.6707 loss_dfl: 136.9688 2024/03/18 00:39:15 - mmengine - INFO - Epoch(train) [43][500/925] lr: 9.8525e-05 eta: 6:20:03 time: 0.6934 data_time: 0.0030 memory: 11239 grad_norm: 672.6281 loss: 370.4891 loss_cls: 120.4558 loss_bbox: 113.8649 loss_dfl: 136.1685 2024/03/18 00:39:49 - mmengine - INFO - Epoch(train) [43][550/925] lr: 9.8525e-05 eta: 6:19:31 time: 0.6754 data_time: 0.0028 memory: 11305 grad_norm: 683.7573 loss: 375.2282 loss_cls: 122.5883 loss_bbox: 115.5195 loss_dfl: 137.1204 2024/03/18 00:40:24 - mmengine - INFO - Epoch(train) [43][600/925] lr: 9.8525e-05 eta: 6:19:00 time: 0.7024 data_time: 0.0030 memory: 11439 grad_norm: 753.8937 loss: 382.4145 loss_cls: 126.8523 loss_bbox: 117.3696 loss_dfl: 138.1925 2024/03/18 00:40:56 - mmengine - INFO - Epoch(train) [43][650/925] lr: 9.8525e-05 eta: 6:18:26 time: 0.6475 data_time: 0.0026 memory: 11372 grad_norm: 723.0045 loss: 379.6192 loss_cls: 124.9936 loss_bbox: 116.3395 loss_dfl: 138.2861 2024/03/18 00:41:30 - mmengine - INFO - Epoch(train) [43][700/925] lr: 9.8525e-05 eta: 6:17:54 time: 0.6663 data_time: 0.0026 memory: 11959 grad_norm: 649.1678 loss: 374.2545 loss_cls: 122.2397 loss_bbox: 114.7787 loss_dfl: 137.2361 2024/03/18 00:42:04 - mmengine - INFO - Epoch(train) [43][750/925] lr: 9.8525e-05 eta: 6:17:22 time: 0.6782 data_time: 0.0027 memory: 11652 grad_norm: 707.9052 loss: 378.8267 loss_cls: 123.8239 loss_bbox: 117.5838 loss_dfl: 137.4191 2024/03/18 00:42:36 - mmengine - INFO - Epoch(train) [43][800/925] lr: 9.8525e-05 eta: 6:16:49 time: 0.6565 data_time: 0.0028 memory: 11372 grad_norm: 715.3368 loss: 371.0727 loss_cls: 120.7024 loss_bbox: 115.3670 loss_dfl: 135.0033 2024/03/18 00:43:10 - mmengine - INFO - Epoch(train) [43][850/925] lr: 9.8525e-05 eta: 6:16:16 time: 0.6716 data_time: 0.0030 memory: 11425 grad_norm: 674.3069 loss: 378.2755 loss_cls: 124.4001 loss_bbox: 116.3485 loss_dfl: 137.5270 2024/03/18 00:43:44 - mmengine - INFO - Epoch(train) [43][900/925] lr: 9.8525e-05 eta: 6:15:44 time: 0.6734 data_time: 0.0027 memory: 11239 grad_norm: 714.6232 loss: 375.5071 loss_cls: 122.0271 loss_bbox: 116.4403 loss_dfl: 137.0397 2024/03/18 00:44:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:44:36 - mmengine - INFO - Epoch(train) [44][ 50/925] lr: 9.6050e-05 eta: 6:14:57 time: 0.7210 data_time: 0.0654 memory: 11279 grad_norm: 735.6396 loss: 374.0201 loss_cls: 122.1429 loss_bbox: 114.6197 loss_dfl: 137.2575 2024/03/18 00:45:10 - mmengine - INFO - Epoch(train) [44][100/925] lr: 9.6050e-05 eta: 6:14:24 time: 0.6681 data_time: 0.0026 memory: 11865 grad_norm: 715.6074 loss: 377.0966 loss_cls: 123.9416 loss_bbox: 116.5801 loss_dfl: 136.5749 2024/03/18 00:45:44 - mmengine - INFO - Epoch(train) [44][150/925] lr: 9.6050e-05 eta: 6:13:53 time: 0.6873 data_time: 0.0027 memory: 11439 grad_norm: 724.1692 loss: 377.3249 loss_cls: 123.8328 loss_bbox: 116.5490 loss_dfl: 136.9431 2024/03/18 00:46:17 - mmengine - INFO - Epoch(train) [44][200/925] lr: 9.6050e-05 eta: 6:13:20 time: 0.6591 data_time: 0.0027 memory: 11532 grad_norm: 732.2342 loss: 374.7574 loss_cls: 122.1681 loss_bbox: 115.3284 loss_dfl: 137.2608 2024/03/18 00:46:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:46:50 - mmengine - INFO - Epoch(train) [44][250/925] lr: 9.6050e-05 eta: 6:12:47 time: 0.6631 data_time: 0.0028 memory: 11292 grad_norm: 738.0159 loss: 379.2945 loss_cls: 124.9202 loss_bbox: 116.0204 loss_dfl: 138.3539 2024/03/18 00:47:25 - mmengine - INFO - Epoch(train) [44][300/925] lr: 9.6050e-05 eta: 6:12:16 time: 0.6932 data_time: 0.0027 memory: 11625 grad_norm: 686.5018 loss: 375.9123 loss_cls: 122.3957 loss_bbox: 115.5188 loss_dfl: 137.9979 2024/03/18 00:47:58 - mmengine - INFO - Epoch(train) [44][350/925] lr: 9.6050e-05 eta: 6:11:43 time: 0.6522 data_time: 0.0028 memory: 10999 grad_norm: 742.4236 loss: 372.7155 loss_cls: 120.1655 loss_bbox: 115.4503 loss_dfl: 137.0998 2024/03/18 00:48:32 - mmengine - INFO - Epoch(train) [44][400/925] lr: 9.6050e-05 eta: 6:11:11 time: 0.6783 data_time: 0.0026 memory: 11319 grad_norm: 674.3280 loss: 372.7321 loss_cls: 120.4155 loss_bbox: 114.3187 loss_dfl: 137.9979 2024/03/18 00:49:05 - mmengine - INFO - Epoch(train) [44][450/925] lr: 9.6050e-05 eta: 6:10:38 time: 0.6730 data_time: 0.0028 memory: 11225 grad_norm: 709.8926 loss: 377.3187 loss_cls: 124.4926 loss_bbox: 115.7452 loss_dfl: 137.0809 2024/03/18 00:49:38 - mmengine - INFO - Epoch(train) [44][500/925] lr: 9.6050e-05 eta: 6:10:06 time: 0.6644 data_time: 0.0028 memory: 11132 grad_norm: 710.7519 loss: 378.9914 loss_cls: 123.5919 loss_bbox: 117.1569 loss_dfl: 138.2426 2024/03/18 00:50:12 - mmengine - INFO - Epoch(train) [44][550/925] lr: 9.6050e-05 eta: 6:09:33 time: 0.6673 data_time: 0.0030 memory: 11172 grad_norm: 650.0602 loss: 375.1094 loss_cls: 121.9901 loss_bbox: 115.3846 loss_dfl: 137.7348 2024/03/18 00:50:46 - mmengine - INFO - Epoch(train) [44][600/925] lr: 9.6050e-05 eta: 6:09:01 time: 0.6745 data_time: 0.0028 memory: 11692 grad_norm: 699.4909 loss: 379.3788 loss_cls: 124.6051 loss_bbox: 116.5426 loss_dfl: 138.2310 2024/03/18 00:51:19 - mmengine - INFO - Epoch(train) [44][650/925] lr: 9.6050e-05 eta: 6:08:28 time: 0.6742 data_time: 0.0027 memory: 11345 grad_norm: 674.1887 loss: 374.4667 loss_cls: 122.1548 loss_bbox: 114.5721 loss_dfl: 137.7398 2024/03/18 00:51:53 - mmengine - INFO - Epoch(train) [44][700/925] lr: 9.6050e-05 eta: 6:07:56 time: 0.6643 data_time: 0.0028 memory: 11625 grad_norm: 715.9490 loss: 377.7572 loss_cls: 125.6919 loss_bbox: 113.4382 loss_dfl: 138.6272 2024/03/18 00:52:26 - mmengine - INFO - Epoch(train) [44][750/925] lr: 9.6050e-05 eta: 6:07:23 time: 0.6661 data_time: 0.0027 memory: 11345 grad_norm: 723.6187 loss: 375.4846 loss_cls: 122.1723 loss_bbox: 115.4044 loss_dfl: 137.9079 2024/03/18 00:53:00 - mmengine - INFO - Epoch(train) [44][800/925] lr: 9.6050e-05 eta: 6:06:51 time: 0.6762 data_time: 0.0027 memory: 11492 grad_norm: 651.6007 loss: 380.3074 loss_cls: 123.6177 loss_bbox: 118.0004 loss_dfl: 138.6894 2024/03/18 00:53:33 - mmengine - INFO - Epoch(train) [44][850/925] lr: 9.6050e-05 eta: 6:06:18 time: 0.6542 data_time: 0.0028 memory: 11079 grad_norm: 733.0399 loss: 376.1338 loss_cls: 122.6522 loss_bbox: 115.3303 loss_dfl: 138.1512 2024/03/18 00:54:06 - mmengine - INFO - Epoch(train) [44][900/925] lr: 9.6050e-05 eta: 6:05:45 time: 0.6695 data_time: 0.0028 memory: 11425 grad_norm: 725.2608 loss: 380.5247 loss_cls: 125.0606 loss_bbox: 117.8919 loss_dfl: 137.5723 2024/03/18 00:54:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:54:59 - mmengine - INFO - Epoch(train) [45][ 50/925] lr: 9.3575e-05 eta: 6:04:58 time: 0.7238 data_time: 0.0660 memory: 11279 grad_norm: 659.9070 loss: 373.8345 loss_cls: 122.1668 loss_bbox: 113.8986 loss_dfl: 137.7690 2024/03/18 00:55:32 - mmengine - INFO - Epoch(train) [45][100/925] lr: 9.3575e-05 eta: 6:04:25 time: 0.6579 data_time: 0.0028 memory: 11292 grad_norm: 755.2686 loss: 376.0714 loss_cls: 123.4858 loss_bbox: 115.1163 loss_dfl: 137.4693 2024/03/18 00:56:05 - mmengine - INFO - Epoch(train) [45][150/925] lr: 9.3575e-05 eta: 6:03:53 time: 0.6619 data_time: 0.0028 memory: 11465 grad_norm: 764.6460 loss: 380.2549 loss_cls: 125.2813 loss_bbox: 116.5273 loss_dfl: 138.4463 2024/03/18 00:56:38 - mmengine - INFO - Epoch(train) [45][200/925] lr: 9.3575e-05 eta: 6:03:20 time: 0.6585 data_time: 0.0029 memory: 11185 grad_norm: 714.9358 loss: 374.5662 loss_cls: 123.5352 loss_bbox: 114.0671 loss_dfl: 136.9639 2024/03/18 00:57:11 - mmengine - INFO - Epoch(train) [45][250/925] lr: 9.3575e-05 eta: 6:02:47 time: 0.6552 data_time: 0.0024 memory: 11145 grad_norm: 658.9576 loss: 374.5485 loss_cls: 121.8553 loss_bbox: 115.7671 loss_dfl: 136.9261 2024/03/18 00:57:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 00:57:44 - mmengine - INFO - Epoch(train) [45][300/925] lr: 9.3575e-05 eta: 6:02:14 time: 0.6676 data_time: 0.0025 memory: 11132 grad_norm: 719.1043 loss: 372.8747 loss_cls: 122.3392 loss_bbox: 113.4432 loss_dfl: 137.0923 2024/03/18 00:58:18 - mmengine - INFO - Epoch(train) [45][350/925] lr: 9.3575e-05 eta: 6:01:42 time: 0.6710 data_time: 0.0028 memory: 11599 grad_norm: 684.7244 loss: 373.8995 loss_cls: 121.8063 loss_bbox: 114.8409 loss_dfl: 137.2523 2024/03/18 00:58:50 - mmengine - INFO - Epoch(train) [45][400/925] lr: 9.3575e-05 eta: 6:01:08 time: 0.6528 data_time: 0.0027 memory: 11519 grad_norm: 729.1081 loss: 375.2555 loss_cls: 121.7008 loss_bbox: 116.1259 loss_dfl: 137.4288 2024/03/18 00:59:24 - mmengine - INFO - Epoch(train) [45][450/925] lr: 9.3575e-05 eta: 6:00:36 time: 0.6725 data_time: 0.0027 memory: 11252 grad_norm: 724.5145 loss: 377.7393 loss_cls: 124.8193 loss_bbox: 115.2253 loss_dfl: 137.6948 2024/03/18 00:59:57 - mmengine - INFO - Epoch(train) [45][500/925] lr: 9.3575e-05 eta: 6:00:03 time: 0.6658 data_time: 0.0028 memory: 11279 grad_norm: 718.9533 loss: 374.2882 loss_cls: 120.6177 loss_bbox: 115.7877 loss_dfl: 137.8829 2024/03/18 01:00:30 - mmengine - INFO - Epoch(train) [45][550/925] lr: 9.3575e-05 eta: 5:59:30 time: 0.6561 data_time: 0.0026 memory: 11732 grad_norm: 680.6885 loss: 384.4422 loss_cls: 126.9832 loss_bbox: 117.4764 loss_dfl: 139.9826 2024/03/18 01:01:03 - mmengine - INFO - Epoch(train) [45][600/925] lr: 9.3575e-05 eta: 5:58:57 time: 0.6552 data_time: 0.0025 memory: 11972 grad_norm: 706.5231 loss: 379.5791 loss_cls: 124.5232 loss_bbox: 117.2379 loss_dfl: 137.8180 2024/03/18 01:01:36 - mmengine - INFO - Epoch(train) [45][650/925] lr: 9.3575e-05 eta: 5:58:25 time: 0.6638 data_time: 0.0026 memory: 11692 grad_norm: 692.6036 loss: 369.5016 loss_cls: 119.6488 loss_bbox: 113.5002 loss_dfl: 136.3527 2024/03/18 01:02:09 - mmengine - INFO - Epoch(train) [45][700/925] lr: 9.3575e-05 eta: 5:57:52 time: 0.6618 data_time: 0.0027 memory: 11265 grad_norm: 689.1483 loss: 378.3173 loss_cls: 123.1853 loss_bbox: 115.8468 loss_dfl: 139.2852 2024/03/18 01:02:43 - mmengine - INFO - Epoch(train) [45][750/925] lr: 9.3575e-05 eta: 5:57:19 time: 0.6643 data_time: 0.0027 memory: 11305 grad_norm: 706.1241 loss: 381.6621 loss_cls: 127.7869 loss_bbox: 115.2202 loss_dfl: 138.6551 2024/03/18 01:03:16 - mmengine - INFO - Epoch(train) [45][800/925] lr: 9.3575e-05 eta: 5:56:46 time: 0.6577 data_time: 0.0024 memory: 11519 grad_norm: 720.6023 loss: 372.4369 loss_cls: 119.7335 loss_bbox: 115.0761 loss_dfl: 137.6273 2024/03/18 01:03:49 - mmengine - INFO - Epoch(train) [45][850/925] lr: 9.3575e-05 eta: 5:56:13 time: 0.6663 data_time: 0.0027 memory: 11452 grad_norm: 736.1601 loss: 379.7023 loss_cls: 124.0694 loss_bbox: 116.5479 loss_dfl: 139.0849 2024/03/18 01:04:22 - mmengine - INFO - Epoch(train) [45][900/925] lr: 9.3575e-05 eta: 5:55:40 time: 0.6532 data_time: 0.0028 memory: 11212 grad_norm: 757.5740 loss: 372.3915 loss_cls: 121.8586 loss_bbox: 113.7540 loss_dfl: 136.7789 2024/03/18 01:04:38 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:04:39 - mmengine - INFO - Saving checkpoint at 45 epochs 2024/03/18 01:04:47 - mmengine - INFO - Epoch(val) [45][ 50/625] eta: 0:00:13 time: 0.0239 data_time: 0.0008 memory: 11239 2024/03/18 01:04:48 - mmengine - INFO - Epoch(val) [45][100/625] eta: 0:00:12 time: 0.0235 data_time: 0.0003 memory: 1709 2024/03/18 01:04:50 - mmengine - INFO - Epoch(val) [45][150/625] eta: 0:00:11 time: 0.0237 data_time: 0.0003 memory: 1709 2024/03/18 01:04:51 - mmengine - INFO - Epoch(val) [45][200/625] eta: 0:00:10 time: 0.0239 data_time: 0.0003 memory: 1709 2024/03/18 01:04:52 - mmengine - INFO - Epoch(val) [45][250/625] eta: 0:00:08 time: 0.0234 data_time: 0.0003 memory: 1709 2024/03/18 01:04:53 - mmengine - INFO - Epoch(val) [45][300/625] eta: 0:00:07 time: 0.0232 data_time: 0.0003 memory: 1709 2024/03/18 01:04:54 - mmengine - INFO - Epoch(val) [45][350/625] eta: 0:00:06 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/18 01:04:55 - mmengine - INFO - Epoch(val) [45][400/625] eta: 0:00:05 time: 0.0221 data_time: 0.0002 memory: 1709 2024/03/18 01:04:56 - mmengine - INFO - Epoch(val) [45][450/625] eta: 0:00:04 time: 0.0220 data_time: 0.0003 memory: 1709 2024/03/18 01:04:58 - mmengine - INFO - Epoch(val) [45][500/625] eta: 0:00:02 time: 0.0226 data_time: 0.0003 memory: 1709 2024/03/18 01:04:59 - mmengine - INFO - Epoch(val) [45][550/625] eta: 0:00:01 time: 0.0222 data_time: 0.0003 memory: 1709 2024/03/18 01:05:00 - mmengine - INFO - Epoch(val) [45][600/625] eta: 0:00:00 time: 0.0223 data_time: 0.0003 memory: 1709 2024/03/18 01:05:11 - mmengine - INFO - Evaluating bbox... 2024/03/18 01:06:23 - mmengine - INFO - bbox_mAP_copypaste: 0.528 0.695 0.574 0.352 0.576 0.687 2024/03/18 01:06:25 - mmengine - INFO - Epoch(val) [45][625/625] coco/bbox_mAP: 0.5280 coco/bbox_mAP_50: 0.6950 coco/bbox_mAP_75: 0.5740 coco/bbox_mAP_s: 0.3520 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6870 data_time: 0.0003 time: 0.0222 2024/03/18 01:07:00 - mmengine - INFO - Epoch(train) [46][ 50/925] lr: 9.1100e-05 eta: 5:54:53 time: 0.7027 data_time: 0.0631 memory: 11332 grad_norm: 720.3399 loss: 374.6298 loss_cls: 122.9837 loss_bbox: 114.0550 loss_dfl: 137.5911 2024/03/18 01:07:32 - mmengine - INFO - Epoch(train) [46][100/925] lr: 9.1100e-05 eta: 5:54:19 time: 0.6490 data_time: 0.0027 memory: 11372 grad_norm: 718.1879 loss: 375.1733 loss_cls: 121.1287 loss_bbox: 116.2026 loss_dfl: 137.8420 2024/03/18 01:08:06 - mmengine - INFO - Epoch(train) [46][150/925] lr: 9.1100e-05 eta: 5:53:47 time: 0.6650 data_time: 0.0028 memory: 11265 grad_norm: 728.6699 loss: 376.3952 loss_cls: 122.9963 loss_bbox: 115.6109 loss_dfl: 137.7880 2024/03/18 01:08:39 - mmengine - INFO - Epoch(train) [46][200/925] lr: 9.1100e-05 eta: 5:53:14 time: 0.6568 data_time: 0.0027 memory: 11439 grad_norm: 692.3659 loss: 377.9091 loss_cls: 122.2574 loss_bbox: 117.9681 loss_dfl: 137.6837 2024/03/18 01:09:12 - mmengine - INFO - Epoch(train) [46][250/925] lr: 9.1100e-05 eta: 5:52:41 time: 0.6615 data_time: 0.0029 memory: 11279 grad_norm: 662.6644 loss: 375.2521 loss_cls: 123.5557 loss_bbox: 114.9290 loss_dfl: 136.7674 2024/03/18 01:09:45 - mmengine - INFO - Epoch(train) [46][300/925] lr: 9.1100e-05 eta: 5:52:08 time: 0.6559 data_time: 0.0028 memory: 11452 grad_norm: 658.3809 loss: 370.7156 loss_cls: 120.3327 loss_bbox: 114.3226 loss_dfl: 136.0603 2024/03/18 01:10:18 - mmengine - INFO - Epoch(train) [46][350/925] lr: 9.1100e-05 eta: 5:51:35 time: 0.6580 data_time: 0.0028 memory: 11399 grad_norm: inf loss: 376.7197 loss_cls: 123.0847 loss_bbox: 116.5256 loss_dfl: 137.1095 2024/03/18 01:10:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:10:51 - mmengine - INFO - Epoch(train) [46][400/925] lr: 9.1100e-05 eta: 5:51:02 time: 0.6662 data_time: 0.0028 memory: 11319 grad_norm: 664.4116 loss: 379.1489 loss_cls: 123.9537 loss_bbox: 117.1717 loss_dfl: 138.0236 2024/03/18 01:11:24 - mmengine - INFO - Epoch(train) [46][450/925] lr: 9.1100e-05 eta: 5:50:29 time: 0.6603 data_time: 0.0027 memory: 11305 grad_norm: 745.9170 loss: 373.8329 loss_cls: 122.0170 loss_bbox: 114.3390 loss_dfl: 137.4769 2024/03/18 01:11:57 - mmengine - INFO - Epoch(train) [46][500/925] lr: 9.1100e-05 eta: 5:49:56 time: 0.6563 data_time: 0.0028 memory: 11505 grad_norm: 706.5962 loss: 376.6812 loss_cls: 122.2666 loss_bbox: 116.9417 loss_dfl: 137.4729 2024/03/18 01:12:30 - mmengine - INFO - Epoch(train) [46][550/925] lr: 9.1100e-05 eta: 5:49:23 time: 0.6565 data_time: 0.0028 memory: 11519 grad_norm: 667.8669 loss: 372.2342 loss_cls: 121.0706 loss_bbox: 114.3878 loss_dfl: 136.7758 2024/03/18 01:13:02 - mmengine - INFO - Epoch(train) [46][600/925] lr: 9.1100e-05 eta: 5:48:50 time: 0.6537 data_time: 0.0029 memory: 11425 grad_norm: 719.8541 loss: 372.4928 loss_cls: 122.4457 loss_bbox: 112.7038 loss_dfl: 137.3433 2024/03/18 01:13:35 - mmengine - INFO - Epoch(train) [46][650/925] lr: 9.1100e-05 eta: 5:48:17 time: 0.6570 data_time: 0.0030 memory: 11119 grad_norm: 720.6177 loss: 372.4155 loss_cls: 121.5110 loss_bbox: 113.9077 loss_dfl: 136.9968 2024/03/18 01:14:08 - mmengine - INFO - Epoch(train) [46][700/925] lr: 9.1100e-05 eta: 5:47:44 time: 0.6582 data_time: 0.0028 memory: 11345 grad_norm: 705.7786 loss: 375.4635 loss_cls: 122.6194 loss_bbox: 115.1121 loss_dfl: 137.7319 2024/03/18 01:14:41 - mmengine - INFO - Epoch(train) [46][750/925] lr: 9.1100e-05 eta: 5:47:11 time: 0.6567 data_time: 0.0028 memory: 11439 grad_norm: 711.0779 loss: 385.3764 loss_cls: 127.3660 loss_bbox: 118.0611 loss_dfl: 139.9493 2024/03/18 01:15:14 - mmengine - INFO - Epoch(train) [46][800/925] lr: 9.1100e-05 eta: 5:46:38 time: 0.6624 data_time: 0.0027 memory: 11745 grad_norm: 719.3831 loss: 370.8285 loss_cls: 119.6643 loss_bbox: 115.1847 loss_dfl: 135.9795 2024/03/18 01:15:47 - mmengine - INFO - Epoch(train) [46][850/925] lr: 9.1100e-05 eta: 5:46:05 time: 0.6602 data_time: 0.0027 memory: 11812 grad_norm: 749.3085 loss: 373.6914 loss_cls: 120.4806 loss_bbox: 115.3665 loss_dfl: 137.8443 2024/03/18 01:16:20 - mmengine - INFO - Epoch(train) [46][900/925] lr: 9.1100e-05 eta: 5:45:33 time: 0.6639 data_time: 0.0027 memory: 11825 grad_norm: 695.1329 loss: 377.7576 loss_cls: 122.8563 loss_bbox: 116.0719 loss_dfl: 138.8295 2024/03/18 01:16:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:17:12 - mmengine - INFO - Epoch(train) [47][ 50/925] lr: 8.8625e-05 eta: 5:44:44 time: 0.7084 data_time: 0.0674 memory: 11865 grad_norm: 757.4578 loss: 367.5881 loss_cls: 117.4005 loss_bbox: 113.5107 loss_dfl: 136.6769 2024/03/18 01:17:45 - mmengine - INFO - Epoch(train) [47][100/925] lr: 8.8625e-05 eta: 5:44:11 time: 0.6526 data_time: 0.0027 memory: 11132 grad_norm: 730.7367 loss: 364.3334 loss_cls: 117.2274 loss_bbox: 111.0950 loss_dfl: 136.0110 2024/03/18 01:18:16 - mmengine - INFO - Epoch(train) [47][150/925] lr: 8.8625e-05 eta: 5:43:37 time: 0.6339 data_time: 0.0026 memory: 11319 grad_norm: 685.6231 loss: 377.5894 loss_cls: 124.0296 loss_bbox: 115.5854 loss_dfl: 137.9743 2024/03/18 01:18:49 - mmengine - INFO - Epoch(train) [47][200/925] lr: 8.8625e-05 eta: 5:43:04 time: 0.6494 data_time: 0.0027 memory: 11465 grad_norm: 681.1753 loss: 374.7082 loss_cls: 121.5380 loss_bbox: 116.0835 loss_dfl: 137.0867 2024/03/18 01:19:21 - mmengine - INFO - Epoch(train) [47][250/925] lr: 8.8625e-05 eta: 5:42:31 time: 0.6456 data_time: 0.0026 memory: 11345 grad_norm: 706.8885 loss: 378.3761 loss_cls: 123.6987 loss_bbox: 115.9117 loss_dfl: 138.7658 2024/03/18 01:19:54 - mmengine - INFO - Epoch(train) [47][300/925] lr: 8.8625e-05 eta: 5:41:57 time: 0.6494 data_time: 0.0027 memory: 11545 grad_norm: 704.8220 loss: 373.8448 loss_cls: 119.6590 loss_bbox: 116.4021 loss_dfl: 137.7837 2024/03/18 01:20:27 - mmengine - INFO - Epoch(train) [47][350/925] lr: 8.8625e-05 eta: 5:41:25 time: 0.6588 data_time: 0.0029 memory: 11279 grad_norm: 680.6197 loss: 380.0673 loss_cls: 124.5109 loss_bbox: 116.4553 loss_dfl: 139.1011 2024/03/18 01:20:59 - mmengine - INFO - Epoch(train) [47][400/925] lr: 8.8625e-05 eta: 5:40:51 time: 0.6408 data_time: 0.0028 memory: 11399 grad_norm: 696.7614 loss: 376.0873 loss_cls: 122.7691 loss_bbox: 115.7235 loss_dfl: 137.5947 2024/03/18 01:21:32 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:21:32 - mmengine - INFO - Epoch(train) [47][450/925] lr: 8.8625e-05 eta: 5:40:18 time: 0.6634 data_time: 0.0028 memory: 11345 grad_norm: 772.4148 loss: 369.0096 loss_cls: 118.6477 loss_bbox: 113.6058 loss_dfl: 136.7561 2024/03/18 01:22:04 - mmengine - INFO - Epoch(train) [47][500/925] lr: 8.8625e-05 eta: 5:39:45 time: 0.6408 data_time: 0.0025 memory: 11279 grad_norm: 763.9743 loss: 380.1126 loss_cls: 123.8942 loss_bbox: 118.1218 loss_dfl: 138.0966 2024/03/18 01:22:36 - mmengine - INFO - Epoch(train) [47][550/925] lr: 8.8625e-05 eta: 5:39:11 time: 0.6413 data_time: 0.0028 memory: 11772 grad_norm: 689.5854 loss: 371.8678 loss_cls: 120.5954 loss_bbox: 113.9504 loss_dfl: 137.3221 2024/03/18 01:23:09 - mmengine - INFO - Epoch(train) [47][600/925] lr: 8.8625e-05 eta: 5:38:38 time: 0.6521 data_time: 0.0025 memory: 11452 grad_norm: 682.4467 loss: 370.0004 loss_cls: 120.5723 loss_bbox: 113.0438 loss_dfl: 136.3843 2024/03/18 01:23:41 - mmengine - INFO - Epoch(train) [47][650/925] lr: 8.8625e-05 eta: 5:38:04 time: 0.6432 data_time: 0.0027 memory: 11439 grad_norm: 667.7240 loss: 369.8059 loss_cls: 119.8212 loss_bbox: 114.1064 loss_dfl: 135.8782 2024/03/18 01:24:13 - mmengine - INFO - Epoch(train) [47][700/925] lr: 8.8625e-05 eta: 5:37:31 time: 0.6432 data_time: 0.0029 memory: 11345 grad_norm: 744.8128 loss: 378.2566 loss_cls: 124.1531 loss_bbox: 116.5254 loss_dfl: 137.5782 2024/03/18 01:24:46 - mmengine - INFO - Epoch(train) [47][750/925] lr: 8.8625e-05 eta: 5:36:58 time: 0.6465 data_time: 0.0030 memory: 11305 grad_norm: 697.8144 loss: 380.6792 loss_cls: 124.5395 loss_bbox: 117.1432 loss_dfl: 138.9964 2024/03/18 01:25:18 - mmengine - INFO - Epoch(train) [47][800/925] lr: 8.8625e-05 eta: 5:36:24 time: 0.6435 data_time: 0.0030 memory: 11559 grad_norm: 731.1590 loss: 376.3375 loss_cls: 122.1942 loss_bbox: 115.7204 loss_dfl: 138.4229 2024/03/18 01:25:50 - mmengine - INFO - Epoch(train) [47][850/925] lr: 8.8625e-05 eta: 5:35:51 time: 0.6532 data_time: 0.0032 memory: 11372 grad_norm: 704.0632 loss: 375.9026 loss_cls: 123.3314 loss_bbox: 114.8476 loss_dfl: 137.7236 2024/03/18 01:26:24 - mmengine - INFO - Epoch(train) [47][900/925] lr: 8.8625e-05 eta: 5:35:18 time: 0.6660 data_time: 0.0033 memory: 11905 grad_norm: 698.2789 loss: 374.1878 loss_cls: 121.4445 loss_bbox: 115.8552 loss_dfl: 136.8880 2024/03/18 01:26:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:27:16 - mmengine - INFO - Epoch(train) [48][ 50/925] lr: 8.6150e-05 eta: 5:34:31 time: 0.7053 data_time: 0.0540 memory: 11692 grad_norm: 702.9657 loss: 379.5002 loss_cls: 123.4319 loss_bbox: 117.2458 loss_dfl: 138.8225 2024/03/18 01:27:49 - mmengine - INFO - Epoch(train) [48][100/925] lr: 8.6150e-05 eta: 5:33:58 time: 0.6542 data_time: 0.0030 memory: 11625 grad_norm: 717.8483 loss: 376.4204 loss_cls: 123.0571 loss_bbox: 115.6539 loss_dfl: 137.7095 2024/03/18 01:28:23 - mmengine - INFO - Epoch(train) [48][150/925] lr: 8.6150e-05 eta: 5:33:25 time: 0.6719 data_time: 0.0031 memory: 11372 grad_norm: 723.9424 loss: 375.3647 loss_cls: 122.6718 loss_bbox: 115.4210 loss_dfl: 137.2719 2024/03/18 01:28:56 - mmengine - INFO - Epoch(train) [48][200/925] lr: 8.6150e-05 eta: 5:32:52 time: 0.6636 data_time: 0.0031 memory: 11412 grad_norm: 833.6910 loss: 372.7379 loss_cls: 121.1671 loss_bbox: 114.5198 loss_dfl: 137.0510 2024/03/18 01:29:30 - mmengine - INFO - Epoch(train) [48][250/925] lr: 8.6150e-05 eta: 5:32:20 time: 0.6748 data_time: 0.0032 memory: 11239 grad_norm: 715.8703 loss: 372.1134 loss_cls: 120.7413 loss_bbox: 115.0209 loss_dfl: 136.3512 2024/03/18 01:30:03 - mmengine - INFO - Epoch(train) [48][300/925] lr: 8.6150e-05 eta: 5:31:48 time: 0.6726 data_time: 0.0029 memory: 11692 grad_norm: 659.7293 loss: 377.2631 loss_cls: 122.7745 loss_bbox: 117.0444 loss_dfl: 137.4441 2024/03/18 01:30:37 - mmengine - INFO - Epoch(train) [48][350/925] lr: 8.6150e-05 eta: 5:31:15 time: 0.6799 data_time: 0.0034 memory: 11332 grad_norm: 772.8829 loss: 373.7081 loss_cls: 122.6608 loss_bbox: 113.5404 loss_dfl: 137.5070 2024/03/18 01:31:11 - mmengine - INFO - Epoch(train) [48][400/925] lr: 8.6150e-05 eta: 5:30:43 time: 0.6651 data_time: 0.0029 memory: 11199 grad_norm: 682.3198 loss: 368.2957 loss_cls: 119.4862 loss_bbox: 112.6189 loss_dfl: 136.1906 2024/03/18 01:31:44 - mmengine - INFO - Epoch(train) [48][450/925] lr: 8.6150e-05 eta: 5:30:10 time: 0.6685 data_time: 0.0030 memory: 11505 grad_norm: 670.5736 loss: 373.7286 loss_cls: 121.4584 loss_bbox: 115.6431 loss_dfl: 136.6271 2024/03/18 01:32:18 - mmengine - INFO - Epoch(train) [48][500/925] lr: 8.6150e-05 eta: 5:29:38 time: 0.6713 data_time: 0.0030 memory: 11279 grad_norm: 712.7016 loss: 380.3976 loss_cls: 124.9712 loss_bbox: 117.4079 loss_dfl: 138.0185 2024/03/18 01:32:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:32:50 - mmengine - INFO - Epoch(train) [48][550/925] lr: 8.6150e-05 eta: 5:29:04 time: 0.6516 data_time: 0.0029 memory: 11279 grad_norm: 692.8058 loss: 374.5091 loss_cls: 123.5220 loss_bbox: 114.8403 loss_dfl: 136.1467 2024/03/18 01:33:24 - mmengine - INFO - Epoch(train) [48][600/925] lr: 8.6150e-05 eta: 5:28:32 time: 0.6684 data_time: 0.0031 memory: 11519 grad_norm: inf loss: 370.4331 loss_cls: 119.3687 loss_bbox: 114.7399 loss_dfl: 136.3245 2024/03/18 01:33:57 - mmengine - INFO - Epoch(train) [48][650/925] lr: 8.6150e-05 eta: 5:27:59 time: 0.6646 data_time: 0.0030 memory: 11612 grad_norm: 724.2647 loss: 373.2480 loss_cls: 120.4791 loss_bbox: 116.5191 loss_dfl: 136.2498 2024/03/18 01:34:31 - mmengine - INFO - Epoch(train) [48][700/925] lr: 8.6150e-05 eta: 5:27:27 time: 0.6728 data_time: 0.0030 memory: 11439 grad_norm: 686.0338 loss: 378.5921 loss_cls: 122.9853 loss_bbox: 118.0941 loss_dfl: 137.5127 2024/03/18 01:35:04 - mmengine - INFO - Epoch(train) [48][750/925] lr: 8.6150e-05 eta: 5:26:54 time: 0.6580 data_time: 0.0030 memory: 11639 grad_norm: 658.4522 loss: 377.0246 loss_cls: 121.5009 loss_bbox: 118.1149 loss_dfl: 137.4088 2024/03/18 01:35:36 - mmengine - INFO - Epoch(train) [48][800/925] lr: 8.6150e-05 eta: 5:26:20 time: 0.6481 data_time: 0.0028 memory: 11452 grad_norm: 688.2097 loss: 376.2947 loss_cls: 122.3540 loss_bbox: 116.0735 loss_dfl: 137.8671 2024/03/18 01:36:09 - mmengine - INFO - Epoch(train) [48][850/925] lr: 8.6150e-05 eta: 5:25:47 time: 0.6501 data_time: 0.0028 memory: 11439 grad_norm: 672.6660 loss: 376.5993 loss_cls: 122.8212 loss_bbox: 116.2335 loss_dfl: 137.5446 2024/03/18 01:36:42 - mmengine - INFO - Epoch(train) [48][900/925] lr: 8.6150e-05 eta: 5:25:14 time: 0.6559 data_time: 0.0027 memory: 11265 grad_norm: 708.4586 loss: 373.2913 loss_cls: 119.2165 loss_bbox: 116.7810 loss_dfl: 137.2938 2024/03/18 01:36:58 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:37:37 - mmengine - INFO - Epoch(train) [49][ 50/925] lr: 8.3675e-05 eta: 5:24:28 time: 0.7608 data_time: 0.0753 memory: 11639 grad_norm: 714.8489 loss: 371.2284 loss_cls: 119.5988 loss_bbox: 113.9129 loss_dfl: 137.7166 2024/03/18 01:38:10 - mmengine - INFO - Epoch(train) [49][100/925] lr: 8.3675e-05 eta: 5:23:55 time: 0.6663 data_time: 0.0030 memory: 11759 grad_norm: 718.9093 loss: 376.0784 loss_cls: 123.2948 loss_bbox: 114.7302 loss_dfl: 138.0534 2024/03/18 01:38:44 - mmengine - INFO - Epoch(train) [49][150/925] lr: 8.3675e-05 eta: 5:23:23 time: 0.6802 data_time: 0.0031 memory: 11372 grad_norm: 718.1730 loss: 370.3697 loss_cls: 120.7793 loss_bbox: 112.6642 loss_dfl: 136.9263 2024/03/18 01:39:18 - mmengine - INFO - Epoch(train) [49][200/925] lr: 8.3675e-05 eta: 5:22:51 time: 0.6717 data_time: 0.0028 memory: 11372 grad_norm: 707.7280 loss: 374.4711 loss_cls: 121.5661 loss_bbox: 115.9657 loss_dfl: 136.9392 2024/03/18 01:39:51 - mmengine - INFO - Epoch(train) [49][250/925] lr: 8.3675e-05 eta: 5:22:18 time: 0.6621 data_time: 0.0029 memory: 11319 grad_norm: 713.1185 loss: 375.3419 loss_cls: 122.4777 loss_bbox: 114.7020 loss_dfl: 138.1622 2024/03/18 01:40:25 - mmengine - INFO - Epoch(train) [49][300/925] lr: 8.3675e-05 eta: 5:21:45 time: 0.6742 data_time: 0.0027 memory: 11279 grad_norm: 774.0781 loss: 374.2672 loss_cls: 120.5678 loss_bbox: 115.7597 loss_dfl: 137.9397 2024/03/18 01:40:58 - mmengine - INFO - Epoch(train) [49][350/925] lr: 8.3675e-05 eta: 5:21:12 time: 0.6594 data_time: 0.0028 memory: 11585 grad_norm: 667.4159 loss: 376.3913 loss_cls: 123.5391 loss_bbox: 115.7390 loss_dfl: 137.1133 2024/03/18 01:41:32 - mmengine - INFO - Epoch(train) [49][400/925] lr: 8.3675e-05 eta: 5:20:40 time: 0.6803 data_time: 0.0028 memory: 11265 grad_norm: 734.4819 loss: 372.7421 loss_cls: 120.3278 loss_bbox: 114.4382 loss_dfl: 137.9761 2024/03/18 01:42:06 - mmengine - INFO - Epoch(train) [49][450/925] lr: 8.3675e-05 eta: 5:20:08 time: 0.6803 data_time: 0.0028 memory: 11505 grad_norm: 686.0327 loss: 370.3417 loss_cls: 119.5320 loss_bbox: 114.4040 loss_dfl: 136.4057 2024/03/18 01:42:39 - mmengine - INFO - Epoch(train) [49][500/925] lr: 8.3675e-05 eta: 5:19:35 time: 0.6677 data_time: 0.0030 memory: 11532 grad_norm: 702.1629 loss: 366.9234 loss_cls: 117.6252 loss_bbox: 114.1320 loss_dfl: 135.1662 2024/03/18 01:43:13 - mmengine - INFO - Epoch(train) [49][550/925] lr: 8.3675e-05 eta: 5:19:03 time: 0.6843 data_time: 0.0030 memory: 11692 grad_norm: 752.7790 loss: 368.5860 loss_cls: 117.3526 loss_bbox: 115.2033 loss_dfl: 136.0301 2024/03/18 01:43:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:43:46 - mmengine - INFO - Epoch(train) [49][600/925] lr: 8.3675e-05 eta: 5:18:30 time: 0.6563 data_time: 0.0027 memory: 11332 grad_norm: 697.9299 loss: 373.9603 loss_cls: 120.6647 loss_bbox: 115.3296 loss_dfl: 137.9659 2024/03/18 01:44:19 - mmengine - INFO - Epoch(train) [49][650/925] lr: 8.3675e-05 eta: 5:17:57 time: 0.6614 data_time: 0.0028 memory: 11439 grad_norm: 691.1485 loss: 374.4491 loss_cls: 120.8740 loss_bbox: 115.7846 loss_dfl: 137.7904 2024/03/18 01:44:53 - mmengine - INFO - Epoch(train) [49][700/925] lr: 8.3675e-05 eta: 5:17:25 time: 0.6688 data_time: 0.0030 memory: 11065 grad_norm: 736.5217 loss: 367.2292 loss_cls: 117.2141 loss_bbox: 113.9434 loss_dfl: 136.0717 2024/03/18 01:45:26 - mmengine - INFO - Epoch(train) [49][750/925] lr: 8.3675e-05 eta: 5:16:52 time: 0.6603 data_time: 0.0026 memory: 11412 grad_norm: 703.1642 loss: 370.0042 loss_cls: 119.9950 loss_bbox: 113.9383 loss_dfl: 136.0709 2024/03/18 01:46:00 - mmengine - INFO - Epoch(train) [49][800/925] lr: 8.3675e-05 eta: 5:16:19 time: 0.6756 data_time: 0.0030 memory: 11252 grad_norm: 700.1873 loss: 374.9059 loss_cls: 121.9586 loss_bbox: 115.8485 loss_dfl: 137.0987 2024/03/18 01:46:34 - mmengine - INFO - Epoch(train) [49][850/925] lr: 8.3675e-05 eta: 5:15:47 time: 0.6893 data_time: 0.0031 memory: 11665 grad_norm: 721.4555 loss: 368.7369 loss_cls: 119.0539 loss_bbox: 113.6645 loss_dfl: 136.0184 2024/03/18 01:47:08 - mmengine - INFO - Epoch(train) [49][900/925] lr: 8.3675e-05 eta: 5:15:15 time: 0.6707 data_time: 0.0029 memory: 11239 grad_norm: 790.3605 loss: 372.6315 loss_cls: 120.9051 loss_bbox: 114.2789 loss_dfl: 137.4476 2024/03/18 01:47:24 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:48:01 - mmengine - INFO - Epoch(train) [50][ 50/925] lr: 8.1200e-05 eta: 5:14:27 time: 0.7358 data_time: 0.0718 memory: 11239 grad_norm: 736.8629 loss: 371.0917 loss_cls: 119.1193 loss_bbox: 114.7132 loss_dfl: 137.2593 2024/03/18 01:48:35 - mmengine - INFO - Epoch(train) [50][100/925] lr: 8.1200e-05 eta: 5:13:55 time: 0.6738 data_time: 0.0027 memory: 11679 grad_norm: 723.2529 loss: 373.7568 loss_cls: 121.5721 loss_bbox: 114.5577 loss_dfl: 137.6270 2024/03/18 01:49:09 - mmengine - INFO - Epoch(train) [50][150/925] lr: 8.1200e-05 eta: 5:13:22 time: 0.6787 data_time: 0.0033 memory: 11079 grad_norm: 721.4337 loss: 364.8118 loss_cls: 118.3531 loss_bbox: 110.1464 loss_dfl: 136.3123 2024/03/18 01:49:44 - mmengine - INFO - Epoch(train) [50][200/925] lr: 8.1200e-05 eta: 5:12:51 time: 0.6977 data_time: 0.0031 memory: 12065 grad_norm: 758.6255 loss: 374.0846 loss_cls: 121.7738 loss_bbox: 115.8921 loss_dfl: 136.4188 2024/03/18 01:50:17 - mmengine - INFO - Epoch(train) [50][250/925] lr: 8.1200e-05 eta: 5:12:18 time: 0.6709 data_time: 0.0031 memory: 11212 grad_norm: 709.5777 loss: 382.0491 loss_cls: 126.5512 loss_bbox: 117.2622 loss_dfl: 138.2357 2024/03/18 01:50:51 - mmengine - INFO - Epoch(train) [50][300/925] lr: 8.1200e-05 eta: 5:11:46 time: 0.6730 data_time: 0.0031 memory: 11412 grad_norm: 711.5736 loss: 372.8173 loss_cls: 120.6322 loss_bbox: 114.9793 loss_dfl: 137.2059 2024/03/18 01:51:25 - mmengine - INFO - Epoch(train) [50][350/925] lr: 8.1200e-05 eta: 5:11:13 time: 0.6760 data_time: 0.0028 memory: 11732 grad_norm: 673.4107 loss: 373.8968 loss_cls: 120.6991 loss_bbox: 116.0732 loss_dfl: 137.1244 2024/03/18 01:51:58 - mmengine - INFO - Epoch(train) [50][400/925] lr: 8.1200e-05 eta: 5:10:40 time: 0.6597 data_time: 0.0029 memory: 11172 grad_norm: 721.8052 loss: 373.2983 loss_cls: 121.6601 loss_bbox: 114.6468 loss_dfl: 136.9914 2024/03/18 01:52:31 - mmengine - INFO - Epoch(train) [50][450/925] lr: 8.1200e-05 eta: 5:10:08 time: 0.6681 data_time: 0.0027 memory: 11332 grad_norm: 654.0438 loss: 369.1572 loss_cls: 118.6003 loss_bbox: 114.1571 loss_dfl: 136.3998 2024/03/18 01:53:04 - mmengine - INFO - Epoch(train) [50][500/925] lr: 8.1200e-05 eta: 5:09:34 time: 0.6504 data_time: 0.0028 memory: 11372 grad_norm: 696.2791 loss: 368.1131 loss_cls: 117.9651 loss_bbox: 114.3206 loss_dfl: 135.8274 2024/03/18 01:53:37 - mmengine - INFO - Epoch(train) [50][550/925] lr: 8.1200e-05 eta: 5:09:01 time: 0.6635 data_time: 0.0029 memory: 11599 grad_norm: 731.5925 loss: 371.1005 loss_cls: 120.2699 loss_bbox: 115.6900 loss_dfl: 135.1406 2024/03/18 01:54:11 - mmengine - INFO - Epoch(train) [50][600/925] lr: 8.1200e-05 eta: 5:08:29 time: 0.6811 data_time: 0.0030 memory: 11892 grad_norm: 698.1612 loss: 372.0718 loss_cls: 121.3042 loss_bbox: 114.0295 loss_dfl: 136.7380 2024/03/18 01:54:45 - mmengine - INFO - Epoch(train) [50][650/925] lr: 8.1200e-05 eta: 5:07:57 time: 0.6705 data_time: 0.0029 memory: 11185 grad_norm: 692.1196 loss: 372.1692 loss_cls: 122.3720 loss_bbox: 113.1240 loss_dfl: 136.6732 2024/03/18 01:55:02 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:55:19 - mmengine - INFO - Epoch(train) [50][700/925] lr: 8.1200e-05 eta: 5:07:24 time: 0.6810 data_time: 0.0028 memory: 11319 grad_norm: 775.8291 loss: 375.6309 loss_cls: 122.9623 loss_bbox: 116.0864 loss_dfl: 136.5822 2024/03/18 01:55:52 - mmengine - INFO - Epoch(train) [50][750/925] lr: 8.1200e-05 eta: 5:06:52 time: 0.6680 data_time: 0.0029 memory: 11652 grad_norm: 692.3810 loss: 372.4539 loss_cls: 120.1812 loss_bbox: 115.3947 loss_dfl: 136.8780 2024/03/18 01:56:25 - mmengine - INFO - Epoch(train) [50][800/925] lr: 8.1200e-05 eta: 5:06:19 time: 0.6586 data_time: 0.0027 memory: 11359 grad_norm: 797.7375 loss: 369.5035 loss_cls: 117.8752 loss_bbox: 114.9146 loss_dfl: 136.7137 2024/03/18 01:56:59 - mmengine - INFO - Epoch(train) [50][850/925] lr: 8.1200e-05 eta: 5:05:46 time: 0.6697 data_time: 0.0026 memory: 11132 grad_norm: 700.8952 loss: 367.0312 loss_cls: 117.5307 loss_bbox: 113.7915 loss_dfl: 135.7090 2024/03/18 01:57:32 - mmengine - INFO - Epoch(train) [50][900/925] lr: 8.1200e-05 eta: 5:05:13 time: 0.6694 data_time: 0.0031 memory: 11372 grad_norm: 735.3307 loss: 364.7346 loss_cls: 116.8125 loss_bbox: 111.8753 loss_dfl: 136.0467 2024/03/18 01:57:48 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 01:57:49 - mmengine - INFO - Saving checkpoint at 50 epochs 2024/03/18 01:57:58 - mmengine - INFO - Epoch(val) [50][ 50/625] eta: 0:00:14 time: 0.0244 data_time: 0.0011 memory: 11279 2024/03/18 01:57:59 - mmengine - INFO - Epoch(val) [50][100/625] eta: 0:00:12 time: 0.0244 data_time: 0.0007 memory: 1709 2024/03/18 01:58:00 - mmengine - INFO - Epoch(val) [50][150/625] eta: 0:00:11 time: 0.0236 data_time: 0.0005 memory: 1709 2024/03/18 01:58:01 - mmengine - INFO - Epoch(val) [50][200/625] eta: 0:00:10 time: 0.0229 data_time: 0.0003 memory: 1709 2024/03/18 01:58:06 - mmengine - INFO - Epoch(val) [50][250/625] eta: 0:00:13 time: 0.0884 data_time: 0.0647 memory: 1709 2024/03/18 01:58:07 - mmengine - INFO - Epoch(val) [50][300/625] eta: 0:00:11 time: 0.0235 data_time: 0.0010 memory: 1709 2024/03/18 01:58:08 - mmengine - INFO - Epoch(val) [50][350/625] eta: 0:00:09 time: 0.0219 data_time: 0.0006 memory: 1709 2024/03/18 01:58:09 - mmengine - INFO - Epoch(val) [50][400/625] eta: 0:00:07 time: 0.0218 data_time: 0.0007 memory: 1709 2024/03/18 01:58:10 - mmengine - INFO - Epoch(val) [50][450/625] eta: 0:00:05 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 01:58:11 - mmengine - INFO - Epoch(val) [50][500/625] eta: 0:00:03 time: 0.0213 data_time: 0.0004 memory: 1709 2024/03/18 01:58:12 - mmengine - INFO - Epoch(val) [50][550/625] eta: 0:00:02 time: 0.0212 data_time: 0.0004 memory: 1709 2024/03/18 01:58:13 - mmengine - INFO - Epoch(val) [50][600/625] eta: 0:00:00 time: 0.0217 data_time: 0.0007 memory: 1709 2024/03/18 01:58:24 - mmengine - INFO - Evaluating bbox... 2024/03/18 01:59:26 - mmengine - INFO - bbox_mAP_copypaste: 0.529 0.697 0.576 0.357 0.576 0.686 2024/03/18 01:59:27 - mmengine - INFO - Epoch(val) [50][625/625] coco/bbox_mAP: 0.5290 coco/bbox_mAP_50: 0.6970 coco/bbox_mAP_75: 0.5760 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6860 data_time: 0.0007 time: 0.0217 2024/03/18 02:00:03 - mmengine - INFO - Epoch(train) [51][ 50/925] lr: 7.8725e-05 eta: 5:04:26 time: 0.7293 data_time: 0.0619 memory: 11319 grad_norm: 728.0413 loss: 370.1150 loss_cls: 119.1896 loss_bbox: 114.4219 loss_dfl: 136.5035 2024/03/18 02:00:37 - mmengine - INFO - Epoch(train) [51][100/925] lr: 7.8725e-05 eta: 5:03:53 time: 0.6724 data_time: 0.0029 memory: 11412 grad_norm: inf loss: 374.2713 loss_cls: 120.0823 loss_bbox: 116.4248 loss_dfl: 137.7643 2024/03/18 02:01:11 - mmengine - INFO - Epoch(train) [51][150/925] lr: 7.8725e-05 eta: 5:03:21 time: 0.6715 data_time: 0.0030 memory: 11719 grad_norm: 695.7933 loss: 371.8903 loss_cls: 120.9115 loss_bbox: 113.9384 loss_dfl: 137.0405 2024/03/18 02:01:45 - mmengine - INFO - Epoch(train) [51][200/925] lr: 7.8725e-05 eta: 5:02:48 time: 0.6803 data_time: 0.0033 memory: 11439 grad_norm: 676.4591 loss: 367.2435 loss_cls: 117.6939 loss_bbox: 113.7769 loss_dfl: 135.7727 2024/03/18 02:02:20 - mmengine - INFO - Epoch(train) [51][250/925] lr: 7.8725e-05 eta: 5:02:16 time: 0.6959 data_time: 0.0037 memory: 11425 grad_norm: 737.5903 loss: 371.1327 loss_cls: 119.8173 loss_bbox: 114.0158 loss_dfl: 137.2996 2024/03/18 02:02:53 - mmengine - INFO - Epoch(train) [51][300/925] lr: 7.8725e-05 eta: 5:01:43 time: 0.6585 data_time: 0.0029 memory: 11505 grad_norm: 718.5795 loss: 368.0050 loss_cls: 117.6461 loss_bbox: 113.1378 loss_dfl: 137.2211 2024/03/18 02:03:27 - mmengine - INFO - Epoch(train) [51][350/925] lr: 7.8725e-05 eta: 5:01:11 time: 0.6883 data_time: 0.0031 memory: 11585 grad_norm: 654.0636 loss: 374.0792 loss_cls: 122.0694 loss_bbox: 115.7790 loss_dfl: 136.2309 2024/03/18 02:04:01 - mmengine - INFO - Epoch(train) [51][400/925] lr: 7.8725e-05 eta: 5:00:39 time: 0.6715 data_time: 0.0029 memory: 11585 grad_norm: 707.3136 loss: 373.1590 loss_cls: 120.6168 loss_bbox: 114.5503 loss_dfl: 137.9920 2024/03/18 02:04:34 - mmengine - INFO - Epoch(train) [51][450/925] lr: 7.8725e-05 eta: 5:00:06 time: 0.6647 data_time: 0.0028 memory: 11572 grad_norm: 674.5406 loss: 377.2008 loss_cls: 123.3587 loss_bbox: 115.9600 loss_dfl: 137.8821 2024/03/18 02:05:08 - mmengine - INFO - Epoch(train) [51][500/925] lr: 7.8725e-05 eta: 4:59:33 time: 0.6754 data_time: 0.0027 memory: 11199 grad_norm: 693.1883 loss: 368.1435 loss_cls: 118.7297 loss_bbox: 112.7202 loss_dfl: 136.6935 2024/03/18 02:05:41 - mmengine - INFO - Epoch(train) [51][550/925] lr: 7.8725e-05 eta: 4:59:00 time: 0.6658 data_time: 0.0025 memory: 11305 grad_norm: 697.8976 loss: 373.4407 loss_cls: 120.7145 loss_bbox: 115.7719 loss_dfl: 136.9543 2024/03/18 02:06:15 - mmengine - INFO - Epoch(train) [51][600/925] lr: 7.8725e-05 eta: 4:58:28 time: 0.6779 data_time: 0.0025 memory: 11865 grad_norm: 712.1929 loss: 375.9813 loss_cls: 122.0088 loss_bbox: 116.8103 loss_dfl: 137.1622 2024/03/18 02:06:49 - mmengine - INFO - Epoch(train) [51][650/925] lr: 7.8725e-05 eta: 4:57:56 time: 0.6774 data_time: 0.0029 memory: 11225 grad_norm: 756.2702 loss: 372.5395 loss_cls: 120.6835 loss_bbox: 115.1616 loss_dfl: 136.6943 2024/03/18 02:07:23 - mmengine - INFO - Epoch(train) [51][700/925] lr: 7.8725e-05 eta: 4:57:23 time: 0.6771 data_time: 0.0029 memory: 11252 grad_norm: 779.1006 loss: 370.1215 loss_cls: 119.5222 loss_bbox: 113.7410 loss_dfl: 136.8582 2024/03/18 02:07:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:07:56 - mmengine - INFO - Epoch(train) [51][750/925] lr: 7.8725e-05 eta: 4:56:50 time: 0.6660 data_time: 0.0030 memory: 11545 grad_norm: 696.2982 loss: 366.5387 loss_cls: 118.7207 loss_bbox: 112.0519 loss_dfl: 135.7661 2024/03/18 02:08:29 - mmengine - INFO - Epoch(train) [51][800/925] lr: 7.8725e-05 eta: 4:56:17 time: 0.6640 data_time: 0.0028 memory: 11332 grad_norm: 693.2440 loss: 376.7749 loss_cls: 122.0776 loss_bbox: 117.1629 loss_dfl: 137.5344 2024/03/18 02:09:02 - mmengine - INFO - Epoch(train) [51][850/925] lr: 7.8725e-05 eta: 4:55:44 time: 0.6610 data_time: 0.0029 memory: 11479 grad_norm: 670.8177 loss: 371.8584 loss_cls: 119.8356 loss_bbox: 114.4375 loss_dfl: 137.5853 2024/03/18 02:09:36 - mmengine - INFO - Epoch(train) [51][900/925] lr: 7.8725e-05 eta: 4:55:12 time: 0.6665 data_time: 0.0028 memory: 11385 grad_norm: 691.7319 loss: 368.1640 loss_cls: 119.3805 loss_bbox: 112.4774 loss_dfl: 136.3062 2024/03/18 02:09:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:10:29 - mmengine - INFO - Epoch(train) [52][ 50/925] lr: 7.6250e-05 eta: 4:54:24 time: 0.7304 data_time: 0.0598 memory: 11719 grad_norm: 710.8116 loss: 370.6218 loss_cls: 118.5580 loss_bbox: 114.7207 loss_dfl: 137.3431 2024/03/18 02:11:02 - mmengine - INFO - Epoch(train) [52][100/925] lr: 7.6250e-05 eta: 4:53:51 time: 0.6646 data_time: 0.0030 memory: 11772 grad_norm: 707.2916 loss: 369.7005 loss_cls: 118.5205 loss_bbox: 114.7431 loss_dfl: 136.4369 2024/03/18 02:11:36 - mmengine - INFO - Epoch(train) [52][150/925] lr: 7.6250e-05 eta: 4:53:18 time: 0.6806 data_time: 0.0029 memory: 11079 grad_norm: 723.3062 loss: 372.8537 loss_cls: 121.9395 loss_bbox: 114.0192 loss_dfl: 136.8950 2024/03/18 02:12:10 - mmengine - INFO - Epoch(train) [52][200/925] lr: 7.6250e-05 eta: 4:52:46 time: 0.6709 data_time: 0.0028 memory: 11385 grad_norm: 724.5866 loss: 367.0503 loss_cls: 117.2834 loss_bbox: 114.0084 loss_dfl: 135.7585 2024/03/18 02:12:42 - mmengine - INFO - Epoch(train) [52][250/925] lr: 7.6250e-05 eta: 4:52:13 time: 0.6522 data_time: 0.0026 memory: 11519 grad_norm: 661.4403 loss: 374.1836 loss_cls: 121.5704 loss_bbox: 115.7996 loss_dfl: 136.8136 2024/03/18 02:13:16 - mmengine - INFO - Epoch(train) [52][300/925] lr: 7.6250e-05 eta: 4:51:40 time: 0.6663 data_time: 0.0025 memory: 11319 grad_norm: 724.1748 loss: 367.6158 loss_cls: 118.8698 loss_bbox: 112.4703 loss_dfl: 136.2757 2024/03/18 02:13:49 - mmengine - INFO - Epoch(train) [52][350/925] lr: 7.6250e-05 eta: 4:51:07 time: 0.6627 data_time: 0.0030 memory: 11452 grad_norm: 713.0548 loss: 370.2848 loss_cls: 118.9853 loss_bbox: 115.0067 loss_dfl: 136.2928 2024/03/18 02:14:23 - mmengine - INFO - Epoch(train) [52][400/925] lr: 7.6250e-05 eta: 4:50:35 time: 0.6942 data_time: 0.0030 memory: 11212 grad_norm: 699.1032 loss: 372.7133 loss_cls: 119.6246 loss_bbox: 116.0749 loss_dfl: 137.0138 2024/03/18 02:14:58 - mmengine - INFO - Epoch(train) [52][450/925] lr: 7.6250e-05 eta: 4:50:03 time: 0.6860 data_time: 0.0028 memory: 11199 grad_norm: 722.7715 loss: 374.7216 loss_cls: 121.4288 loss_bbox: 115.8495 loss_dfl: 137.4432 2024/03/18 02:15:31 - mmengine - INFO - Epoch(train) [52][500/925] lr: 7.6250e-05 eta: 4:49:30 time: 0.6610 data_time: 0.0030 memory: 11639 grad_norm: 752.0879 loss: 364.1970 loss_cls: 116.3997 loss_bbox: 112.1219 loss_dfl: 135.6755 2024/03/18 02:16:05 - mmengine - INFO - Epoch(train) [52][550/925] lr: 7.6250e-05 eta: 4:48:57 time: 0.6789 data_time: 0.0032 memory: 11452 grad_norm: 685.5200 loss: 370.6210 loss_cls: 119.8518 loss_bbox: 113.7912 loss_dfl: 136.9780 2024/03/18 02:16:38 - mmengine - INFO - Epoch(train) [52][600/925] lr: 7.6250e-05 eta: 4:48:24 time: 0.6642 data_time: 0.0028 memory: 11319 grad_norm: 723.2965 loss: 369.3152 loss_cls: 117.8744 loss_bbox: 114.9349 loss_dfl: 136.5060 2024/03/18 02:17:12 - mmengine - INFO - Epoch(train) [52][650/925] lr: 7.6250e-05 eta: 4:47:52 time: 0.6718 data_time: 0.0030 memory: 11532 grad_norm: 690.8564 loss: 368.8421 loss_cls: 119.0891 loss_bbox: 113.8368 loss_dfl: 135.9162 2024/03/18 02:17:45 - mmengine - INFO - Epoch(train) [52][700/925] lr: 7.6250e-05 eta: 4:47:19 time: 0.6629 data_time: 0.0028 memory: 11345 grad_norm: 710.8946 loss: 372.8305 loss_cls: 121.3436 loss_bbox: 114.0343 loss_dfl: 137.4526 2024/03/18 02:18:18 - mmengine - INFO - Epoch(train) [52][750/925] lr: 7.6250e-05 eta: 4:46:46 time: 0.6593 data_time: 0.0028 memory: 11439 grad_norm: 755.9836 loss: 373.2703 loss_cls: 119.9397 loss_bbox: 116.2507 loss_dfl: 137.0799 2024/03/18 02:18:52 - mmengine - INFO - Epoch(train) [52][800/925] lr: 7.6250e-05 eta: 4:46:14 time: 0.6896 data_time: 0.0032 memory: 11305 grad_norm: 712.4017 loss: 365.4243 loss_cls: 116.0747 loss_bbox: 113.3085 loss_dfl: 136.0410 2024/03/18 02:19:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:19:26 - mmengine - INFO - Epoch(train) [52][850/925] lr: 7.6250e-05 eta: 4:45:41 time: 0.6688 data_time: 0.0028 memory: 11345 grad_norm: 706.8988 loss: 366.7555 loss_cls: 117.9186 loss_bbox: 113.0150 loss_dfl: 135.8219 2024/03/18 02:19:59 - mmengine - INFO - Epoch(train) [52][900/925] lr: 7.6250e-05 eta: 4:45:08 time: 0.6677 data_time: 0.0027 memory: 11599 grad_norm: 707.9448 loss: 371.0689 loss_cls: 119.9744 loss_bbox: 114.9113 loss_dfl: 136.1832 2024/03/18 02:20:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:20:53 - mmengine - INFO - Epoch(train) [53][ 50/925] lr: 7.3775e-05 eta: 4:44:21 time: 0.7415 data_time: 0.0745 memory: 11212 grad_norm: 668.3086 loss: 374.2245 loss_cls: 122.2802 loss_bbox: 115.1486 loss_dfl: 136.7958 2024/03/18 02:21:27 - mmengine - INFO - Epoch(train) [53][100/925] lr: 7.3775e-05 eta: 4:43:48 time: 0.6637 data_time: 0.0028 memory: 11185 grad_norm: 759.5937 loss: 368.1199 loss_cls: 119.2998 loss_bbox: 112.6800 loss_dfl: 136.1401 2024/03/18 02:22:00 - mmengine - INFO - Epoch(train) [53][150/925] lr: 7.3775e-05 eta: 4:43:15 time: 0.6627 data_time: 0.0028 memory: 11492 grad_norm: 714.2424 loss: 374.9209 loss_cls: 122.2205 loss_bbox: 114.8146 loss_dfl: 137.8858 2024/03/18 02:22:34 - mmengine - INFO - Epoch(train) [53][200/925] lr: 7.3775e-05 eta: 4:42:42 time: 0.6808 data_time: 0.0029 memory: 12065 grad_norm: 747.2184 loss: 369.1709 loss_cls: 118.9040 loss_bbox: 114.1187 loss_dfl: 136.1482 2024/03/18 02:23:07 - mmengine - INFO - Epoch(train) [53][250/925] lr: 7.3775e-05 eta: 4:42:09 time: 0.6673 data_time: 0.0028 memory: 11839 grad_norm: 660.8035 loss: 368.2677 loss_cls: 118.7010 loss_bbox: 113.7270 loss_dfl: 135.8398 2024/03/18 02:23:40 - mmengine - INFO - Epoch(train) [53][300/925] lr: 7.3775e-05 eta: 4:41:37 time: 0.6646 data_time: 0.0029 memory: 11452 grad_norm: inf loss: 367.2891 loss_cls: 117.5787 loss_bbox: 113.5510 loss_dfl: 136.1595 2024/03/18 02:24:16 - mmengine - INFO - Epoch(train) [53][350/925] lr: 7.3775e-05 eta: 4:41:05 time: 0.7003 data_time: 0.0029 memory: 11679 grad_norm: 658.0812 loss: 371.5543 loss_cls: 118.5699 loss_bbox: 116.2854 loss_dfl: 136.6990 2024/03/18 02:24:49 - mmengine - INFO - Epoch(train) [53][400/925] lr: 7.3775e-05 eta: 4:40:32 time: 0.6671 data_time: 0.0028 memory: 11692 grad_norm: 737.9754 loss: 372.8795 loss_cls: 121.0586 loss_bbox: 115.5405 loss_dfl: 136.2803 2024/03/18 02:25:22 - mmengine - INFO - Epoch(train) [53][450/925] lr: 7.3775e-05 eta: 4:39:59 time: 0.6691 data_time: 0.0029 memory: 11812 grad_norm: 754.7584 loss: 372.5732 loss_cls: 119.7658 loss_bbox: 115.6185 loss_dfl: 137.1890 2024/03/18 02:25:57 - mmengine - INFO - Epoch(train) [53][500/925] lr: 7.3775e-05 eta: 4:39:27 time: 0.6850 data_time: 0.0028 memory: 11359 grad_norm: 715.4343 loss: 367.9472 loss_cls: 117.7500 loss_bbox: 114.2759 loss_dfl: 135.9213 2024/03/18 02:26:31 - mmengine - INFO - Epoch(train) [53][550/925] lr: 7.3775e-05 eta: 4:38:54 time: 0.6881 data_time: 0.0028 memory: 11359 grad_norm: 719.6470 loss: 371.3777 loss_cls: 119.5079 loss_bbox: 115.7498 loss_dfl: 136.1200 2024/03/18 02:27:06 - mmengine - INFO - Epoch(train) [53][600/925] lr: 7.3775e-05 eta: 4:38:22 time: 0.6931 data_time: 0.0036 memory: 11732 grad_norm: 683.3247 loss: 360.8800 loss_cls: 114.5506 loss_bbox: 111.4558 loss_dfl: 134.8737 2024/03/18 02:27:41 - mmengine - INFO - Epoch(train) [53][650/925] lr: 7.3775e-05 eta: 4:37:50 time: 0.6967 data_time: 0.0029 memory: 11665 grad_norm: 758.3995 loss: 367.6340 loss_cls: 117.7025 loss_bbox: 113.2774 loss_dfl: 136.6541 2024/03/18 02:28:15 - mmengine - INFO - Epoch(train) [53][700/925] lr: 7.3775e-05 eta: 4:37:18 time: 0.6848 data_time: 0.0032 memory: 11239 grad_norm: 726.5349 loss: 365.5767 loss_cls: 115.4417 loss_bbox: 113.6907 loss_dfl: 136.4443 2024/03/18 02:28:49 - mmengine - INFO - Epoch(train) [53][750/925] lr: 7.3775e-05 eta: 4:36:45 time: 0.6806 data_time: 0.0029 memory: 11159 grad_norm: 750.2640 loss: 369.7439 loss_cls: 118.5332 loss_bbox: 115.1635 loss_dfl: 136.0471 2024/03/18 02:29:22 - mmengine - INFO - Epoch(train) [53][800/925] lr: 7.3775e-05 eta: 4:36:13 time: 0.6663 data_time: 0.0031 memory: 11172 grad_norm: 705.2782 loss: 371.5522 loss_cls: 120.4193 loss_bbox: 115.1220 loss_dfl: 136.0109 2024/03/18 02:29:56 - mmengine - INFO - Epoch(train) [53][850/925] lr: 7.3775e-05 eta: 4:35:40 time: 0.6809 data_time: 0.0027 memory: 11585 grad_norm: 686.2035 loss: 368.6461 loss_cls: 117.3155 loss_bbox: 115.0786 loss_dfl: 136.2520 2024/03/18 02:30:30 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:30:30 - mmengine - INFO - Epoch(train) [53][900/925] lr: 7.3775e-05 eta: 4:35:07 time: 0.6679 data_time: 0.0027 memory: 11252 grad_norm: 676.9993 loss: 369.6988 loss_cls: 119.5444 loss_bbox: 113.5763 loss_dfl: 136.5781 2024/03/18 02:30:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:31:23 - mmengine - INFO - Epoch(train) [54][ 50/925] lr: 7.1300e-05 eta: 4:34:19 time: 0.7217 data_time: 0.0666 memory: 11492 grad_norm: 728.5366 loss: 364.7545 loss_cls: 116.3940 loss_bbox: 112.9213 loss_dfl: 135.4392 2024/03/18 02:31:56 - mmengine - INFO - Epoch(train) [54][100/925] lr: 7.1300e-05 eta: 4:33:46 time: 0.6692 data_time: 0.0030 memory: 11345 grad_norm: 684.3749 loss: 368.4435 loss_cls: 119.0813 loss_bbox: 113.7586 loss_dfl: 135.6036 2024/03/18 02:32:30 - mmengine - INFO - Epoch(train) [54][150/925] lr: 7.1300e-05 eta: 4:33:14 time: 0.6694 data_time: 0.0029 memory: 11239 grad_norm: 650.1409 loss: 366.0512 loss_cls: 116.9066 loss_bbox: 113.7782 loss_dfl: 135.3664 2024/03/18 02:33:02 - mmengine - INFO - Epoch(train) [54][200/925] lr: 7.1300e-05 eta: 4:32:40 time: 0.6489 data_time: 0.0029 memory: 11399 grad_norm: 680.5794 loss: 368.9036 loss_cls: 118.0681 loss_bbox: 115.1182 loss_dfl: 135.7172 2024/03/18 02:33:35 - mmengine - INFO - Epoch(train) [54][250/925] lr: 7.1300e-05 eta: 4:32:07 time: 0.6610 data_time: 0.0029 memory: 11519 grad_norm: 687.4534 loss: 370.6341 loss_cls: 119.4603 loss_bbox: 115.1299 loss_dfl: 136.0438 2024/03/18 02:34:08 - mmengine - INFO - Epoch(train) [54][300/925] lr: 7.1300e-05 eta: 4:31:34 time: 0.6527 data_time: 0.0028 memory: 11199 grad_norm: 731.4539 loss: 367.4250 loss_cls: 117.7488 loss_bbox: 112.3959 loss_dfl: 137.2803 2024/03/18 02:34:41 - mmengine - INFO - Epoch(train) [54][350/925] lr: 7.1300e-05 eta: 4:31:01 time: 0.6523 data_time: 0.0028 memory: 11425 grad_norm: 715.4866 loss: 363.8810 loss_cls: 116.8531 loss_bbox: 112.5802 loss_dfl: 134.4478 2024/03/18 02:35:14 - mmengine - INFO - Epoch(train) [54][400/925] lr: 7.1300e-05 eta: 4:30:28 time: 0.6680 data_time: 0.0028 memory: 11465 grad_norm: 724.3828 loss: 369.5449 loss_cls: 119.3220 loss_bbox: 114.0979 loss_dfl: 136.1249 2024/03/18 02:35:47 - mmengine - INFO - Epoch(train) [54][450/925] lr: 7.1300e-05 eta: 4:29:55 time: 0.6526 data_time: 0.0030 memory: 11279 grad_norm: 714.5895 loss: 367.4525 loss_cls: 118.3814 loss_bbox: 114.1360 loss_dfl: 134.9350 2024/03/18 02:36:20 - mmengine - INFO - Epoch(train) [54][500/925] lr: 7.1300e-05 eta: 4:29:22 time: 0.6572 data_time: 0.0029 memory: 11625 grad_norm: 744.7291 loss: 366.9307 loss_cls: 117.9927 loss_bbox: 113.0392 loss_dfl: 135.8988 2024/03/18 02:36:54 - mmengine - INFO - Epoch(train) [54][550/925] lr: 7.1300e-05 eta: 4:28:49 time: 0.6818 data_time: 0.0029 memory: 12052 grad_norm: 757.8882 loss: 366.7426 loss_cls: 117.7134 loss_bbox: 113.5896 loss_dfl: 135.4396 2024/03/18 02:37:27 - mmengine - INFO - Epoch(train) [54][600/925] lr: 7.1300e-05 eta: 4:28:16 time: 0.6611 data_time: 0.0028 memory: 11559 grad_norm: 716.3082 loss: 369.3793 loss_cls: 118.8558 loss_bbox: 114.2511 loss_dfl: 136.2724 2024/03/18 02:38:00 - mmengine - INFO - Epoch(train) [54][650/925] lr: 7.1300e-05 eta: 4:27:43 time: 0.6621 data_time: 0.0030 memory: 11252 grad_norm: 691.2883 loss: 365.6525 loss_cls: 117.2839 loss_bbox: 113.1579 loss_dfl: 135.2108 2024/03/18 02:38:33 - mmengine - INFO - Epoch(train) [54][700/925] lr: 7.1300e-05 eta: 4:27:10 time: 0.6522 data_time: 0.0028 memory: 11412 grad_norm: 749.8767 loss: 372.6450 loss_cls: 121.9312 loss_bbox: 113.4004 loss_dfl: 137.3134 2024/03/18 02:39:06 - mmengine - INFO - Epoch(train) [54][750/925] lr: 7.1300e-05 eta: 4:26:37 time: 0.6633 data_time: 0.0030 memory: 11412 grad_norm: 674.9191 loss: 364.6956 loss_cls: 116.3701 loss_bbox: 112.6710 loss_dfl: 135.6545 2024/03/18 02:39:41 - mmengine - INFO - Epoch(train) [54][800/925] lr: 7.1300e-05 eta: 4:26:05 time: 0.6982 data_time: 0.0033 memory: 11372 grad_norm: 739.2032 loss: 369.0280 loss_cls: 119.1437 loss_bbox: 113.6414 loss_dfl: 136.2429 2024/03/18 02:40:15 - mmengine - INFO - Epoch(train) [54][850/925] lr: 7.1300e-05 eta: 4:25:32 time: 0.6733 data_time: 0.0035 memory: 11745 grad_norm: 720.1486 loss: 366.5853 loss_cls: 118.1879 loss_bbox: 112.5804 loss_dfl: 135.8170 2024/03/18 02:40:48 - mmengine - INFO - Epoch(train) [54][900/925] lr: 7.1300e-05 eta: 4:24:59 time: 0.6622 data_time: 0.0028 memory: 11385 grad_norm: 704.9077 loss: 364.3835 loss_cls: 116.0688 loss_bbox: 112.7436 loss_dfl: 135.5710 2024/03/18 02:41:04 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:41:41 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:41:41 - mmengine - INFO - Epoch(train) [55][ 50/925] lr: 6.8825e-05 eta: 4:24:11 time: 0.7255 data_time: 0.0699 memory: 11319 grad_norm: 711.6915 loss: 371.9195 loss_cls: 119.1674 loss_bbox: 116.0748 loss_dfl: 136.6774 2024/03/18 02:42:14 - mmengine - INFO - Epoch(train) [55][100/925] lr: 6.8825e-05 eta: 4:23:38 time: 0.6615 data_time: 0.0027 memory: 11452 grad_norm: 774.2867 loss: 372.8474 loss_cls: 120.6683 loss_bbox: 114.9363 loss_dfl: 137.2428 2024/03/18 02:42:47 - mmengine - INFO - Epoch(train) [55][150/925] lr: 6.8825e-05 eta: 4:23:05 time: 0.6562 data_time: 0.0026 memory: 11745 grad_norm: 675.4227 loss: 363.5699 loss_cls: 115.6077 loss_bbox: 112.8555 loss_dfl: 135.1067 2024/03/18 02:43:19 - mmengine - INFO - Epoch(train) [55][200/925] lr: 6.8825e-05 eta: 4:22:32 time: 0.6451 data_time: 0.0027 memory: 11359 grad_norm: 740.0931 loss: 365.4923 loss_cls: 115.4339 loss_bbox: 114.0801 loss_dfl: 135.9783 2024/03/18 02:43:52 - mmengine - INFO - Epoch(train) [55][250/925] lr: 6.8825e-05 eta: 4:21:58 time: 0.6510 data_time: 0.0028 memory: 11332 grad_norm: 746.1611 loss: 361.9710 loss_cls: 114.6985 loss_bbox: 112.1093 loss_dfl: 135.1632 2024/03/18 02:44:25 - mmengine - INFO - Epoch(train) [55][300/925] lr: 6.8825e-05 eta: 4:21:26 time: 0.6669 data_time: 0.0029 memory: 11319 grad_norm: 718.5197 loss: 369.5503 loss_cls: 118.4170 loss_bbox: 115.1003 loss_dfl: 136.0330 2024/03/18 02:44:59 - mmengine - INFO - Epoch(train) [55][350/925] lr: 6.8825e-05 eta: 4:20:53 time: 0.6675 data_time: 0.0030 memory: 11385 grad_norm: 702.8038 loss: 367.2678 loss_cls: 117.3333 loss_bbox: 113.4484 loss_dfl: 136.4861 2024/03/18 02:45:31 - mmengine - INFO - Epoch(train) [55][400/925] lr: 6.8825e-05 eta: 4:20:19 time: 0.6477 data_time: 0.0029 memory: 11519 grad_norm: 750.2459 loss: 371.7581 loss_cls: 119.0009 loss_bbox: 115.7043 loss_dfl: 137.0529 2024/03/18 02:46:04 - mmengine - INFO - Epoch(train) [55][450/925] lr: 6.8825e-05 eta: 4:19:46 time: 0.6627 data_time: 0.0028 memory: 11199 grad_norm: 715.3957 loss: 366.2272 loss_cls: 117.8262 loss_bbox: 112.7692 loss_dfl: 135.6318 2024/03/18 02:46:36 - mmengine - INFO - Epoch(train) [55][500/925] lr: 6.8825e-05 eta: 4:19:13 time: 0.6427 data_time: 0.0028 memory: 11452 grad_norm: 696.3605 loss: 372.4459 loss_cls: 119.1120 loss_bbox: 116.6156 loss_dfl: 136.7182 2024/03/18 02:47:09 - mmengine - INFO - Epoch(train) [55][550/925] lr: 6.8825e-05 eta: 4:18:40 time: 0.6431 data_time: 0.0029 memory: 11612 grad_norm: 708.6707 loss: 370.2588 loss_cls: 117.9233 loss_bbox: 114.9025 loss_dfl: 137.4330 2024/03/18 02:47:41 - mmengine - INFO - Epoch(train) [55][600/925] lr: 6.8825e-05 eta: 4:18:06 time: 0.6540 data_time: 0.0029 memory: 11439 grad_norm: 759.5803 loss: 372.4738 loss_cls: 120.6564 loss_bbox: 114.7843 loss_dfl: 137.0331 2024/03/18 02:48:14 - mmengine - INFO - Epoch(train) [55][650/925] lr: 6.8825e-05 eta: 4:17:33 time: 0.6589 data_time: 0.0031 memory: 11172 grad_norm: 702.3002 loss: 363.1209 loss_cls: 114.8885 loss_bbox: 112.5374 loss_dfl: 135.6950 2024/03/18 02:48:46 - mmengine - INFO - Epoch(train) [55][700/925] lr: 6.8825e-05 eta: 4:17:00 time: 0.6377 data_time: 0.0029 memory: 11399 grad_norm: inf loss: 366.7329 loss_cls: 117.8453 loss_bbox: 112.5924 loss_dfl: 136.2951 2024/03/18 02:49:19 - mmengine - INFO - Epoch(train) [55][750/925] lr: 6.8825e-05 eta: 4:16:26 time: 0.6452 data_time: 0.0031 memory: 11585 grad_norm: 719.5874 loss: 374.3068 loss_cls: 121.3834 loss_bbox: 115.3410 loss_dfl: 137.5824 2024/03/18 02:49:51 - mmengine - INFO - Epoch(train) [55][800/925] lr: 6.8825e-05 eta: 4:15:53 time: 0.6497 data_time: 0.0029 memory: 11839 grad_norm: 732.3395 loss: 368.9649 loss_cls: 118.2439 loss_bbox: 114.8952 loss_dfl: 135.8258 2024/03/18 02:50:23 - mmengine - INFO - Epoch(train) [55][850/925] lr: 6.8825e-05 eta: 4:15:20 time: 0.6459 data_time: 0.0029 memory: 11239 grad_norm: 725.8440 loss: 358.4768 loss_cls: 113.3962 loss_bbox: 111.5524 loss_dfl: 133.5282 2024/03/18 02:50:55 - mmengine - INFO - Epoch(train) [55][900/925] lr: 6.8825e-05 eta: 4:14:46 time: 0.6353 data_time: 0.0024 memory: 11492 grad_norm: 716.9303 loss: 366.5888 loss_cls: 119.9224 loss_bbox: 111.1563 loss_dfl: 135.5102 2024/03/18 02:51:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:51:12 - mmengine - INFO - Saving checkpoint at 55 epochs 2024/03/18 02:51:21 - mmengine - INFO - Epoch(val) [55][ 50/625] eta: 0:00:15 time: 0.0261 data_time: 0.0027 memory: 11692 2024/03/18 02:51:22 - mmengine - INFO - Epoch(val) [55][100/625] eta: 0:00:13 time: 0.0246 data_time: 0.0013 memory: 1709 2024/03/18 02:51:24 - mmengine - INFO - Epoch(val) [55][150/625] eta: 0:00:12 time: 0.0252 data_time: 0.0018 memory: 1709 2024/03/18 02:51:25 - mmengine - INFO - Epoch(val) [55][200/625] eta: 0:00:10 time: 0.0244 data_time: 0.0009 memory: 1709 2024/03/18 02:51:26 - mmengine - INFO - Epoch(val) [55][250/625] eta: 0:00:09 time: 0.0247 data_time: 0.0012 memory: 1709 2024/03/18 02:51:27 - mmengine - INFO - Epoch(val) [55][300/625] eta: 0:00:08 time: 0.0248 data_time: 0.0012 memory: 1709 2024/03/18 02:51:29 - mmengine - INFO - Epoch(val) [55][350/625] eta: 0:00:06 time: 0.0274 data_time: 0.0043 memory: 1709 2024/03/18 02:51:30 - mmengine - INFO - Epoch(val) [55][400/625] eta: 0:00:05 time: 0.0235 data_time: 0.0006 memory: 1709 2024/03/18 02:51:31 - mmengine - INFO - Epoch(val) [55][450/625] eta: 0:00:04 time: 0.0252 data_time: 0.0019 memory: 1709 2024/03/18 02:51:32 - mmengine - INFO - Epoch(val) [55][500/625] eta: 0:00:03 time: 0.0243 data_time: 0.0011 memory: 1709 2024/03/18 02:51:33 - mmengine - INFO - Epoch(val) [55][550/625] eta: 0:00:01 time: 0.0247 data_time: 0.0013 memory: 1709 2024/03/18 02:51:35 - mmengine - INFO - Epoch(val) [55][600/625] eta: 0:00:00 time: 0.0241 data_time: 0.0008 memory: 1709 2024/03/18 02:51:46 - mmengine - INFO - Evaluating bbox... 2024/03/18 02:52:52 - mmengine - INFO - bbox_mAP_copypaste: 0.530 0.697 0.577 0.359 0.576 0.687 2024/03/18 02:52:54 - mmengine - INFO - Epoch(val) [55][625/625] coco/bbox_mAP: 0.5300 coco/bbox_mAP_50: 0.6970 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5760 coco/bbox_mAP_l: 0.6870 data_time: 0.0019 time: 0.0250 2024/03/18 02:53:31 - mmengine - INFO - Epoch(train) [56][ 50/925] lr: 6.6350e-05 eta: 4:13:58 time: 0.7247 data_time: 0.0758 memory: 11145 grad_norm: 747.8551 loss: 365.2720 loss_cls: 116.8043 loss_bbox: 112.6916 loss_dfl: 135.7761 2024/03/18 02:54:03 - mmengine - INFO - Epoch(train) [56][100/925] lr: 6.6350e-05 eta: 4:13:24 time: 0.6388 data_time: 0.0028 memory: 11639 grad_norm: 736.5943 loss: 368.6802 loss_cls: 119.4791 loss_bbox: 113.4421 loss_dfl: 135.7590 2024/03/18 02:54:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 02:54:35 - mmengine - INFO - Epoch(train) [56][150/925] lr: 6.6350e-05 eta: 4:12:51 time: 0.6428 data_time: 0.0031 memory: 11545 grad_norm: 735.7609 loss: 371.9942 loss_cls: 120.5482 loss_bbox: 114.9906 loss_dfl: 136.4555 2024/03/18 02:55:07 - mmengine - INFO - Epoch(train) [56][200/925] lr: 6.6350e-05 eta: 4:12:18 time: 0.6499 data_time: 0.0031 memory: 11145 grad_norm: 722.6625 loss: 364.9856 loss_cls: 115.7169 loss_bbox: 114.0484 loss_dfl: 135.2203 2024/03/18 02:55:54 - mmengine - INFO - Epoch(train) [56][250/925] lr: 6.6350e-05 eta: 4:11:51 time: 0.9279 data_time: 0.2072 memory: 11239 grad_norm: 713.0996 loss: 361.8776 loss_cls: 114.1837 loss_bbox: 112.2826 loss_dfl: 135.4112 2024/03/18 02:56:41 - mmengine - INFO - Epoch(train) [56][300/925] lr: 6.6350e-05 eta: 4:11:24 time: 0.9405 data_time: 0.0654 memory: 11345 grad_norm: 745.9563 loss: 361.9392 loss_cls: 112.6882 loss_bbox: 113.3581 loss_dfl: 135.8929 2024/03/18 02:57:13 - mmengine - INFO - Epoch(train) [56][350/925] lr: 6.6350e-05 eta: 4:10:50 time: 0.6400 data_time: 0.0028 memory: 11332 grad_norm: 720.6983 loss: 367.1519 loss_cls: 116.8496 loss_bbox: 113.8705 loss_dfl: 136.4318 2024/03/18 02:57:49 - mmengine - INFO - Epoch(train) [56][400/925] lr: 6.6350e-05 eta: 4:10:19 time: 0.7203 data_time: 0.0027 memory: 11492 grad_norm: 743.2140 loss: 364.3795 loss_cls: 114.9043 loss_bbox: 114.0910 loss_dfl: 135.3841 2024/03/18 02:58:21 - mmengine - INFO - Epoch(train) [56][450/925] lr: 6.6350e-05 eta: 4:09:45 time: 0.6368 data_time: 0.0031 memory: 11225 grad_norm: 763.5684 loss: 370.2063 loss_cls: 119.1259 loss_bbox: 114.3240 loss_dfl: 136.7563 2024/03/18 02:58:53 - mmengine - INFO - Epoch(train) [56][500/925] lr: 6.6350e-05 eta: 4:09:12 time: 0.6476 data_time: 0.0029 memory: 11399 grad_norm: 706.0935 loss: 368.7377 loss_cls: 117.1154 loss_bbox: 114.7338 loss_dfl: 136.8885 2024/03/18 02:59:25 - mmengine - INFO - Epoch(train) [56][550/925] lr: 6.6350e-05 eta: 4:08:38 time: 0.6428 data_time: 0.0028 memory: 11239 grad_norm: 710.6309 loss: 362.0888 loss_cls: 116.2136 loss_bbox: 111.5953 loss_dfl: 134.2799 2024/03/18 02:59:57 - mmengine - INFO - Epoch(train) [56][600/925] lr: 6.6350e-05 eta: 4:08:04 time: 0.6247 data_time: 0.0028 memory: 11425 grad_norm: 750.9302 loss: 366.3918 loss_cls: 117.2102 loss_bbox: 113.6602 loss_dfl: 135.5214 2024/03/18 03:00:29 - mmengine - INFO - Epoch(train) [56][650/925] lr: 6.6350e-05 eta: 4:07:31 time: 0.6392 data_time: 0.0078 memory: 12185 grad_norm: 697.3504 loss: 369.6982 loss_cls: 118.5136 loss_bbox: 114.7847 loss_dfl: 136.3999 2024/03/18 03:01:01 - mmengine - INFO - Epoch(train) [56][700/925] lr: 6.6350e-05 eta: 4:06:57 time: 0.6400 data_time: 0.0029 memory: 11105 grad_norm: 699.2130 loss: 366.8922 loss_cls: 117.8970 loss_bbox: 112.8358 loss_dfl: 136.1595 2024/03/18 03:01:32 - mmengine - INFO - Epoch(train) [56][750/925] lr: 6.6350e-05 eta: 4:06:24 time: 0.6316 data_time: 0.0028 memory: 11332 grad_norm: 705.0331 loss: 370.0080 loss_cls: 118.5093 loss_bbox: 114.1015 loss_dfl: 137.3972 2024/03/18 03:02:04 - mmengine - INFO - Epoch(train) [56][800/925] lr: 6.6350e-05 eta: 4:05:50 time: 0.6306 data_time: 0.0026 memory: 11185 grad_norm: 745.3236 loss: 365.7554 loss_cls: 116.8258 loss_bbox: 113.0361 loss_dfl: 135.8935 2024/03/18 03:02:36 - mmengine - INFO - Epoch(train) [56][850/925] lr: 6.6350e-05 eta: 4:05:17 time: 0.6424 data_time: 0.0030 memory: 11279 grad_norm: 710.8414 loss: 366.2707 loss_cls: 116.3753 loss_bbox: 113.8695 loss_dfl: 136.0259 2024/03/18 03:03:08 - mmengine - INFO - Epoch(train) [56][900/925] lr: 6.6350e-05 eta: 4:04:43 time: 0.6377 data_time: 0.0029 memory: 11452 grad_norm: 702.5368 loss: 364.3489 loss_cls: 117.5029 loss_bbox: 111.0996 loss_dfl: 135.7464 2024/03/18 03:03:23 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:03:59 - mmengine - INFO - Epoch(train) [57][ 50/925] lr: 6.3875e-05 eta: 4:03:54 time: 0.7075 data_time: 0.0734 memory: 11492 grad_norm: 707.1435 loss: 364.0161 loss_cls: 116.5537 loss_bbox: 112.4877 loss_dfl: 134.9747 2024/03/18 03:04:31 - mmengine - INFO - Epoch(train) [57][100/925] lr: 6.3875e-05 eta: 4:03:21 time: 0.6442 data_time: 0.0027 memory: 11585 grad_norm: 699.5594 loss: 364.4675 loss_cls: 115.9374 loss_bbox: 112.4386 loss_dfl: 136.0915 2024/03/18 03:05:03 - mmengine - INFO - Epoch(train) [57][150/925] lr: 6.3875e-05 eta: 4:02:47 time: 0.6304 data_time: 0.0027 memory: 11452 grad_norm: 728.9349 loss: 364.4222 loss_cls: 115.9931 loss_bbox: 112.3590 loss_dfl: 136.0702 2024/03/18 03:05:35 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:05:35 - mmengine - INFO - Epoch(train) [57][200/925] lr: 6.3875e-05 eta: 4:02:13 time: 0.6358 data_time: 0.0028 memory: 11399 grad_norm: inf loss: 366.5619 loss_cls: 116.2108 loss_bbox: 114.2280 loss_dfl: 136.1231 2024/03/18 03:06:07 - mmengine - INFO - Epoch(train) [57][250/925] lr: 6.3875e-05 eta: 4:01:40 time: 0.6403 data_time: 0.0029 memory: 11465 grad_norm: 701.2002 loss: 365.9218 loss_cls: 115.7571 loss_bbox: 113.3435 loss_dfl: 136.8212 2024/03/18 03:06:38 - mmengine - INFO - Epoch(train) [57][300/925] lr: 6.3875e-05 eta: 4:01:06 time: 0.6304 data_time: 0.0029 memory: 11425 grad_norm: 718.5304 loss: 364.4745 loss_cls: 116.1259 loss_bbox: 112.6511 loss_dfl: 135.6975 2024/03/18 03:07:10 - mmengine - INFO - Epoch(train) [57][350/925] lr: 6.3875e-05 eta: 4:00:33 time: 0.6435 data_time: 0.0028 memory: 11505 grad_norm: 738.3726 loss: 369.9231 loss_cls: 118.1417 loss_bbox: 114.9245 loss_dfl: 136.8569 2024/03/18 03:07:43 - mmengine - INFO - Epoch(train) [57][400/925] lr: 6.3875e-05 eta: 3:59:59 time: 0.6424 data_time: 0.0028 memory: 11412 grad_norm: 757.3172 loss: 366.7152 loss_cls: 117.2060 loss_bbox: 113.2145 loss_dfl: 136.2947 2024/03/18 03:08:15 - mmengine - INFO - Epoch(train) [57][450/925] lr: 6.3875e-05 eta: 3:59:26 time: 0.6378 data_time: 0.0029 memory: 11532 grad_norm: 734.6940 loss: 364.3985 loss_cls: 116.4725 loss_bbox: 112.0097 loss_dfl: 135.9164 2024/03/18 03:08:47 - mmengine - INFO - Epoch(train) [57][500/925] lr: 6.3875e-05 eta: 3:58:52 time: 0.6411 data_time: 0.0027 memory: 11252 grad_norm: 726.8242 loss: 367.5944 loss_cls: 118.1930 loss_bbox: 112.4745 loss_dfl: 136.9269 2024/03/18 03:09:19 - mmengine - INFO - Epoch(train) [57][550/925] lr: 6.3875e-05 eta: 3:58:19 time: 0.6450 data_time: 0.0028 memory: 11545 grad_norm: 732.1033 loss: 366.0109 loss_cls: 117.4511 loss_bbox: 112.9408 loss_dfl: 135.6190 2024/03/18 03:09:51 - mmengine - INFO - Epoch(train) [57][600/925] lr: 6.3875e-05 eta: 3:57:46 time: 0.6406 data_time: 0.0028 memory: 11052 grad_norm: 736.7018 loss: 359.1325 loss_cls: 114.5529 loss_bbox: 110.0453 loss_dfl: 134.5342 2024/03/18 03:10:23 - mmengine - INFO - Epoch(train) [57][650/925] lr: 6.3875e-05 eta: 3:57:12 time: 0.6382 data_time: 0.0028 memory: 11452 grad_norm: 751.5646 loss: 369.9882 loss_cls: 119.7404 loss_bbox: 113.6080 loss_dfl: 136.6398 2024/03/18 03:10:55 - mmengine - INFO - Epoch(train) [57][700/925] lr: 6.3875e-05 eta: 3:56:39 time: 0.6334 data_time: 0.0029 memory: 11612 grad_norm: 762.7675 loss: 365.7057 loss_cls: 116.7843 loss_bbox: 113.0990 loss_dfl: 135.8224 2024/03/18 03:11:27 - mmengine - INFO - Epoch(train) [57][750/925] lr: 6.3875e-05 eta: 3:56:05 time: 0.6401 data_time: 0.0026 memory: 11305 grad_norm: 766.6757 loss: 365.9713 loss_cls: 117.4651 loss_bbox: 112.6433 loss_dfl: 135.8628 2024/03/18 03:11:58 - mmengine - INFO - Epoch(train) [57][800/925] lr: 6.3875e-05 eta: 3:55:32 time: 0.6336 data_time: 0.0027 memory: 11705 grad_norm: 757.2224 loss: 367.1103 loss_cls: 118.0961 loss_bbox: 113.8412 loss_dfl: 135.1731 2024/03/18 03:12:30 - mmengine - INFO - Epoch(train) [57][850/925] lr: 6.3875e-05 eta: 3:54:58 time: 0.6312 data_time: 0.0030 memory: 11305 grad_norm: 753.0891 loss: 366.2300 loss_cls: 117.6853 loss_bbox: 112.3766 loss_dfl: 136.1680 2024/03/18 03:13:02 - mmengine - INFO - Epoch(train) [57][900/925] lr: 6.3875e-05 eta: 3:54:25 time: 0.6457 data_time: 0.0030 memory: 11452 grad_norm: 703.5083 loss: 363.6345 loss_cls: 114.2376 loss_bbox: 113.0065 loss_dfl: 136.3904 2024/03/18 03:13:18 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:13:52 - mmengine - INFO - Epoch(train) [58][ 50/925] lr: 6.1400e-05 eta: 3:53:35 time: 0.6813 data_time: 0.0629 memory: 11439 grad_norm: 751.8802 loss: 362.3378 loss_cls: 113.8167 loss_bbox: 112.6946 loss_dfl: 135.8265 2024/03/18 03:14:24 - mmengine - INFO - Epoch(train) [58][100/925] lr: 6.1400e-05 eta: 3:53:02 time: 0.6282 data_time: 0.0030 memory: 11399 grad_norm: 711.2177 loss: 365.5522 loss_cls: 115.3211 loss_bbox: 114.2031 loss_dfl: 136.0280 2024/03/18 03:14:55 - mmengine - INFO - Epoch(train) [58][150/925] lr: 6.1400e-05 eta: 3:52:28 time: 0.6290 data_time: 0.0030 memory: 11439 grad_norm: 786.8835 loss: 368.6911 loss_cls: 118.5309 loss_bbox: 114.4310 loss_dfl: 135.7292 2024/03/18 03:15:27 - mmengine - INFO - Epoch(train) [58][200/925] lr: 6.1400e-05 eta: 3:51:54 time: 0.6283 data_time: 0.0029 memory: 11319 grad_norm: 753.2555 loss: 367.4192 loss_cls: 117.7085 loss_bbox: 113.5889 loss_dfl: 136.1217 2024/03/18 03:15:58 - mmengine - INFO - Epoch(train) [58][250/925] lr: 6.1400e-05 eta: 3:51:21 time: 0.6204 data_time: 0.0028 memory: 11292 grad_norm: 737.5085 loss: 360.8532 loss_cls: 112.2817 loss_bbox: 112.7623 loss_dfl: 135.8092 2024/03/18 03:16:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:16:29 - mmengine - INFO - Epoch(train) [58][300/925] lr: 6.1400e-05 eta: 3:50:47 time: 0.6258 data_time: 0.0030 memory: 11239 grad_norm: 806.6691 loss: 369.5149 loss_cls: 118.5366 loss_bbox: 114.0465 loss_dfl: 136.9318 2024/03/18 03:17:01 - mmengine - INFO - Epoch(train) [58][350/925] lr: 6.1400e-05 eta: 3:50:13 time: 0.6270 data_time: 0.0030 memory: 11572 grad_norm: 754.5204 loss: 363.8023 loss_cls: 116.3728 loss_bbox: 112.9644 loss_dfl: 134.4651 2024/03/18 03:17:32 - mmengine - INFO - Epoch(train) [58][400/925] lr: 6.1400e-05 eta: 3:49:40 time: 0.6283 data_time: 0.0028 memory: 11319 grad_norm: 750.6895 loss: 370.8445 loss_cls: 118.4088 loss_bbox: 115.5221 loss_dfl: 136.9136 2024/03/18 03:18:03 - mmengine - INFO - Epoch(train) [58][450/925] lr: 6.1400e-05 eta: 3:49:06 time: 0.6268 data_time: 0.0029 memory: 11399 grad_norm: 775.8166 loss: 364.4782 loss_cls: 115.6843 loss_bbox: 112.8541 loss_dfl: 135.9398 2024/03/18 03:18:35 - mmengine - INFO - Epoch(train) [58][500/925] lr: 6.1400e-05 eta: 3:48:32 time: 0.6236 data_time: 0.0029 memory: 11239 grad_norm: 729.6654 loss: 359.7210 loss_cls: 113.7826 loss_bbox: 111.0507 loss_dfl: 134.8877 2024/03/18 03:19:06 - mmengine - INFO - Epoch(train) [58][550/925] lr: 6.1400e-05 eta: 3:47:58 time: 0.6253 data_time: 0.0029 memory: 11145 grad_norm: 831.6366 loss: 370.0902 loss_cls: 118.9412 loss_bbox: 114.1665 loss_dfl: 136.9825 2024/03/18 03:19:38 - mmengine - INFO - Epoch(train) [58][600/925] lr: 6.1400e-05 eta: 3:47:25 time: 0.6308 data_time: 0.0042 memory: 11492 grad_norm: 765.4206 loss: 363.5352 loss_cls: 116.5779 loss_bbox: 111.4000 loss_dfl: 135.5574 2024/03/18 03:20:09 - mmengine - INFO - Epoch(train) [58][650/925] lr: 6.1400e-05 eta: 3:46:51 time: 0.6262 data_time: 0.0027 memory: 11172 grad_norm: 763.2882 loss: 360.0755 loss_cls: 113.1841 loss_bbox: 112.0912 loss_dfl: 134.8002 2024/03/18 03:20:40 - mmengine - INFO - Epoch(train) [58][700/925] lr: 6.1400e-05 eta: 3:46:17 time: 0.6144 data_time: 0.0027 memory: 11185 grad_norm: 748.1875 loss: 364.0635 loss_cls: 116.5796 loss_bbox: 111.5876 loss_dfl: 135.8964 2024/03/18 03:21:11 - mmengine - INFO - Epoch(train) [58][750/925] lr: 6.1400e-05 eta: 3:45:44 time: 0.6303 data_time: 0.0027 memory: 11585 grad_norm: 706.9129 loss: 367.6590 loss_cls: 117.6102 loss_bbox: 113.8373 loss_dfl: 136.2115 2024/03/18 03:21:43 - mmengine - INFO - Epoch(train) [58][800/925] lr: 6.1400e-05 eta: 3:45:10 time: 0.6254 data_time: 0.0028 memory: 11399 grad_norm: 719.9931 loss: 364.7328 loss_cls: 116.7528 loss_bbox: 113.1758 loss_dfl: 134.8042 2024/03/18 03:22:14 - mmengine - INFO - Epoch(train) [58][850/925] lr: 6.1400e-05 eta: 3:44:37 time: 0.6307 data_time: 0.0029 memory: 11119 grad_norm: 687.4284 loss: 367.1997 loss_cls: 117.4778 loss_bbox: 113.6409 loss_dfl: 136.0810 2024/03/18 03:22:46 - mmengine - INFO - Epoch(train) [58][900/925] lr: 6.1400e-05 eta: 3:44:03 time: 0.6332 data_time: 0.0028 memory: 11612 grad_norm: 778.5052 loss: 367.7135 loss_cls: 117.6905 loss_bbox: 113.4337 loss_dfl: 136.5893 2024/03/18 03:23:01 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:23:36 - mmengine - INFO - Epoch(train) [59][ 50/925] lr: 5.8925e-05 eta: 3:43:14 time: 0.6878 data_time: 0.0543 memory: 11279 grad_norm: 695.8329 loss: 364.9337 loss_cls: 114.7984 loss_bbox: 114.6748 loss_dfl: 135.4605 2024/03/18 03:24:07 - mmengine - INFO - Epoch(train) [59][100/925] lr: 5.8925e-05 eta: 3:42:40 time: 0.6222 data_time: 0.0031 memory: 11412 grad_norm: 729.3885 loss: 364.7238 loss_cls: 115.2003 loss_bbox: 114.0582 loss_dfl: 135.4653 2024/03/18 03:24:39 - mmengine - INFO - Epoch(train) [59][150/925] lr: 5.8925e-05 eta: 3:42:06 time: 0.6321 data_time: 0.0028 memory: 11239 grad_norm: 694.3065 loss: 362.7143 loss_cls: 115.5955 loss_bbox: 112.1961 loss_dfl: 134.9227 2024/03/18 03:25:10 - mmengine - INFO - Epoch(train) [59][200/925] lr: 5.8925e-05 eta: 3:41:33 time: 0.6256 data_time: 0.0031 memory: 11279 grad_norm: 725.2738 loss: 366.3041 loss_cls: 116.6567 loss_bbox: 114.3259 loss_dfl: 135.3214 2024/03/18 03:25:41 - mmengine - INFO - Epoch(train) [59][250/925] lr: 5.8925e-05 eta: 3:40:59 time: 0.6226 data_time: 0.0028 memory: 11159 grad_norm: 803.8115 loss: 367.1817 loss_cls: 117.5917 loss_bbox: 112.9867 loss_dfl: 136.6033 2024/03/18 03:26:13 - mmengine - INFO - Epoch(train) [59][300/925] lr: 5.8925e-05 eta: 3:40:26 time: 0.6372 data_time: 0.0032 memory: 11585 grad_norm: 733.9537 loss: 362.3414 loss_cls: 114.7762 loss_bbox: 112.4059 loss_dfl: 135.1593 2024/03/18 03:26:44 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:26:44 - mmengine - INFO - Epoch(train) [59][350/925] lr: 5.8925e-05 eta: 3:39:52 time: 0.6230 data_time: 0.0030 memory: 11492 grad_norm: 737.4238 loss: 361.8007 loss_cls: 113.9160 loss_bbox: 113.0214 loss_dfl: 134.8632 2024/03/18 03:27:16 - mmengine - INFO - Epoch(train) [59][400/925] lr: 5.8925e-05 eta: 3:39:18 time: 0.6273 data_time: 0.0026 memory: 11599 grad_norm: 705.5615 loss: 368.0009 loss_cls: 116.6611 loss_bbox: 114.5015 loss_dfl: 136.8383 2024/03/18 03:27:47 - mmengine - INFO - Epoch(train) [59][450/925] lr: 5.8925e-05 eta: 3:38:45 time: 0.6273 data_time: 0.0029 memory: 11905 grad_norm: 758.9698 loss: 360.0561 loss_cls: 112.4888 loss_bbox: 111.9690 loss_dfl: 135.5983 2024/03/18 03:28:18 - mmengine - INFO - Epoch(train) [59][500/925] lr: 5.8925e-05 eta: 3:38:11 time: 0.6266 data_time: 0.0030 memory: 11625 grad_norm: 716.8138 loss: 366.0188 loss_cls: 116.5791 loss_bbox: 113.2660 loss_dfl: 136.1737 2024/03/18 03:28:50 - mmengine - INFO - Epoch(train) [59][550/925] lr: 5.8925e-05 eta: 3:37:38 time: 0.6348 data_time: 0.0030 memory: 11425 grad_norm: 715.5639 loss: 370.1045 loss_cls: 120.4200 loss_bbox: 113.7394 loss_dfl: 135.9450 2024/03/18 03:29:21 - mmengine - INFO - Epoch(train) [59][600/925] lr: 5.8925e-05 eta: 3:37:04 time: 0.6236 data_time: 0.0026 memory: 11212 grad_norm: 761.4738 loss: 362.7482 loss_cls: 115.7532 loss_bbox: 112.0721 loss_dfl: 134.9230 2024/03/18 03:29:53 - mmengine - INFO - Epoch(train) [59][650/925] lr: 5.8925e-05 eta: 3:36:31 time: 0.6293 data_time: 0.0029 memory: 11359 grad_norm: 772.1987 loss: 362.4948 loss_cls: 115.2503 loss_bbox: 111.7051 loss_dfl: 135.5394 2024/03/18 03:30:24 - mmengine - INFO - Epoch(train) [59][700/925] lr: 5.8925e-05 eta: 3:35:57 time: 0.6267 data_time: 0.0031 memory: 11239 grad_norm: 745.7079 loss: 363.2968 loss_cls: 114.4124 loss_bbox: 113.2891 loss_dfl: 135.5953 2024/03/18 03:30:55 - mmengine - INFO - Epoch(train) [59][750/925] lr: 5.8925e-05 eta: 3:35:23 time: 0.6212 data_time: 0.0030 memory: 11172 grad_norm: 719.2982 loss: 361.2927 loss_cls: 114.0505 loss_bbox: 111.7172 loss_dfl: 135.5250 2024/03/18 03:31:27 - mmengine - INFO - Epoch(train) [59][800/925] lr: 5.8925e-05 eta: 3:34:50 time: 0.6310 data_time: 0.0028 memory: 11745 grad_norm: 735.1368 loss: 368.5318 loss_cls: 118.8392 loss_bbox: 114.3559 loss_dfl: 135.3366 2024/03/18 03:31:58 - mmengine - INFO - Epoch(train) [59][850/925] lr: 5.8925e-05 eta: 3:34:16 time: 0.6281 data_time: 0.0029 memory: 11372 grad_norm: 791.4090 loss: 363.3186 loss_cls: 114.9920 loss_bbox: 112.3310 loss_dfl: 135.9956 2024/03/18 03:32:30 - mmengine - INFO - Epoch(train) [59][900/925] lr: 5.8925e-05 eta: 3:33:43 time: 0.6221 data_time: 0.0031 memory: 11599 grad_norm: 762.4843 loss: 360.2356 loss_cls: 112.5134 loss_bbox: 112.5995 loss_dfl: 135.1227 2024/03/18 03:32:45 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:33:20 - mmengine - INFO - Epoch(train) [60][ 50/925] lr: 5.6450e-05 eta: 3:32:54 time: 0.6877 data_time: 0.0568 memory: 11372 grad_norm: inf loss: 369.5141 loss_cls: 117.6292 loss_bbox: 114.6371 loss_dfl: 137.2478 2024/03/18 03:33:51 - mmengine - INFO - Epoch(train) [60][100/925] lr: 5.6450e-05 eta: 3:32:20 time: 0.6280 data_time: 0.0029 memory: 11412 grad_norm: 744.6071 loss: 372.4564 loss_cls: 119.4988 loss_bbox: 115.6837 loss_dfl: 137.2738 2024/03/18 03:34:23 - mmengine - INFO - Epoch(train) [60][150/925] lr: 5.6450e-05 eta: 3:31:46 time: 0.6217 data_time: 0.0028 memory: 11492 grad_norm: 732.7315 loss: 373.1386 loss_cls: 118.7750 loss_bbox: 116.1853 loss_dfl: 138.1782 2024/03/18 03:34:54 - mmengine - INFO - Epoch(train) [60][200/925] lr: 5.6450e-05 eta: 3:31:13 time: 0.6309 data_time: 0.0031 memory: 11345 grad_norm: 733.2230 loss: 364.5500 loss_cls: 115.0072 loss_bbox: 114.2200 loss_dfl: 135.3228 2024/03/18 03:35:25 - mmengine - INFO - Epoch(train) [60][250/925] lr: 5.6450e-05 eta: 3:30:39 time: 0.6209 data_time: 0.0031 memory: 11292 grad_norm: 700.5854 loss: 368.0696 loss_cls: 117.7436 loss_bbox: 113.7073 loss_dfl: 136.6187 2024/03/18 03:35:57 - mmengine - INFO - Epoch(train) [60][300/925] lr: 5.6450e-05 eta: 3:30:06 time: 0.6245 data_time: 0.0031 memory: 11199 grad_norm: 769.8247 loss: 365.4577 loss_cls: 116.8837 loss_bbox: 112.6539 loss_dfl: 135.9202 2024/03/18 03:36:28 - mmengine - INFO - Epoch(train) [60][350/925] lr: 5.6450e-05 eta: 3:29:32 time: 0.6382 data_time: 0.0029 memory: 11479 grad_norm: 781.6420 loss: 362.1478 loss_cls: 115.1039 loss_bbox: 111.9572 loss_dfl: 135.0867 2024/03/18 03:37:00 - mmengine - INFO - Epoch(train) [60][400/925] lr: 5.6450e-05 eta: 3:28:59 time: 0.6231 data_time: 0.0032 memory: 11279 grad_norm: 694.4570 loss: 368.4983 loss_cls: 118.1164 loss_bbox: 113.4304 loss_dfl: 136.9515 2024/03/18 03:37:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:37:31 - mmengine - INFO - Epoch(train) [60][450/925] lr: 5.6450e-05 eta: 3:28:25 time: 0.6332 data_time: 0.0029 memory: 11372 grad_norm: 767.6224 loss: 362.3985 loss_cls: 114.4203 loss_bbox: 112.0560 loss_dfl: 135.9223 2024/03/18 03:38:03 - mmengine - INFO - Epoch(train) [60][500/925] lr: 5.6450e-05 eta: 3:27:52 time: 0.6289 data_time: 0.0031 memory: 11705 grad_norm: 762.0962 loss: 360.2775 loss_cls: 113.2979 loss_bbox: 112.2002 loss_dfl: 134.7794 2024/03/18 03:38:34 - mmengine - INFO - Epoch(train) [60][550/925] lr: 5.6450e-05 eta: 3:27:19 time: 0.6286 data_time: 0.0031 memory: 11545 grad_norm: 751.2588 loss: 368.7327 loss_cls: 118.1024 loss_bbox: 114.4033 loss_dfl: 136.2270 2024/03/18 03:39:06 - mmengine - INFO - Epoch(train) [60][600/925] lr: 5.6450e-05 eta: 3:26:45 time: 0.6342 data_time: 0.0030 memory: 11599 grad_norm: 747.2512 loss: 362.4769 loss_cls: 113.9281 loss_bbox: 113.3286 loss_dfl: 135.2202 2024/03/18 03:39:37 - mmengine - INFO - Epoch(train) [60][650/925] lr: 5.6450e-05 eta: 3:26:12 time: 0.6243 data_time: 0.0027 memory: 11452 grad_norm: 733.8472 loss: 365.7624 loss_cls: 116.2837 loss_bbox: 113.8248 loss_dfl: 135.6539 2024/03/18 03:40:09 - mmengine - INFO - Epoch(train) [60][700/925] lr: 5.6450e-05 eta: 3:25:38 time: 0.6252 data_time: 0.0029 memory: 11105 grad_norm: 698.8471 loss: 367.9128 loss_cls: 118.3615 loss_bbox: 112.4154 loss_dfl: 137.1359 2024/03/18 03:40:40 - mmengine - INFO - Epoch(train) [60][750/925] lr: 5.6450e-05 eta: 3:25:05 time: 0.6243 data_time: 0.0025 memory: 11519 grad_norm: 756.7310 loss: 366.8903 loss_cls: 117.1885 loss_bbox: 114.5319 loss_dfl: 135.1700 2024/03/18 03:41:11 - mmengine - INFO - Epoch(train) [60][800/925] lr: 5.6450e-05 eta: 3:24:31 time: 0.6161 data_time: 0.0027 memory: 11479 grad_norm: 759.6254 loss: 370.4096 loss_cls: 118.9828 loss_bbox: 115.2846 loss_dfl: 136.1421 2024/03/18 03:41:43 - mmengine - INFO - Epoch(train) [60][850/925] lr: 5.6450e-05 eta: 3:23:58 time: 0.6473 data_time: 0.0030 memory: 11572 grad_norm: 771.7035 loss: 361.6638 loss_cls: 115.8901 loss_bbox: 110.6624 loss_dfl: 135.1113 2024/03/18 03:42:15 - mmengine - INFO - Epoch(train) [60][900/925] lr: 5.6450e-05 eta: 3:23:24 time: 0.6339 data_time: 0.0031 memory: 11505 grad_norm: 746.5788 loss: 364.9867 loss_cls: 114.6631 loss_bbox: 113.5418 loss_dfl: 136.7818 2024/03/18 03:42:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:42:31 - mmengine - INFO - Saving checkpoint at 60 epochs 2024/03/18 03:42:40 - mmengine - INFO - Epoch(val) [60][ 50/625] eta: 0:00:14 time: 0.0257 data_time: 0.0009 memory: 11332 2024/03/18 03:42:41 - mmengine - INFO - Epoch(val) [60][100/625] eta: 0:00:13 time: 0.0265 data_time: 0.0003 memory: 1709 2024/03/18 03:42:42 - mmengine - INFO - Epoch(val) [60][150/625] eta: 0:00:12 time: 0.0268 data_time: 0.0003 memory: 1709 2024/03/18 03:42:44 - mmengine - INFO - Epoch(val) [60][200/625] eta: 0:00:11 time: 0.0272 data_time: 0.0003 memory: 1709 2024/03/18 03:42:45 - mmengine - INFO - Epoch(val) [60][250/625] eta: 0:00:10 time: 0.0279 data_time: 0.0003 memory: 1709 2024/03/18 03:42:46 - mmengine - INFO - Epoch(val) [60][300/625] eta: 0:00:08 time: 0.0257 data_time: 0.0003 memory: 1709 2024/03/18 03:42:47 - mmengine - INFO - Epoch(val) [60][350/625] eta: 0:00:07 time: 0.0234 data_time: 0.0002 memory: 1709 2024/03/18 03:42:49 - mmengine - INFO - Epoch(val) [60][400/625] eta: 0:00:05 time: 0.0236 data_time: 0.0003 memory: 1709 2024/03/18 03:42:50 - mmengine - INFO - Epoch(val) [60][450/625] eta: 0:00:04 time: 0.0238 data_time: 0.0003 memory: 1709 2024/03/18 03:42:51 - mmengine - INFO - Epoch(val) [60][500/625] eta: 0:00:03 time: 0.0239 data_time: 0.0003 memory: 1709 2024/03/18 03:42:52 - mmengine - INFO - Epoch(val) [60][550/625] eta: 0:00:01 time: 0.0238 data_time: 0.0003 memory: 1709 2024/03/18 03:42:53 - mmengine - INFO - Epoch(val) [60][600/625] eta: 0:00:00 time: 0.0240 data_time: 0.0003 memory: 1709 2024/03/18 03:43:05 - mmengine - INFO - Evaluating bbox... 2024/03/18 03:44:17 - mmengine - INFO - bbox_mAP_copypaste: 0.530 0.698 0.578 0.358 0.577 0.688 2024/03/18 03:44:19 - mmengine - INFO - Epoch(val) [60][625/625] coco/bbox_mAP: 0.5300 coco/bbox_mAP_50: 0.6980 coco/bbox_mAP_75: 0.5780 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5770 coco/bbox_mAP_l: 0.6880 data_time: 0.0003 time: 0.0238 2024/03/18 03:44:54 - mmengine - INFO - Epoch(train) [61][ 50/925] lr: 5.3975e-05 eta: 3:22:35 time: 0.7031 data_time: 0.0705 memory: 11759 grad_norm: 752.9502 loss: 367.2205 loss_cls: 117.8421 loss_bbox: 114.0330 loss_dfl: 135.3454 2024/03/18 03:45:27 - mmengine - INFO - Epoch(train) [61][100/925] lr: 5.3975e-05 eta: 3:22:02 time: 0.6454 data_time: 0.0031 memory: 11279 grad_norm: 757.6010 loss: 363.0853 loss_cls: 114.8010 loss_bbox: 112.5829 loss_dfl: 135.7014 2024/03/18 03:45:59 - mmengine - INFO - Epoch(train) [61][150/925] lr: 5.3975e-05 eta: 3:21:29 time: 0.6403 data_time: 0.0032 memory: 11519 grad_norm: 736.4522 loss: 365.1140 loss_cls: 115.8100 loss_bbox: 113.5840 loss_dfl: 135.7200 2024/03/18 03:46:30 - mmengine - INFO - Epoch(train) [61][200/925] lr: 5.3975e-05 eta: 3:20:56 time: 0.6283 data_time: 0.0031 memory: 11345 grad_norm: 705.0756 loss: 366.9783 loss_cls: 117.5539 loss_bbox: 113.3028 loss_dfl: 136.1215 2024/03/18 03:47:03 - mmengine - INFO - Epoch(train) [61][250/925] lr: 5.3975e-05 eta: 3:20:23 time: 0.6668 data_time: 0.0030 memory: 11212 grad_norm: 784.8629 loss: 363.6764 loss_cls: 115.5351 loss_bbox: 112.1530 loss_dfl: 135.9884 2024/03/18 03:47:36 - mmengine - INFO - Epoch(train) [61][300/925] lr: 5.3975e-05 eta: 3:19:50 time: 0.6579 data_time: 0.0034 memory: 11252 grad_norm: 694.1362 loss: 367.6110 loss_cls: 117.9779 loss_bbox: 113.0920 loss_dfl: 136.5411 2024/03/18 03:48:09 - mmengine - INFO - Epoch(train) [61][350/925] lr: 5.3975e-05 eta: 3:19:17 time: 0.6434 data_time: 0.0031 memory: 11519 grad_norm: 697.1392 loss: 367.1833 loss_cls: 116.5617 loss_bbox: 114.3799 loss_dfl: 136.2417 2024/03/18 03:48:41 - mmengine - INFO - Epoch(train) [61][400/925] lr: 5.3975e-05 eta: 3:18:44 time: 0.6545 data_time: 0.0030 memory: 11292 grad_norm: 774.4590 loss: 361.9110 loss_cls: 114.4500 loss_bbox: 111.4269 loss_dfl: 136.0341 2024/03/18 03:49:13 - mmengine - INFO - Epoch(train) [61][450/925] lr: 5.3975e-05 eta: 3:18:10 time: 0.6360 data_time: 0.0029 memory: 11399 grad_norm: 664.6283 loss: 365.6150 loss_cls: 115.6797 loss_bbox: 114.2828 loss_dfl: 135.6525 2024/03/18 03:49:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:49:46 - mmengine - INFO - Epoch(train) [61][500/925] lr: 5.3975e-05 eta: 3:17:37 time: 0.6538 data_time: 0.0032 memory: 11145 grad_norm: 783.7046 loss: 358.1544 loss_cls: 113.4734 loss_bbox: 110.3225 loss_dfl: 134.3585 2024/03/18 03:50:18 - mmengine - INFO - Epoch(train) [61][550/925] lr: 5.3975e-05 eta: 3:17:04 time: 0.6478 data_time: 0.0030 memory: 11425 grad_norm: 760.8683 loss: 369.2500 loss_cls: 117.0103 loss_bbox: 114.9589 loss_dfl: 137.2809 2024/03/18 03:50:51 - mmengine - INFO - Epoch(train) [61][600/925] lr: 5.3975e-05 eta: 3:16:31 time: 0.6473 data_time: 0.0030 memory: 11279 grad_norm: 732.0964 loss: 363.8974 loss_cls: 115.9271 loss_bbox: 112.1819 loss_dfl: 135.7884 2024/03/18 03:51:23 - mmengine - INFO - Epoch(train) [61][650/925] lr: 5.3975e-05 eta: 3:15:58 time: 0.6436 data_time: 0.0029 memory: 11759 grad_norm: 711.3880 loss: 364.5938 loss_cls: 116.4568 loss_bbox: 113.1744 loss_dfl: 134.9626 2024/03/18 03:51:56 - mmengine - INFO - Epoch(train) [61][700/925] lr: 5.3975e-05 eta: 3:15:25 time: 0.6497 data_time: 0.0033 memory: 11465 grad_norm: 794.7165 loss: 368.6994 loss_cls: 117.7751 loss_bbox: 114.8179 loss_dfl: 136.1064 2024/03/18 03:52:29 - mmengine - INFO - Epoch(train) [61][750/925] lr: 5.3975e-05 eta: 3:14:52 time: 0.6648 data_time: 0.0032 memory: 11532 grad_norm: 756.7918 loss: 365.1847 loss_cls: 116.9354 loss_bbox: 113.5203 loss_dfl: 134.7290 2024/03/18 03:53:02 - mmengine - INFO - Epoch(train) [61][800/925] lr: 5.3975e-05 eta: 3:14:19 time: 0.6526 data_time: 0.0032 memory: 11399 grad_norm: 700.1119 loss: 367.0106 loss_cls: 115.3272 loss_bbox: 115.8067 loss_dfl: 135.8766 2024/03/18 03:53:34 - mmengine - INFO - Epoch(train) [61][850/925] lr: 5.3975e-05 eta: 3:13:46 time: 0.6403 data_time: 0.0032 memory: 11359 grad_norm: 691.2244 loss: 364.1240 loss_cls: 114.6885 loss_bbox: 113.8077 loss_dfl: 135.6278 2024/03/18 03:54:07 - mmengine - INFO - Epoch(train) [61][900/925] lr: 5.3975e-05 eta: 3:13:13 time: 0.6587 data_time: 0.0030 memory: 11292 grad_norm: 788.0577 loss: 363.4373 loss_cls: 115.3823 loss_bbox: 113.0806 loss_dfl: 134.9744 2024/03/18 03:54:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 03:54:59 - mmengine - INFO - Epoch(train) [62][ 50/925] lr: 5.1500e-05 eta: 3:12:24 time: 0.7234 data_time: 0.0733 memory: 11265 grad_norm: 737.8325 loss: 358.5749 loss_cls: 112.5515 loss_bbox: 111.2920 loss_dfl: 134.7314 2024/03/18 03:55:31 - mmengine - INFO - Epoch(train) [62][100/925] lr: 5.1500e-05 eta: 3:11:51 time: 0.6471 data_time: 0.0031 memory: 11399 grad_norm: 757.8782 loss: 364.2569 loss_cls: 115.1440 loss_bbox: 113.0653 loss_dfl: 136.0476 2024/03/18 03:56:05 - mmengine - INFO - Epoch(train) [62][150/925] lr: 5.1500e-05 eta: 3:11:18 time: 0.6695 data_time: 0.0031 memory: 11425 grad_norm: 695.6323 loss: 365.3371 loss_cls: 116.1928 loss_bbox: 112.8421 loss_dfl: 136.3022 2024/03/18 03:56:38 - mmengine - INFO - Epoch(train) [62][200/925] lr: 5.1500e-05 eta: 3:10:45 time: 0.6714 data_time: 0.0033 memory: 11252 grad_norm: 746.3261 loss: 363.0157 loss_cls: 115.3693 loss_bbox: 112.2617 loss_dfl: 135.3847 2024/03/18 03:57:11 - mmengine - INFO - Epoch(train) [62][250/925] lr: 5.1500e-05 eta: 3:10:12 time: 0.6476 data_time: 0.0029 memory: 11265 grad_norm: 776.3310 loss: 367.5138 loss_cls: 116.0611 loss_bbox: 115.0829 loss_dfl: 136.3699 2024/03/18 03:57:43 - mmengine - INFO - Epoch(train) [62][300/925] lr: 5.1500e-05 eta: 3:09:39 time: 0.6501 data_time: 0.0029 memory: 11345 grad_norm: 761.9862 loss: 361.2023 loss_cls: 114.3391 loss_bbox: 112.1288 loss_dfl: 134.7344 2024/03/18 03:58:16 - mmengine - INFO - Epoch(train) [62][350/925] lr: 5.1500e-05 eta: 3:09:06 time: 0.6509 data_time: 0.0030 memory: 11252 grad_norm: 720.0068 loss: 364.5136 loss_cls: 117.5866 loss_bbox: 112.1333 loss_dfl: 134.7937 2024/03/18 03:58:48 - mmengine - INFO - Epoch(train) [62][400/925] lr: 5.1500e-05 eta: 3:08:33 time: 0.6471 data_time: 0.0030 memory: 11239 grad_norm: 739.3474 loss: 367.1977 loss_cls: 116.0541 loss_bbox: 113.9971 loss_dfl: 137.1465 2024/03/18 03:59:21 - mmengine - INFO - Epoch(train) [62][450/925] lr: 5.1500e-05 eta: 3:08:00 time: 0.6528 data_time: 0.0031 memory: 11545 grad_norm: 692.5987 loss: 359.4898 loss_cls: 113.2015 loss_bbox: 112.0979 loss_dfl: 134.1904 2024/03/18 03:59:53 - mmengine - INFO - Epoch(train) [62][500/925] lr: 5.1500e-05 eta: 3:07:27 time: 0.6508 data_time: 0.0028 memory: 11559 grad_norm: 744.0856 loss: 359.2496 loss_cls: 113.7002 loss_bbox: 110.6144 loss_dfl: 134.9351 2024/03/18 04:00:27 - mmengine - INFO - Epoch(train) [62][550/925] lr: 5.1500e-05 eta: 3:06:54 time: 0.6645 data_time: 0.0032 memory: 11505 grad_norm: 773.4700 loss: 365.5146 loss_cls: 117.1652 loss_bbox: 112.4865 loss_dfl: 135.8629 2024/03/18 04:00:42 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:00:59 - mmengine - INFO - Epoch(train) [62][600/925] lr: 5.1500e-05 eta: 3:06:21 time: 0.6523 data_time: 0.0028 memory: 11425 grad_norm: 753.7746 loss: 364.3229 loss_cls: 116.7933 loss_bbox: 112.0138 loss_dfl: 135.5157 2024/03/18 04:01:32 - mmengine - INFO - Epoch(train) [62][650/925] lr: 5.1500e-05 eta: 3:05:48 time: 0.6570 data_time: 0.0026 memory: 11399 grad_norm: 755.6160 loss: 357.4962 loss_cls: 112.7244 loss_bbox: 110.6698 loss_dfl: 134.1020 2024/03/18 04:02:04 - mmengine - INFO - Epoch(train) [62][700/925] lr: 5.1500e-05 eta: 3:05:15 time: 0.6461 data_time: 0.0029 memory: 11225 grad_norm: 765.9739 loss: 362.8300 loss_cls: 114.9595 loss_bbox: 112.1307 loss_dfl: 135.7399 2024/03/18 04:02:37 - mmengine - INFO - Epoch(train) [62][750/925] lr: 5.1500e-05 eta: 3:04:42 time: 0.6533 data_time: 0.0032 memory: 11105 grad_norm: 718.2154 loss: 362.1832 loss_cls: 113.9941 loss_bbox: 112.0933 loss_dfl: 136.0959 2024/03/18 04:03:12 - mmengine - INFO - Epoch(train) [62][800/925] lr: 5.1500e-05 eta: 3:04:10 time: 0.6879 data_time: 0.0033 memory: 11145 grad_norm: 738.7373 loss: 361.4733 loss_cls: 112.6674 loss_bbox: 113.3056 loss_dfl: 135.5003 2024/03/18 04:03:45 - mmengine - INFO - Epoch(train) [62][850/925] lr: 5.1500e-05 eta: 3:03:37 time: 0.6682 data_time: 0.0034 memory: 11079 grad_norm: 731.8170 loss: 355.7944 loss_cls: 111.0033 loss_bbox: 109.9146 loss_dfl: 134.8765 2024/03/18 04:04:18 - mmengine - INFO - Epoch(train) [62][900/925] lr: 5.1500e-05 eta: 3:03:04 time: 0.6478 data_time: 0.0031 memory: 11359 grad_norm: 704.6562 loss: 360.2363 loss_cls: 113.5527 loss_bbox: 111.6648 loss_dfl: 135.0188 2024/03/18 04:04:33 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:05:10 - mmengine - INFO - Epoch(train) [63][ 50/925] lr: 4.9025e-05 eta: 3:02:15 time: 0.7218 data_time: 0.0517 memory: 11279 grad_norm: 741.6551 loss: 362.8389 loss_cls: 113.8147 loss_bbox: 112.5224 loss_dfl: 136.5018 2024/03/18 04:05:43 - mmengine - INFO - Epoch(train) [63][100/925] lr: 4.9025e-05 eta: 3:01:42 time: 0.6516 data_time: 0.0030 memory: 11199 grad_norm: 802.2690 loss: 358.2818 loss_cls: 113.5628 loss_bbox: 110.1232 loss_dfl: 134.5958 2024/03/18 04:06:15 - mmengine - INFO - Epoch(train) [63][150/925] lr: 4.9025e-05 eta: 3:01:09 time: 0.6519 data_time: 0.0032 memory: 11505 grad_norm: 725.2852 loss: 362.0132 loss_cls: 114.1101 loss_bbox: 112.0216 loss_dfl: 135.8816 2024/03/18 04:06:50 - mmengine - INFO - Epoch(train) [63][200/925] lr: 4.9025e-05 eta: 3:00:36 time: 0.6831 data_time: 0.0034 memory: 11399 grad_norm: 751.1839 loss: 368.6220 loss_cls: 117.9602 loss_bbox: 114.4649 loss_dfl: 136.1968 2024/03/18 04:07:23 - mmengine - INFO - Epoch(train) [63][250/925] lr: 4.9025e-05 eta: 3:00:04 time: 0.6715 data_time: 0.0028 memory: 11145 grad_norm: 724.6586 loss: 366.3606 loss_cls: 116.8415 loss_bbox: 113.3352 loss_dfl: 136.1838 2024/03/18 04:07:56 - mmengine - INFO - Epoch(train) [63][300/925] lr: 4.9025e-05 eta: 2:59:31 time: 0.6573 data_time: 0.0031 memory: 11345 grad_norm: 774.6477 loss: 362.6634 loss_cls: 115.5285 loss_bbox: 111.9766 loss_dfl: 135.1583 2024/03/18 04:08:29 - mmengine - INFO - Epoch(train) [63][350/925] lr: 4.9025e-05 eta: 2:58:58 time: 0.6605 data_time: 0.0032 memory: 11385 grad_norm: 711.2039 loss: 360.6700 loss_cls: 112.3535 loss_bbox: 113.6194 loss_dfl: 134.6972 2024/03/18 04:09:03 - mmengine - INFO - Epoch(train) [63][400/925] lr: 4.9025e-05 eta: 2:58:25 time: 0.6675 data_time: 0.0031 memory: 12492 grad_norm: 760.2184 loss: 365.3390 loss_cls: 116.0877 loss_bbox: 113.7571 loss_dfl: 135.4942 2024/03/18 04:09:35 - mmengine - INFO - Epoch(train) [63][450/925] lr: 4.9025e-05 eta: 2:57:52 time: 0.6467 data_time: 0.0031 memory: 11399 grad_norm: 754.5229 loss: 356.9426 loss_cls: 111.3887 loss_bbox: 111.0085 loss_dfl: 134.5453 2024/03/18 04:10:08 - mmengine - INFO - Epoch(train) [63][500/925] lr: 4.9025e-05 eta: 2:57:19 time: 0.6514 data_time: 0.0032 memory: 11346 grad_norm: 738.6127 loss: 357.9424 loss_cls: 113.8231 loss_bbox: 109.6810 loss_dfl: 134.4383 2024/03/18 04:10:41 - mmengine - INFO - Epoch(train) [63][550/925] lr: 4.9025e-05 eta: 2:56:46 time: 0.6776 data_time: 0.0031 memory: 11586 grad_norm: 722.3620 loss: 365.1919 loss_cls: 116.6874 loss_bbox: 113.1756 loss_dfl: 135.3290 2024/03/18 04:11:15 - mmengine - INFO - Epoch(train) [63][600/925] lr: 4.9025e-05 eta: 2:56:13 time: 0.6654 data_time: 0.0035 memory: 11426 grad_norm: 695.9639 loss: 357.6895 loss_cls: 111.4156 loss_bbox: 111.5712 loss_dfl: 134.7028 2024/03/18 04:11:47 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:11:47 - mmengine - INFO - Epoch(train) [63][650/925] lr: 4.9025e-05 eta: 2:55:40 time: 0.6526 data_time: 0.0030 memory: 11239 grad_norm: 783.0657 loss: 361.8235 loss_cls: 113.7529 loss_bbox: 113.3063 loss_dfl: 134.7642 2024/03/18 04:12:21 - mmengine - INFO - Epoch(train) [63][700/925] lr: 4.9025e-05 eta: 2:55:07 time: 0.6617 data_time: 0.0030 memory: 11346 grad_norm: 758.3338 loss: 362.9924 loss_cls: 114.1499 loss_bbox: 113.2733 loss_dfl: 135.5692 2024/03/18 04:12:54 - mmengine - INFO - Epoch(train) [63][750/925] lr: 4.9025e-05 eta: 2:54:34 time: 0.6613 data_time: 0.0031 memory: 11373 grad_norm: 765.8423 loss: 365.1870 loss_cls: 117.4227 loss_bbox: 111.7630 loss_dfl: 136.0013 2024/03/18 04:13:26 - mmengine - INFO - Epoch(train) [63][800/925] lr: 4.9025e-05 eta: 2:54:01 time: 0.6519 data_time: 0.0029 memory: 11373 grad_norm: 764.5968 loss: 357.5063 loss_cls: 112.8143 loss_bbox: 110.4122 loss_dfl: 134.2799 2024/03/18 04:14:00 - mmengine - INFO - Epoch(train) [63][850/925] lr: 4.9025e-05 eta: 2:53:29 time: 0.6792 data_time: 0.0035 memory: 11239 grad_norm: 714.9595 loss: 360.1037 loss_cls: 113.8902 loss_bbox: 111.3214 loss_dfl: 134.8921 2024/03/18 04:14:35 - mmengine - INFO - Epoch(train) [63][900/925] lr: 4.9025e-05 eta: 2:52:56 time: 0.6890 data_time: 0.0039 memory: 11213 grad_norm: 719.9085 loss: 362.4203 loss_cls: 115.2082 loss_bbox: 112.1175 loss_dfl: 135.0947 2024/03/18 04:14:50 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:15:27 - mmengine - INFO - Epoch(train) [64][ 50/925] lr: 4.6550e-05 eta: 2:52:07 time: 0.7316 data_time: 0.0689 memory: 11293 grad_norm: 756.1573 loss: 364.9036 loss_cls: 114.1179 loss_bbox: 114.9389 loss_dfl: 135.8468 2024/03/18 04:16:00 - mmengine - INFO - Epoch(train) [64][100/925] lr: 4.6550e-05 eta: 2:51:34 time: 0.6566 data_time: 0.0029 memory: 11666 grad_norm: 683.8138 loss: 361.4427 loss_cls: 113.6598 loss_bbox: 113.0532 loss_dfl: 134.7297 2024/03/18 04:16:33 - mmengine - INFO - Epoch(train) [64][150/925] lr: 4.6550e-05 eta: 2:51:01 time: 0.6514 data_time: 0.0029 memory: 11333 grad_norm: 761.3887 loss: 360.4659 loss_cls: 113.5881 loss_bbox: 112.4358 loss_dfl: 134.4420 2024/03/18 04:17:05 - mmengine - INFO - Epoch(train) [64][200/925] lr: 4.6550e-05 eta: 2:50:28 time: 0.6426 data_time: 0.0030 memory: 11506 grad_norm: 748.6557 loss: 363.7337 loss_cls: 114.6696 loss_bbox: 114.0778 loss_dfl: 134.9862 2024/03/18 04:17:38 - mmengine - INFO - Epoch(train) [64][250/925] lr: 4.6550e-05 eta: 2:49:55 time: 0.6519 data_time: 0.0031 memory: 11399 grad_norm: 725.9628 loss: 360.8910 loss_cls: 112.3073 loss_bbox: 113.6304 loss_dfl: 134.9534 2024/03/18 04:18:11 - mmengine - INFO - Epoch(train) [64][300/925] lr: 4.6550e-05 eta: 2:49:22 time: 0.6669 data_time: 0.0035 memory: 11386 grad_norm: 757.5396 loss: 356.7714 loss_cls: 113.2353 loss_bbox: 109.9165 loss_dfl: 133.6196 2024/03/18 04:18:44 - mmengine - INFO - Epoch(train) [64][350/925] lr: 4.6550e-05 eta: 2:48:49 time: 0.6527 data_time: 0.0033 memory: 11226 grad_norm: 709.8336 loss: 363.4219 loss_cls: 114.8993 loss_bbox: 113.0601 loss_dfl: 135.4625 2024/03/18 04:19:17 - mmengine - INFO - Epoch(train) [64][400/925] lr: 4.6550e-05 eta: 2:48:16 time: 0.6602 data_time: 0.0032 memory: 11253 grad_norm: 784.4775 loss: 362.6464 loss_cls: 113.7204 loss_bbox: 113.7217 loss_dfl: 135.2043 2024/03/18 04:19:50 - mmengine - INFO - Epoch(train) [64][450/925] lr: 4.6550e-05 eta: 2:47:43 time: 0.6559 data_time: 0.0030 memory: 11346 grad_norm: 776.1948 loss: 360.3491 loss_cls: 113.7134 loss_bbox: 111.7528 loss_dfl: 134.8829 2024/03/18 04:20:22 - mmengine - INFO - Epoch(train) [64][500/925] lr: 4.6550e-05 eta: 2:47:10 time: 0.6375 data_time: 0.0028 memory: 11559 grad_norm: 801.2153 loss: 362.5186 loss_cls: 114.5482 loss_bbox: 113.0599 loss_dfl: 134.9105 2024/03/18 04:20:54 - mmengine - INFO - Epoch(train) [64][550/925] lr: 4.6550e-05 eta: 2:46:37 time: 0.6491 data_time: 0.0029 memory: 11733 grad_norm: 717.1760 loss: 364.4671 loss_cls: 114.8244 loss_bbox: 113.9514 loss_dfl: 135.6913 2024/03/18 04:21:27 - mmengine - INFO - Epoch(train) [64][600/925] lr: 4.6550e-05 eta: 2:46:04 time: 0.6593 data_time: 0.0031 memory: 11613 grad_norm: 714.9366 loss: 362.3199 loss_cls: 114.2589 loss_bbox: 112.3898 loss_dfl: 135.6712 2024/03/18 04:21:59 - mmengine - INFO - Epoch(train) [64][650/925] lr: 4.6550e-05 eta: 2:45:31 time: 0.6395 data_time: 0.0031 memory: 11493 grad_norm: 746.2598 loss: 353.0320 loss_cls: 108.4310 loss_bbox: 110.8653 loss_dfl: 133.7358 2024/03/18 04:22:33 - mmengine - INFO - Epoch(train) [64][700/925] lr: 4.6550e-05 eta: 2:44:58 time: 0.6707 data_time: 0.0031 memory: 11333 grad_norm: 732.6017 loss: 361.4900 loss_cls: 114.7937 loss_bbox: 110.8069 loss_dfl: 135.8894 2024/03/18 04:22:49 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:23:06 - mmengine - INFO - Epoch(train) [64][750/925] lr: 4.6550e-05 eta: 2:44:25 time: 0.6576 data_time: 0.0030 memory: 11746 grad_norm: 755.7728 loss: 362.3824 loss_cls: 113.6850 loss_bbox: 113.0170 loss_dfl: 135.6805 2024/03/18 04:23:39 - mmengine - INFO - Epoch(train) [64][800/925] lr: 4.6550e-05 eta: 2:43:52 time: 0.6594 data_time: 0.0030 memory: 11746 grad_norm: 731.8021 loss: 357.6187 loss_cls: 112.0214 loss_bbox: 111.3038 loss_dfl: 134.2935 2024/03/18 04:24:11 - mmengine - INFO - Epoch(train) [64][850/925] lr: 4.6550e-05 eta: 2:43:19 time: 0.6430 data_time: 0.0030 memory: 11213 grad_norm: 707.1226 loss: 357.7036 loss_cls: 112.7400 loss_bbox: 109.9225 loss_dfl: 135.0411 2024/03/18 04:24:44 - mmengine - INFO - Epoch(train) [64][900/925] lr: 4.6550e-05 eta: 2:42:46 time: 0.6635 data_time: 0.0031 memory: 11239 grad_norm: inf loss: 359.3631 loss_cls: 112.5683 loss_bbox: 111.1085 loss_dfl: 135.6864 2024/03/18 04:25:00 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:25:37 - mmengine - INFO - Epoch(train) [65][ 50/925] lr: 4.4075e-05 eta: 2:41:58 time: 0.7219 data_time: 0.0596 memory: 11213 grad_norm: 783.1812 loss: 360.0902 loss_cls: 113.7685 loss_bbox: 110.5249 loss_dfl: 135.7968 2024/03/18 04:26:11 - mmengine - INFO - Epoch(train) [65][100/925] lr: 4.4075e-05 eta: 2:41:25 time: 0.6628 data_time: 0.0031 memory: 11466 grad_norm: 711.0070 loss: 359.6702 loss_cls: 111.9487 loss_bbox: 112.5469 loss_dfl: 135.1746 2024/03/18 04:26:44 - mmengine - INFO - Epoch(train) [65][150/925] lr: 4.4075e-05 eta: 2:40:52 time: 0.6696 data_time: 0.0032 memory: 11546 grad_norm: 707.6441 loss: 357.4754 loss_cls: 112.4961 loss_bbox: 110.6293 loss_dfl: 134.3500 2024/03/18 04:27:17 - mmengine - INFO - Epoch(train) [65][200/925] lr: 4.4075e-05 eta: 2:40:19 time: 0.6516 data_time: 0.0026 memory: 11293 grad_norm: 743.1973 loss: 360.9122 loss_cls: 113.5694 loss_bbox: 112.7898 loss_dfl: 134.5531 2024/03/18 04:27:49 - mmengine - INFO - Epoch(train) [65][250/925] lr: 4.4075e-05 eta: 2:39:46 time: 0.6558 data_time: 0.0028 memory: 11146 grad_norm: 790.1644 loss: 359.6980 loss_cls: 113.3396 loss_bbox: 111.1611 loss_dfl: 135.1974 2024/03/18 04:28:23 - mmengine - INFO - Epoch(train) [65][300/925] lr: 4.4075e-05 eta: 2:39:13 time: 0.6648 data_time: 0.0031 memory: 11359 grad_norm: 747.8519 loss: 361.8471 loss_cls: 113.7146 loss_bbox: 113.0548 loss_dfl: 135.0778 2024/03/18 04:28:56 - mmengine - INFO - Epoch(train) [65][350/925] lr: 4.4075e-05 eta: 2:38:40 time: 0.6731 data_time: 0.0032 memory: 11786 grad_norm: 771.2849 loss: 358.8350 loss_cls: 112.9961 loss_bbox: 112.0998 loss_dfl: 133.7392 2024/03/18 04:29:29 - mmengine - INFO - Epoch(train) [65][400/925] lr: 4.4075e-05 eta: 2:38:07 time: 0.6422 data_time: 0.0031 memory: 11146 grad_norm: 755.7646 loss: 361.5506 loss_cls: 113.9880 loss_bbox: 112.4606 loss_dfl: 135.1021 2024/03/18 04:30:01 - mmengine - INFO - Epoch(train) [65][450/925] lr: 4.4075e-05 eta: 2:37:34 time: 0.6549 data_time: 0.0024 memory: 11719 grad_norm: 771.0093 loss: 358.7015 loss_cls: 112.8381 loss_bbox: 110.8210 loss_dfl: 135.0424 2024/03/18 04:30:35 - mmengine - INFO - Epoch(train) [65][500/925] lr: 4.4075e-05 eta: 2:37:01 time: 0.6692 data_time: 0.0032 memory: 11613 grad_norm: 689.1520 loss: 360.9623 loss_cls: 114.5491 loss_bbox: 112.0125 loss_dfl: 134.4008 2024/03/18 04:31:07 - mmengine - INFO - Epoch(train) [65][550/925] lr: 4.4075e-05 eta: 2:36:28 time: 0.6408 data_time: 0.0026 memory: 11519 grad_norm: 740.5244 loss: 365.5151 loss_cls: 115.5077 loss_bbox: 114.8845 loss_dfl: 135.1229 2024/03/18 04:31:40 - mmengine - INFO - Epoch(train) [65][600/925] lr: 4.4075e-05 eta: 2:35:55 time: 0.6629 data_time: 0.0032 memory: 11346 grad_norm: 748.8421 loss: 362.1965 loss_cls: 114.3942 loss_bbox: 112.9911 loss_dfl: 134.8112 2024/03/18 04:32:13 - mmengine - INFO - Epoch(train) [65][650/925] lr: 4.4075e-05 eta: 2:35:22 time: 0.6648 data_time: 0.0032 memory: 11439 grad_norm: 759.1740 loss: 352.6673 loss_cls: 110.0048 loss_bbox: 108.9887 loss_dfl: 133.6737 2024/03/18 04:32:46 - mmengine - INFO - Epoch(train) [65][700/925] lr: 4.4075e-05 eta: 2:34:49 time: 0.6589 data_time: 0.0030 memory: 11506 grad_norm: 749.5147 loss: 360.5788 loss_cls: 113.5452 loss_bbox: 111.8298 loss_dfl: 135.2037 2024/03/18 04:33:19 - mmengine - INFO - Epoch(train) [65][750/925] lr: 4.4075e-05 eta: 2:34:16 time: 0.6507 data_time: 0.0031 memory: 11346 grad_norm: 757.5542 loss: 356.4261 loss_cls: 113.4077 loss_bbox: 109.0116 loss_dfl: 134.0069 2024/03/18 04:33:52 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:33:52 - mmengine - INFO - Epoch(train) [65][800/925] lr: 4.4075e-05 eta: 2:33:44 time: 0.6583 data_time: 0.0031 memory: 11799 grad_norm: 760.6165 loss: 358.0222 loss_cls: 111.2065 loss_bbox: 112.2237 loss_dfl: 134.5920 2024/03/18 04:34:25 - mmengine - INFO - Epoch(train) [65][850/925] lr: 4.4075e-05 eta: 2:33:11 time: 0.6599 data_time: 0.0031 memory: 11693 grad_norm: 749.3767 loss: 362.9236 loss_cls: 114.4739 loss_bbox: 112.5300 loss_dfl: 135.9198 2024/03/18 04:34:57 - mmengine - INFO - Epoch(train) [65][900/925] lr: 4.4075e-05 eta: 2:32:37 time: 0.6441 data_time: 0.0030 memory: 11346 grad_norm: 737.5261 loss: 363.9507 loss_cls: 115.4674 loss_bbox: 112.6810 loss_dfl: 135.8023 2024/03/18 04:35:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:35:14 - mmengine - INFO - Saving checkpoint at 65 epochs 2024/03/18 04:35:23 - mmengine - INFO - Epoch(val) [65][ 50/625] eta: 0:00:13 time: 0.0230 data_time: 0.0007 memory: 10959 2024/03/18 04:35:24 - mmengine - INFO - Epoch(val) [65][100/625] eta: 0:00:12 time: 0.0234 data_time: 0.0003 memory: 1709 2024/03/18 04:35:25 - mmengine - INFO - Epoch(val) [65][150/625] eta: 0:00:11 time: 0.0236 data_time: 0.0003 memory: 1709 2024/03/18 04:35:26 - mmengine - INFO - Epoch(val) [65][200/625] eta: 0:00:09 time: 0.0236 data_time: 0.0003 memory: 1709 2024/03/18 04:35:27 - mmengine - INFO - Epoch(val) [65][250/625] eta: 0:00:08 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 04:35:29 - mmengine - INFO - Epoch(val) [65][300/625] eta: 0:00:07 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 04:35:30 - mmengine - INFO - Epoch(val) [65][350/625] eta: 0:00:06 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 04:35:31 - mmengine - INFO - Epoch(val) [65][400/625] eta: 0:00:05 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 04:35:32 - mmengine - INFO - Epoch(val) [65][450/625] eta: 0:00:04 time: 0.0222 data_time: 0.0003 memory: 1709 2024/03/18 04:35:33 - mmengine - INFO - Epoch(val) [65][500/625] eta: 0:00:02 time: 0.0220 data_time: 0.0003 memory: 1709 2024/03/18 04:35:34 - mmengine - INFO - Epoch(val) [65][550/625] eta: 0:00:01 time: 0.0212 data_time: 0.0002 memory: 1709 2024/03/18 04:35:35 - mmengine - INFO - Epoch(val) [65][600/625] eta: 0:00:00 time: 0.0210 data_time: 0.0002 memory: 1709 2024/03/18 04:35:45 - mmengine - INFO - Evaluating bbox... 2024/03/18 04:36:53 - mmengine - INFO - bbox_mAP_copypaste: 0.531 0.699 0.577 0.358 0.578 0.691 2024/03/18 04:36:55 - mmengine - INFO - Epoch(val) [65][625/625] coco/bbox_mAP: 0.5310 coco/bbox_mAP_50: 0.6990 coco/bbox_mAP_75: 0.5770 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5780 coco/bbox_mAP_l: 0.6910 data_time: 0.0002 time: 0.0209 2024/03/18 04:37:31 - mmengine - INFO - Epoch(train) [66][ 50/925] lr: 4.1600e-05 eta: 2:31:49 time: 0.7366 data_time: 0.0933 memory: 11253 grad_norm: 687.7280 loss: 360.7468 loss_cls: 114.7578 loss_bbox: 111.7844 loss_dfl: 134.2046 2024/03/18 04:38:03 - mmengine - INFO - Epoch(train) [66][100/925] lr: 4.1600e-05 eta: 2:31:16 time: 0.6392 data_time: 0.0032 memory: 11746 grad_norm: 793.0420 loss: 359.7957 loss_cls: 113.1902 loss_bbox: 111.8528 loss_dfl: 134.7527 2024/03/18 04:38:37 - mmengine - INFO - Epoch(train) [66][150/925] lr: 4.1600e-05 eta: 2:30:43 time: 0.6617 data_time: 0.0027 memory: 11399 grad_norm: 803.4325 loss: 366.2436 loss_cls: 115.7174 loss_bbox: 114.0815 loss_dfl: 136.4448 2024/03/18 04:39:10 - mmengine - INFO - Epoch(train) [66][200/925] lr: 4.1600e-05 eta: 2:30:10 time: 0.6592 data_time: 0.0030 memory: 11266 grad_norm: 772.2572 loss: 361.3906 loss_cls: 114.2575 loss_bbox: 112.0624 loss_dfl: 135.0707 2024/03/18 04:39:43 - mmengine - INFO - Epoch(train) [66][250/925] lr: 4.1600e-05 eta: 2:29:37 time: 0.6614 data_time: 0.0029 memory: 11506 grad_norm: 720.2741 loss: 362.4502 loss_cls: 114.2875 loss_bbox: 111.6868 loss_dfl: 136.4758 2024/03/18 04:40:16 - mmengine - INFO - Epoch(train) [66][300/925] lr: 4.1600e-05 eta: 2:29:04 time: 0.6558 data_time: 0.0026 memory: 11373 grad_norm: 760.5431 loss: 359.0491 loss_cls: 112.7076 loss_bbox: 110.3596 loss_dfl: 135.9820 2024/03/18 04:40:48 - mmengine - INFO - Epoch(train) [66][350/925] lr: 4.1600e-05 eta: 2:28:31 time: 0.6551 data_time: 0.0028 memory: 11853 grad_norm: 735.0035 loss: 363.0099 loss_cls: 114.6072 loss_bbox: 113.0064 loss_dfl: 135.3962 2024/03/18 04:41:21 - mmengine - INFO - Epoch(train) [66][400/925] lr: 4.1600e-05 eta: 2:27:58 time: 0.6613 data_time: 0.0028 memory: 11546 grad_norm: 729.4149 loss: 361.6167 loss_cls: 113.5039 loss_bbox: 112.1022 loss_dfl: 136.0106 2024/03/18 04:41:54 - mmengine - INFO - Epoch(train) [66][450/925] lr: 4.1600e-05 eta: 2:27:25 time: 0.6503 data_time: 0.0028 memory: 11359 grad_norm: 748.7875 loss: 356.2576 loss_cls: 110.4339 loss_bbox: 111.7469 loss_dfl: 134.0768 2024/03/18 04:42:27 - mmengine - INFO - Epoch(train) [66][500/925] lr: 4.1600e-05 eta: 2:26:52 time: 0.6645 data_time: 0.0028 memory: 11426 grad_norm: 717.3056 loss: 359.0806 loss_cls: 112.8772 loss_bbox: 111.1266 loss_dfl: 135.0767 2024/03/18 04:43:00 - mmengine - INFO - Epoch(train) [66][550/925] lr: 4.1600e-05 eta: 2:26:19 time: 0.6628 data_time: 0.0028 memory: 11639 grad_norm: 770.2944 loss: 365.5558 loss_cls: 116.2944 loss_bbox: 113.9912 loss_dfl: 135.2702 2024/03/18 04:43:32 - mmengine - INFO - Epoch(train) [66][600/925] lr: 4.1600e-05 eta: 2:25:46 time: 0.6388 data_time: 0.0028 memory: 11439 grad_norm: 683.4594 loss: 362.1549 loss_cls: 113.6348 loss_bbox: 112.8262 loss_dfl: 135.6939 2024/03/18 04:44:06 - mmengine - INFO - Epoch(train) [66][650/925] lr: 4.1600e-05 eta: 2:25:13 time: 0.6673 data_time: 0.0026 memory: 11213 grad_norm: 793.2286 loss: 358.2957 loss_cls: 111.7451 loss_bbox: 110.8313 loss_dfl: 135.7193 2024/03/18 04:44:39 - mmengine - INFO - Epoch(train) [66][700/925] lr: 4.1600e-05 eta: 2:24:40 time: 0.6584 data_time: 0.0028 memory: 11626 grad_norm: 768.8996 loss: 358.5914 loss_cls: 111.8531 loss_bbox: 111.8805 loss_dfl: 134.8578 2024/03/18 04:45:11 - mmengine - INFO - Epoch(train) [66][750/925] lr: 4.1600e-05 eta: 2:24:07 time: 0.6503 data_time: 0.0026 memory: 11399 grad_norm: 750.3662 loss: 360.1380 loss_cls: 112.6967 loss_bbox: 113.5446 loss_dfl: 133.8967 2024/03/18 04:45:45 - mmengine - INFO - Epoch(train) [66][800/925] lr: 4.1600e-05 eta: 2:23:34 time: 0.6650 data_time: 0.0026 memory: 11586 grad_norm: 774.3124 loss: 357.6171 loss_cls: 110.9355 loss_bbox: 112.2896 loss_dfl: 134.3920 2024/03/18 04:46:17 - mmengine - INFO - Epoch(train) [66][850/925] lr: 4.1600e-05 eta: 2:23:01 time: 0.6523 data_time: 0.0028 memory: 11479 grad_norm: 750.0395 loss: 358.4011 loss_cls: 112.1717 loss_bbox: 111.4951 loss_dfl: 134.7343 2024/03/18 04:46:34 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:46:50 - mmengine - INFO - Epoch(train) [66][900/925] lr: 4.1600e-05 eta: 2:22:28 time: 0.6594 data_time: 0.0027 memory: 11959 grad_norm: 805.7549 loss: 365.8380 loss_cls: 116.0668 loss_bbox: 113.9920 loss_dfl: 135.7792 2024/03/18 04:47:06 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:47:43 - mmengine - INFO - Epoch(train) [67][ 50/925] lr: 3.9125e-05 eta: 2:21:40 time: 0.7299 data_time: 0.0673 memory: 11306 grad_norm: 826.5065 loss: 358.5635 loss_cls: 112.1313 loss_bbox: 110.4578 loss_dfl: 135.9744 2024/03/18 04:48:15 - mmengine - INFO - Epoch(train) [67][100/925] lr: 3.9125e-05 eta: 2:21:06 time: 0.6441 data_time: 0.0031 memory: 11706 grad_norm: 710.1054 loss: 356.5801 loss_cls: 111.6416 loss_bbox: 109.7756 loss_dfl: 135.1630 2024/03/18 04:48:48 - mmengine - INFO - Epoch(train) [67][150/925] lr: 3.9125e-05 eta: 2:20:33 time: 0.6527 data_time: 0.0025 memory: 11266 grad_norm: 804.3718 loss: 355.3215 loss_cls: 110.4115 loss_bbox: 110.8911 loss_dfl: 134.0189 2024/03/18 04:49:21 - mmengine - INFO - Epoch(train) [67][200/925] lr: 3.9125e-05 eta: 2:20:00 time: 0.6625 data_time: 0.0030 memory: 11373 grad_norm: 732.7873 loss: 361.3302 loss_cls: 113.4050 loss_bbox: 112.7379 loss_dfl: 135.1873 2024/03/18 04:49:54 - mmengine - INFO - Epoch(train) [67][250/925] lr: 3.9125e-05 eta: 2:19:27 time: 0.6530 data_time: 0.0030 memory: 11333 grad_norm: inf loss: 361.7608 loss_cls: 113.1679 loss_bbox: 113.1703 loss_dfl: 135.4227 2024/03/18 04:50:26 - mmengine - INFO - Epoch(train) [67][300/925] lr: 3.9125e-05 eta: 2:18:54 time: 0.6417 data_time: 0.0027 memory: 11479 grad_norm: 754.6662 loss: 364.9599 loss_cls: 115.7023 loss_bbox: 112.5328 loss_dfl: 136.7247 2024/03/18 04:50:59 - mmengine - INFO - Epoch(train) [67][350/925] lr: 3.9125e-05 eta: 2:18:21 time: 0.6593 data_time: 0.0030 memory: 11106 grad_norm: 809.3528 loss: 364.3704 loss_cls: 114.8381 loss_bbox: 113.2326 loss_dfl: 136.2996 2024/03/18 04:51:32 - mmengine - INFO - Epoch(train) [67][400/925] lr: 3.9125e-05 eta: 2:17:48 time: 0.6560 data_time: 0.0028 memory: 11813 grad_norm: 762.2353 loss: 360.5495 loss_cls: 113.2712 loss_bbox: 112.0433 loss_dfl: 135.2350 2024/03/18 04:52:04 - mmengine - INFO - Epoch(train) [67][450/925] lr: 3.9125e-05 eta: 2:17:15 time: 0.6547 data_time: 0.0027 memory: 11066 grad_norm: 734.1605 loss: 356.8957 loss_cls: 111.7130 loss_bbox: 111.2465 loss_dfl: 133.9362 2024/03/18 04:52:37 - mmengine - INFO - Epoch(train) [67][500/925] lr: 3.9125e-05 eta: 2:16:43 time: 0.6575 data_time: 0.0029 memory: 11493 grad_norm: 702.0232 loss: 356.3772 loss_cls: 112.5389 loss_bbox: 109.7559 loss_dfl: 134.0824 2024/03/18 04:53:10 - mmengine - INFO - Epoch(train) [67][550/925] lr: 3.9125e-05 eta: 2:16:10 time: 0.6643 data_time: 0.0028 memory: 11653 grad_norm: 719.8285 loss: 358.0208 loss_cls: 111.2662 loss_bbox: 111.5792 loss_dfl: 135.1754 2024/03/18 04:53:43 - mmengine - INFO - Epoch(train) [67][600/925] lr: 3.9125e-05 eta: 2:15:37 time: 0.6540 data_time: 0.0031 memory: 11519 grad_norm: 784.3036 loss: 365.1274 loss_cls: 115.9453 loss_bbox: 112.4394 loss_dfl: 136.7428 2024/03/18 04:54:16 - mmengine - INFO - Epoch(train) [67][650/925] lr: 3.9125e-05 eta: 2:15:04 time: 0.6479 data_time: 0.0028 memory: 11373 grad_norm: 768.8405 loss: 360.6979 loss_cls: 113.3749 loss_bbox: 112.1855 loss_dfl: 135.1375 2024/03/18 04:54:49 - mmengine - INFO - Epoch(train) [67][700/925] lr: 3.9125e-05 eta: 2:14:31 time: 0.6655 data_time: 0.0028 memory: 11266 grad_norm: 757.4287 loss: 361.4051 loss_cls: 114.6320 loss_bbox: 110.8676 loss_dfl: 135.9055 2024/03/18 04:55:22 - mmengine - INFO - Epoch(train) [67][750/925] lr: 3.9125e-05 eta: 2:13:58 time: 0.6570 data_time: 0.0029 memory: 11239 grad_norm: 805.7713 loss: 363.9265 loss_cls: 114.4333 loss_bbox: 113.9329 loss_dfl: 135.5603 2024/03/18 04:55:55 - mmengine - INFO - Epoch(train) [67][800/925] lr: 3.9125e-05 eta: 2:13:25 time: 0.6570 data_time: 0.0028 memory: 11319 grad_norm: 802.1222 loss: 353.4076 loss_cls: 110.8920 loss_bbox: 108.8701 loss_dfl: 133.6455 2024/03/18 04:56:28 - mmengine - INFO - Epoch(train) [67][850/925] lr: 3.9125e-05 eta: 2:12:52 time: 0.6616 data_time: 0.0026 memory: 11413 grad_norm: 737.9467 loss: 361.1817 loss_cls: 114.2976 loss_bbox: 111.6953 loss_dfl: 135.1888 2024/03/18 04:57:01 - mmengine - INFO - Epoch(train) [67][900/925] lr: 3.9125e-05 eta: 2:12:19 time: 0.6612 data_time: 0.0030 memory: 11306 grad_norm: 725.2233 loss: 354.5824 loss_cls: 109.6915 loss_bbox: 110.4885 loss_dfl: 134.4024 2024/03/18 04:57:16 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:57:36 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 04:57:52 - mmengine - INFO - Epoch(train) [68][ 50/925] lr: 3.6650e-05 eta: 2:11:30 time: 0.7012 data_time: 0.0626 memory: 11279 grad_norm: 738.3311 loss: 355.4624 loss_cls: 109.7309 loss_bbox: 111.6140 loss_dfl: 134.1175 2024/03/18 04:58:25 - mmengine - INFO - Epoch(train) [68][100/925] lr: 3.6650e-05 eta: 2:10:57 time: 0.6654 data_time: 0.0029 memory: 11653 grad_norm: 714.0350 loss: 363.9037 loss_cls: 113.6602 loss_bbox: 113.9071 loss_dfl: 136.3363 2024/03/18 04:58:58 - mmengine - INFO - Epoch(train) [68][150/925] lr: 3.6650e-05 eta: 2:10:24 time: 0.6584 data_time: 0.0029 memory: 11266 grad_norm: 768.6547 loss: 359.5770 loss_cls: 113.0874 loss_bbox: 110.7075 loss_dfl: 135.7821 2024/03/18 04:59:31 - mmengine - INFO - Epoch(train) [68][200/925] lr: 3.6650e-05 eta: 2:09:51 time: 0.6573 data_time: 0.0030 memory: 12053 grad_norm: 799.3893 loss: 358.8728 loss_cls: 112.5966 loss_bbox: 110.4806 loss_dfl: 135.7956 2024/03/18 05:00:05 - mmengine - INFO - Epoch(train) [68][250/925] lr: 3.6650e-05 eta: 2:09:18 time: 0.6702 data_time: 0.0029 memory: 11679 grad_norm: 754.2124 loss: 361.6971 loss_cls: 113.7311 loss_bbox: 112.8087 loss_dfl: 135.1574 2024/03/18 05:00:38 - mmengine - INFO - Epoch(train) [68][300/925] lr: 3.6650e-05 eta: 2:08:45 time: 0.6612 data_time: 0.0031 memory: 11639 grad_norm: 780.0462 loss: 356.8855 loss_cls: 111.8072 loss_bbox: 110.7231 loss_dfl: 134.3552 2024/03/18 05:01:10 - mmengine - INFO - Epoch(train) [68][350/925] lr: 3.6650e-05 eta: 2:08:12 time: 0.6512 data_time: 0.0029 memory: 11759 grad_norm: 754.0553 loss: 356.8726 loss_cls: 111.9268 loss_bbox: 111.0971 loss_dfl: 133.8487 2024/03/18 05:01:43 - mmengine - INFO - Epoch(train) [68][400/925] lr: 3.6650e-05 eta: 2:07:39 time: 0.6570 data_time: 0.0030 memory: 11399 grad_norm: 801.3135 loss: 357.7034 loss_cls: 112.1155 loss_bbox: 111.0644 loss_dfl: 134.5235 2024/03/18 05:02:17 - mmengine - INFO - Epoch(train) [68][450/925] lr: 3.6650e-05 eta: 2:07:06 time: 0.6668 data_time: 0.0028 memory: 11359 grad_norm: 749.9932 loss: 354.6219 loss_cls: 109.6586 loss_bbox: 110.6906 loss_dfl: 134.2727 2024/03/18 05:02:49 - mmengine - INFO - Epoch(train) [68][500/925] lr: 3.6650e-05 eta: 2:06:33 time: 0.6477 data_time: 0.0030 memory: 11946 grad_norm: 799.0524 loss: 356.6253 loss_cls: 109.7766 loss_bbox: 111.4414 loss_dfl: 135.4074 2024/03/18 05:03:22 - mmengine - INFO - Epoch(train) [68][550/925] lr: 3.6650e-05 eta: 2:06:00 time: 0.6562 data_time: 0.0030 memory: 11266 grad_norm: 809.6639 loss: 356.9188 loss_cls: 112.6413 loss_bbox: 109.9647 loss_dfl: 134.3127 2024/03/18 05:03:55 - mmengine - INFO - Epoch(train) [68][600/925] lr: 3.6650e-05 eta: 2:05:27 time: 0.6645 data_time: 0.0027 memory: 11533 grad_norm: 777.2536 loss: 360.5275 loss_cls: 112.4274 loss_bbox: 113.0865 loss_dfl: 135.0137 2024/03/18 05:04:28 - mmengine - INFO - Epoch(train) [68][650/925] lr: 3.6650e-05 eta: 2:04:54 time: 0.6596 data_time: 0.0028 memory: 11213 grad_norm: 780.0214 loss: 358.3841 loss_cls: 111.1862 loss_bbox: 112.3761 loss_dfl: 134.8218 2024/03/18 05:05:00 - mmengine - INFO - Epoch(train) [68][700/925] lr: 3.6650e-05 eta: 2:04:21 time: 0.6420 data_time: 0.0029 memory: 11413 grad_norm: inf loss: 366.3489 loss_cls: 116.0306 loss_bbox: 114.4868 loss_dfl: 135.8315 2024/03/18 05:05:34 - mmengine - INFO - Epoch(train) [68][750/925] lr: 3.6650e-05 eta: 2:03:49 time: 0.6777 data_time: 0.0028 memory: 11413 grad_norm: 769.8392 loss: 365.4705 loss_cls: 116.2661 loss_bbox: 113.8672 loss_dfl: 135.3373 2024/03/18 05:06:07 - mmengine - INFO - Epoch(train) [68][800/925] lr: 3.6650e-05 eta: 2:03:16 time: 0.6582 data_time: 0.0027 memory: 11733 grad_norm: 795.0067 loss: 358.0577 loss_cls: 112.3459 loss_bbox: 111.3253 loss_dfl: 134.3866 2024/03/18 05:06:40 - mmengine - INFO - Epoch(train) [68][850/925] lr: 3.6650e-05 eta: 2:02:43 time: 0.6515 data_time: 0.0030 memory: 11826 grad_norm: 759.9325 loss: 356.7490 loss_cls: 110.6898 loss_bbox: 111.3421 loss_dfl: 134.7171 2024/03/18 05:07:13 - mmengine - INFO - Epoch(train) [68][900/925] lr: 3.6650e-05 eta: 2:02:10 time: 0.6617 data_time: 0.0030 memory: 11813 grad_norm: 762.6704 loss: 357.0641 loss_cls: 111.4127 loss_bbox: 111.4826 loss_dfl: 134.1687 2024/03/18 05:07:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:08:05 - mmengine - INFO - Epoch(train) [69][ 50/925] lr: 3.4175e-05 eta: 2:01:21 time: 0.7202 data_time: 0.0684 memory: 11439 grad_norm: 788.4925 loss: 354.7812 loss_cls: 109.3548 loss_bbox: 110.6731 loss_dfl: 134.7533 2024/03/18 05:08:37 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:08:37 - mmengine - INFO - Epoch(train) [69][100/925] lr: 3.4175e-05 eta: 2:00:48 time: 0.6384 data_time: 0.0030 memory: 11746 grad_norm: 772.0554 loss: 359.8819 loss_cls: 112.9547 loss_bbox: 111.3813 loss_dfl: 135.5460 2024/03/18 05:09:10 - mmengine - INFO - Epoch(train) [69][150/925] lr: 3.4175e-05 eta: 2:00:15 time: 0.6510 data_time: 0.0027 memory: 11346 grad_norm: 734.7060 loss: 353.7421 loss_cls: 110.2080 loss_bbox: 109.5395 loss_dfl: 133.9946 2024/03/18 05:09:43 - mmengine - INFO - Epoch(train) [69][200/925] lr: 3.4175e-05 eta: 1:59:42 time: 0.6577 data_time: 0.0030 memory: 11479 grad_norm: 802.7172 loss: 356.7935 loss_cls: 111.3015 loss_bbox: 111.3182 loss_dfl: 134.1739 2024/03/18 05:10:14 - mmengine - INFO - Epoch(train) [69][250/925] lr: 3.4175e-05 eta: 1:59:08 time: 0.6262 data_time: 0.0028 memory: 11293 grad_norm: 747.2704 loss: 357.4675 loss_cls: 112.3214 loss_bbox: 110.9920 loss_dfl: 134.1541 2024/03/18 05:10:47 - mmengine - INFO - Epoch(train) [69][300/925] lr: 3.4175e-05 eta: 1:58:35 time: 0.6628 data_time: 0.0028 memory: 11546 grad_norm: 728.0018 loss: 359.0478 loss_cls: 112.9115 loss_bbox: 111.6943 loss_dfl: 134.4420 2024/03/18 05:11:20 - mmengine - INFO - Epoch(train) [69][350/925] lr: 3.4175e-05 eta: 1:58:02 time: 0.6506 data_time: 0.0027 memory: 11439 grad_norm: 737.8490 loss: 359.6129 loss_cls: 113.0286 loss_bbox: 111.8491 loss_dfl: 134.7351 2024/03/18 05:11:52 - mmengine - INFO - Epoch(train) [69][400/925] lr: 3.4175e-05 eta: 1:57:29 time: 0.6464 data_time: 0.0028 memory: 11479 grad_norm: 738.1758 loss: 359.5403 loss_cls: 112.3798 loss_bbox: 111.2640 loss_dfl: 135.8965 2024/03/18 05:12:24 - mmengine - INFO - Epoch(train) [69][450/925] lr: 3.4175e-05 eta: 1:56:56 time: 0.6424 data_time: 0.0028 memory: 11266 grad_norm: 714.4883 loss: 350.2445 loss_cls: 107.4791 loss_bbox: 109.3061 loss_dfl: 133.4593 2024/03/18 05:12:57 - mmengine - INFO - Epoch(train) [69][500/925] lr: 3.4175e-05 eta: 1:56:23 time: 0.6502 data_time: 0.0028 memory: 11439 grad_norm: 760.9431 loss: 355.6812 loss_cls: 110.4802 loss_bbox: 111.2852 loss_dfl: 133.9158 2024/03/18 05:13:30 - mmengine - INFO - Epoch(train) [69][550/925] lr: 3.4175e-05 eta: 1:55:50 time: 0.6517 data_time: 0.0028 memory: 11239 grad_norm: 766.8100 loss: 347.0812 loss_cls: 106.1286 loss_bbox: 107.5050 loss_dfl: 133.4476 2024/03/18 05:14:01 - mmengine - INFO - Epoch(train) [69][600/925] lr: 3.4175e-05 eta: 1:55:17 time: 0.6348 data_time: 0.0028 memory: 11226 grad_norm: 762.9444 loss: 359.6230 loss_cls: 112.5495 loss_bbox: 111.7556 loss_dfl: 135.3179 2024/03/18 05:14:34 - mmengine - INFO - Epoch(train) [69][650/925] lr: 3.4175e-05 eta: 1:54:44 time: 0.6489 data_time: 0.0028 memory: 11613 grad_norm: 735.2357 loss: 355.0958 loss_cls: 110.6812 loss_bbox: 109.3846 loss_dfl: 135.0300 2024/03/18 05:15:07 - mmengine - INFO - Epoch(train) [69][700/925] lr: 3.4175e-05 eta: 1:54:11 time: 0.6624 data_time: 0.0028 memory: 11106 grad_norm: 747.6485 loss: 357.3284 loss_cls: 111.2298 loss_bbox: 111.8785 loss_dfl: 134.2201 2024/03/18 05:15:39 - mmengine - INFO - Epoch(train) [69][750/925] lr: 3.4175e-05 eta: 1:53:38 time: 0.6322 data_time: 0.0028 memory: 11239 grad_norm: 780.4088 loss: 357.7972 loss_cls: 111.2764 loss_bbox: 111.3407 loss_dfl: 135.1801 2024/03/18 05:16:12 - mmengine - INFO - Epoch(train) [69][800/925] lr: 3.4175e-05 eta: 1:53:05 time: 0.6647 data_time: 0.0028 memory: 12519 grad_norm: 735.4449 loss: 351.8578 loss_cls: 108.6606 loss_bbox: 110.1149 loss_dfl: 133.0823 2024/03/18 05:16:44 - mmengine - INFO - Epoch(train) [69][850/925] lr: 3.4175e-05 eta: 1:52:32 time: 0.6462 data_time: 0.0028 memory: 11333 grad_norm: 741.8525 loss: 354.6106 loss_cls: 110.0771 loss_bbox: 109.9435 loss_dfl: 134.5900 2024/03/18 05:17:15 - mmengine - INFO - Epoch(train) [69][900/925] lr: 3.4175e-05 eta: 1:51:59 time: 0.6242 data_time: 0.0028 memory: 11306 grad_norm: 733.5206 loss: 354.1446 loss_cls: 109.4155 loss_bbox: 111.1088 loss_dfl: 133.6202 2024/03/18 05:17:31 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:17:33 - mmengine - INFO - Epoch(val) [69][ 50/625] eta: 0:00:13 time: 0.0235 data_time: 0.0008 memory: 11226 2024/03/18 05:17:34 - mmengine - INFO - Epoch(val) [69][100/625] eta: 0:00:12 time: 0.0230 data_time: 0.0003 memory: 1709 2024/03/18 05:17:35 - mmengine - INFO - Epoch(val) [69][150/625] eta: 0:00:10 time: 0.0229 data_time: 0.0003 memory: 1709 2024/03/18 05:17:36 - mmengine - INFO - Epoch(val) [69][200/625] eta: 0:00:09 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:17:38 - mmengine - INFO - Epoch(val) [69][250/625] eta: 0:00:08 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:17:39 - mmengine - INFO - Epoch(val) [69][300/625] eta: 0:00:07 time: 0.0229 data_time: 0.0003 memory: 1709 2024/03/18 05:17:40 - mmengine - INFO - Epoch(val) [69][350/625] eta: 0:00:06 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 05:17:41 - mmengine - INFO - Epoch(val) [69][400/625] eta: 0:00:05 time: 0.0232 data_time: 0.0003 memory: 1709 2024/03/18 05:17:42 - mmengine - INFO - Epoch(val) [69][450/625] eta: 0:00:04 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 05:17:43 - mmengine - INFO - Epoch(val) [69][500/625] eta: 0:00:02 time: 0.0234 data_time: 0.0005 memory: 1709 2024/03/18 05:17:45 - mmengine - INFO - Epoch(val) [69][550/625] eta: 0:00:01 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 05:17:46 - mmengine - INFO - Epoch(val) [69][600/625] eta: 0:00:00 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 05:17:55 - mmengine - INFO - Evaluating bbox... 2024/03/18 05:18:53 - mmengine - INFO - bbox_mAP_copypaste: 0.532 0.700 0.579 0.357 0.579 0.691 2024/03/18 05:18:55 - mmengine - INFO - Epoch(val) [69][625/625] coco/bbox_mAP: 0.5320 coco/bbox_mAP_50: 0.7000 coco/bbox_mAP_75: 0.5790 coco/bbox_mAP_s: 0.3570 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6910 data_time: 0.0002 time: 0.0218 2024/03/18 05:19:29 - mmengine - INFO - Epoch(train) [70][ 50/925] lr: 3.1700e-05 eta: 1:51:10 time: 0.6937 data_time: 0.0567 memory: 11319 grad_norm: 786.4444 loss: 356.3431 loss_cls: 111.8602 loss_bbox: 110.4744 loss_dfl: 134.0086 2024/03/18 05:20:02 - mmengine - INFO - Epoch(train) [70][100/925] lr: 3.1700e-05 eta: 1:50:37 time: 0.6447 data_time: 0.0028 memory: 11293 grad_norm: 762.0617 loss: 354.2817 loss_cls: 110.1305 loss_bbox: 109.8183 loss_dfl: 134.3329 2024/03/18 05:20:33 - mmengine - INFO - Epoch(train) [70][150/925] lr: 3.1700e-05 eta: 1:50:03 time: 0.6278 data_time: 0.0030 memory: 11519 grad_norm: 747.6022 loss: 357.5149 loss_cls: 112.8311 loss_bbox: 110.1241 loss_dfl: 134.5598 2024/03/18 05:20:49 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:21:06 - mmengine - INFO - Epoch(train) [70][200/925] lr: 3.1700e-05 eta: 1:49:30 time: 0.6536 data_time: 0.0028 memory: 11399 grad_norm: 770.3733 loss: 356.1914 loss_cls: 109.9069 loss_bbox: 111.8682 loss_dfl: 134.4162 2024/03/18 05:21:39 - mmengine - INFO - Epoch(train) [70][250/925] lr: 3.1700e-05 eta: 1:48:58 time: 0.6584 data_time: 0.0028 memory: 11653 grad_norm: 844.9897 loss: 353.7573 loss_cls: 109.6692 loss_bbox: 109.4577 loss_dfl: 134.6304 2024/03/18 05:22:10 - mmengine - INFO - Epoch(train) [70][300/925] lr: 3.1700e-05 eta: 1:48:24 time: 0.6317 data_time: 0.0028 memory: 11453 grad_norm: 712.0679 loss: 359.5904 loss_cls: 111.9726 loss_bbox: 111.9942 loss_dfl: 135.6236 2024/03/18 05:22:43 - mmengine - INFO - Epoch(train) [70][350/925] lr: 3.1700e-05 eta: 1:47:51 time: 0.6467 data_time: 0.0028 memory: 11186 grad_norm: 792.2792 loss: 357.9463 loss_cls: 113.1157 loss_bbox: 111.1263 loss_dfl: 133.7044 2024/03/18 05:23:16 - mmengine - INFO - Epoch(train) [70][400/925] lr: 3.1700e-05 eta: 1:47:18 time: 0.6587 data_time: 0.0027 memory: 11506 grad_norm: 774.3067 loss: 357.3274 loss_cls: 110.9067 loss_bbox: 111.6166 loss_dfl: 134.8041 2024/03/18 05:23:48 - mmengine - INFO - Epoch(train) [70][450/925] lr: 3.1700e-05 eta: 1:46:45 time: 0.6483 data_time: 0.0028 memory: 11359 grad_norm: 763.7722 loss: 360.6008 loss_cls: 112.7495 loss_bbox: 113.0370 loss_dfl: 134.8144 2024/03/18 05:24:20 - mmengine - INFO - Epoch(train) [70][500/925] lr: 3.1700e-05 eta: 1:46:12 time: 0.6304 data_time: 0.0027 memory: 11186 grad_norm: 778.2135 loss: 352.2312 loss_cls: 109.4627 loss_bbox: 108.6036 loss_dfl: 134.1649 2024/03/18 05:24:52 - mmengine - INFO - Epoch(train) [70][550/925] lr: 3.1700e-05 eta: 1:45:39 time: 0.6522 data_time: 0.0028 memory: 11239 grad_norm: 788.0488 loss: 360.8820 loss_cls: 113.4655 loss_bbox: 112.5347 loss_dfl: 134.8818 2024/03/18 05:25:25 - mmengine - INFO - Epoch(train) [70][600/925] lr: 3.1700e-05 eta: 1:45:06 time: 0.6564 data_time: 0.0029 memory: 11693 grad_norm: 700.7903 loss: 356.1267 loss_cls: 111.0973 loss_bbox: 111.1781 loss_dfl: 133.8513 2024/03/18 05:25:57 - mmengine - INFO - Epoch(train) [70][650/925] lr: 3.1700e-05 eta: 1:44:33 time: 0.6281 data_time: 0.0030 memory: 11253 grad_norm: 776.1517 loss: 356.8448 loss_cls: 111.2441 loss_bbox: 110.9384 loss_dfl: 134.6623 2024/03/18 05:26:29 - mmengine - INFO - Epoch(train) [70][700/925] lr: 3.1700e-05 eta: 1:44:00 time: 0.6557 data_time: 0.0028 memory: 11239 grad_norm: 799.4349 loss: 351.9551 loss_cls: 108.3303 loss_bbox: 109.8732 loss_dfl: 133.7516 2024/03/18 05:27:02 - mmengine - INFO - Epoch(train) [70][750/925] lr: 3.1700e-05 eta: 1:43:27 time: 0.6561 data_time: 0.0030 memory: 11533 grad_norm: 759.5579 loss: 358.8003 loss_cls: 110.6381 loss_bbox: 112.6706 loss_dfl: 135.4916 2024/03/18 05:27:34 - mmengine - INFO - Epoch(train) [70][800/925] lr: 3.1700e-05 eta: 1:42:54 time: 0.6282 data_time: 0.0029 memory: 11253 grad_norm: 768.8599 loss: 360.1498 loss_cls: 112.5719 loss_bbox: 112.3555 loss_dfl: 135.2224 2024/03/18 05:28:06 - mmengine - INFO - Epoch(train) [70][850/925] lr: 3.1700e-05 eta: 1:42:21 time: 0.6544 data_time: 0.0029 memory: 11386 grad_norm: 772.3062 loss: 358.7052 loss_cls: 112.2508 loss_bbox: 111.2376 loss_dfl: 135.2168 2024/03/18 05:28:40 - mmengine - INFO - Epoch(train) [70][900/925] lr: 3.1700e-05 eta: 1:41:48 time: 0.6601 data_time: 0.0027 memory: 11293 grad_norm: 755.5410 loss: 350.2844 loss_cls: 107.0364 loss_bbox: 109.5546 loss_dfl: 133.6934 2024/03/18 05:28:55 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:28:55 - mmengine - INFO - Saving checkpoint at 70 epochs 2024/03/18 05:29:03 - mmengine - INFO - Epoch(val) [70][ 50/625] eta: 0:00:13 time: 0.0231 data_time: 0.0007 memory: 11399 2024/03/18 05:29:04 - mmengine - INFO - Epoch(val) [70][100/625] eta: 0:00:12 time: 0.0227 data_time: 0.0002 memory: 1709 2024/03/18 05:29:06 - mmengine - INFO - Epoch(val) [70][150/625] eta: 0:00:10 time: 0.0226 data_time: 0.0003 memory: 1709 2024/03/18 05:29:07 - mmengine - INFO - Epoch(val) [70][200/625] eta: 0:00:09 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:29:08 - mmengine - INFO - Epoch(val) [70][250/625] eta: 0:00:08 time: 0.0227 data_time: 0.0003 memory: 1709 2024/03/18 05:29:09 - mmengine - INFO - Epoch(val) [70][300/625] eta: 0:00:07 time: 0.0223 data_time: 0.0003 memory: 1709 2024/03/18 05:29:10 - mmengine - INFO - Epoch(val) [70][350/625] eta: 0:00:06 time: 0.0213 data_time: 0.0002 memory: 1709 2024/03/18 05:29:11 - mmengine - INFO - Epoch(val) [70][400/625] eta: 0:00:05 time: 0.0214 data_time: 0.0002 memory: 1709 2024/03/18 05:29:12 - mmengine - INFO - Epoch(val) [70][450/625] eta: 0:00:03 time: 0.0213 data_time: 0.0002 memory: 1709 2024/03/18 05:29:13 - mmengine - INFO - Epoch(val) [70][500/625] eta: 0:00:02 time: 0.0212 data_time: 0.0002 memory: 1709 2024/03/18 05:29:14 - mmengine - INFO - Epoch(val) [70][550/625] eta: 0:00:01 time: 0.0212 data_time: 0.0002 memory: 1709 2024/03/18 05:29:15 - mmengine - INFO - Epoch(val) [70][600/625] eta: 0:00:00 time: 0.0214 data_time: 0.0002 memory: 1709 2024/03/18 05:29:25 - mmengine - INFO - Evaluating bbox... 2024/03/18 05:30:30 - mmengine - INFO - bbox_mAP_copypaste: 0.532 0.700 0.578 0.356 0.579 0.691 2024/03/18 05:30:32 - mmengine - INFO - Epoch(val) [70][625/625] coco/bbox_mAP: 0.5320 coco/bbox_mAP_50: 0.7000 coco/bbox_mAP_75: 0.5780 coco/bbox_mAP_s: 0.3560 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6910 data_time: 0.0002 time: 0.0213 2024/03/18 05:30:32 - mmengine - INFO - Switch pipeline now! 2024/03/18 05:31:04 - mmengine - INFO - Epoch(train) [71][ 50/925] lr: 2.9225e-05 eta: 1:40:59 time: 0.6523 data_time: 0.0460 memory: 10586 grad_norm: inf loss: 351.1561 loss_cls: 102.7162 loss_bbox: 111.4651 loss_dfl: 136.9747 2024/03/18 05:31:36 - mmengine - INFO - Epoch(train) [71][100/925] lr: 2.9225e-05 eta: 1:40:26 time: 0.6404 data_time: 0.0024 memory: 10853 grad_norm: 1627.1975 loss: 352.4197 loss_cls: 100.4215 loss_bbox: 113.6023 loss_dfl: 138.3959 2024/03/18 05:32:08 - mmengine - INFO - Epoch(train) [71][150/925] lr: 2.9225e-05 eta: 1:39:52 time: 0.6232 data_time: 0.0025 memory: 10599 grad_norm: 1543.0475 loss: 341.4825 loss_cls: 96.6032 loss_bbox: 107.5834 loss_dfl: 137.2959 2024/03/18 05:32:39 - mmengine - INFO - Epoch(train) [71][200/925] lr: 2.9225e-05 eta: 1:39:19 time: 0.6242 data_time: 0.0024 memory: 10666 grad_norm: 1403.2499 loss: 342.5345 loss_cls: 99.3338 loss_bbox: 108.0495 loss_dfl: 135.1512 2024/03/18 05:33:11 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:33:11 - mmengine - INFO - Epoch(train) [71][250/925] lr: 2.9225e-05 eta: 1:38:46 time: 0.6368 data_time: 0.0024 memory: 10839 grad_norm: 1395.1781 loss: 345.5415 loss_cls: 98.6633 loss_bbox: 110.1204 loss_dfl: 136.7578 2024/03/18 05:33:43 - mmengine - INFO - Epoch(train) [71][300/925] lr: 2.9225e-05 eta: 1:38:13 time: 0.6419 data_time: 0.0025 memory: 10559 grad_norm: 1406.4932 loss: 340.2618 loss_cls: 96.1927 loss_bbox: 106.9900 loss_dfl: 137.0790 2024/03/18 05:34:14 - mmengine - INFO - Epoch(train) [71][350/925] lr: 2.9225e-05 eta: 1:37:40 time: 0.6296 data_time: 0.0024 memory: 10866 grad_norm: 1386.9308 loss: 336.7940 loss_cls: 94.3880 loss_bbox: 106.4617 loss_dfl: 135.9442 2024/03/18 05:34:46 - mmengine - INFO - Epoch(train) [71][400/925] lr: 2.9225e-05 eta: 1:37:07 time: 0.6271 data_time: 0.0025 memory: 10573 grad_norm: 1470.2265 loss: 343.7455 loss_cls: 98.8542 loss_bbox: 109.1570 loss_dfl: 135.7344 2024/03/18 05:35:18 - mmengine - INFO - Epoch(train) [71][450/925] lr: 2.9225e-05 eta: 1:36:34 time: 0.6416 data_time: 0.0023 memory: 10639 grad_norm: 1412.0498 loss: 344.7248 loss_cls: 97.5540 loss_bbox: 110.3648 loss_dfl: 136.8060 2024/03/18 05:35:49 - mmengine - INFO - Epoch(train) [71][500/925] lr: 2.9225e-05 eta: 1:36:01 time: 0.6281 data_time: 0.0025 memory: 10719 grad_norm: 1338.1113 loss: 347.6322 loss_cls: 97.1108 loss_bbox: 113.9272 loss_dfl: 136.5942 2024/03/18 05:36:21 - mmengine - INFO - Epoch(train) [71][550/925] lr: 2.9225e-05 eta: 1:35:28 time: 0.6349 data_time: 0.0024 memory: 10666 grad_norm: 1450.6367 loss: 341.2367 loss_cls: 97.9341 loss_bbox: 106.5865 loss_dfl: 136.7162 2024/03/18 05:36:53 - mmengine - INFO - Epoch(train) [71][600/925] lr: 2.9225e-05 eta: 1:34:55 time: 0.6439 data_time: 0.0025 memory: 10613 grad_norm: 1268.0778 loss: 334.8281 loss_cls: 94.9582 loss_bbox: 105.3120 loss_dfl: 134.5579 2024/03/18 05:37:26 - mmengine - INFO - Epoch(train) [71][650/925] lr: 2.9225e-05 eta: 1:34:22 time: 0.6471 data_time: 0.0023 memory: 10559 grad_norm: 1328.0681 loss: 335.7606 loss_cls: 94.9603 loss_bbox: 105.6486 loss_dfl: 135.1517 2024/03/18 05:37:56 - mmengine - INFO - Epoch(train) [71][700/925] lr: 2.9225e-05 eta: 1:33:48 time: 0.6165 data_time: 0.0024 memory: 10653 grad_norm: 1261.6164 loss: 345.9551 loss_cls: 97.6002 loss_bbox: 110.9636 loss_dfl: 137.3914 2024/03/18 05:38:29 - mmengine - INFO - Epoch(train) [71][750/925] lr: 2.9225e-05 eta: 1:33:15 time: 0.6436 data_time: 0.0025 memory: 10559 grad_norm: 1231.7214 loss: 342.5249 loss_cls: 97.2331 loss_bbox: 108.9880 loss_dfl: 136.3038 2024/03/18 05:39:01 - mmengine - INFO - Epoch(train) [71][800/925] lr: 2.9225e-05 eta: 1:32:42 time: 0.6383 data_time: 0.0024 memory: 10546 grad_norm: 1229.3726 loss: 342.7829 loss_cls: 97.1239 loss_bbox: 109.8882 loss_dfl: 135.7708 2024/03/18 05:39:32 - mmengine - INFO - Epoch(train) [71][850/925] lr: 2.9225e-05 eta: 1:32:09 time: 0.6198 data_time: 0.0024 memory: 10706 grad_norm: 1340.0955 loss: 335.9785 loss_cls: 93.6194 loss_bbox: 107.1216 loss_dfl: 135.2375 2024/03/18 05:40:04 - mmengine - INFO - Epoch(train) [71][900/925] lr: 2.9225e-05 eta: 1:31:36 time: 0.6374 data_time: 0.0024 memory: 10666 grad_norm: 1317.0247 loss: 340.5856 loss_cls: 96.2616 loss_bbox: 107.9044 loss_dfl: 136.4195 2024/03/18 05:40:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:40:21 - mmengine - INFO - Epoch(val) [71][ 50/625] eta: 0:00:12 time: 0.0224 data_time: 0.0007 memory: 10439 2024/03/18 05:40:22 - mmengine - INFO - Epoch(val) [71][100/625] eta: 0:00:11 time: 0.0219 data_time: 0.0003 memory: 1709 2024/03/18 05:40:23 - mmengine - INFO - Epoch(val) [71][150/625] eta: 0:00:10 time: 0.0219 data_time: 0.0002 memory: 1709 2024/03/18 05:40:24 - mmengine - INFO - Epoch(val) [71][200/625] eta: 0:00:09 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 05:40:25 - mmengine - INFO - Epoch(val) [71][250/625] eta: 0:00:08 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 05:40:26 - mmengine - INFO - Epoch(val) [71][300/625] eta: 0:00:07 time: 0.0220 data_time: 0.0002 memory: 1709 2024/03/18 05:40:27 - mmengine - INFO - Epoch(val) [71][350/625] eta: 0:00:06 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 05:40:28 - mmengine - INFO - Epoch(val) [71][400/625] eta: 0:00:04 time: 0.0219 data_time: 0.0003 memory: 1709 2024/03/18 05:40:29 - mmengine - INFO - Epoch(val) [71][450/625] eta: 0:00:03 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 05:40:30 - mmengine - INFO - Epoch(val) [71][500/625] eta: 0:00:02 time: 0.0219 data_time: 0.0002 memory: 1709 2024/03/18 05:40:32 - mmengine - INFO - Epoch(val) [71][550/625] eta: 0:00:01 time: 0.0220 data_time: 0.0002 memory: 1709 2024/03/18 05:40:33 - mmengine - INFO - Epoch(val) [71][600/625] eta: 0:00:00 time: 0.0218 data_time: 0.0002 memory: 1709 2024/03/18 05:40:45 - mmengine - INFO - Evaluating bbox... 2024/03/18 05:41:48 - mmengine - INFO - bbox_mAP_copypaste: 0.532 0.701 0.579 0.358 0.579 0.692 2024/03/18 05:41:50 - mmengine - INFO - Epoch(val) [71][625/625] coco/bbox_mAP: 0.5320 coco/bbox_mAP_50: 0.7010 coco/bbox_mAP_75: 0.5790 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6920 data_time: 0.0002 time: 0.0215 2024/03/18 05:42:23 - mmengine - INFO - Epoch(train) [72][ 50/925] lr: 2.6750e-05 eta: 1:30:47 time: 0.6718 data_time: 0.0468 memory: 10586 grad_norm: 1134.6782 loss: 335.4435 loss_cls: 96.1478 loss_bbox: 105.7504 loss_dfl: 133.5454 2024/03/18 05:42:54 - mmengine - INFO - Epoch(train) [72][100/925] lr: 2.6750e-05 eta: 1:30:13 time: 0.6089 data_time: 0.0024 memory: 10599 grad_norm: 1162.8597 loss: 336.5305 loss_cls: 93.5479 loss_bbox: 107.8649 loss_dfl: 135.1178 2024/03/18 05:43:25 - mmengine - INFO - Epoch(train) [72][150/925] lr: 2.6750e-05 eta: 1:29:40 time: 0.6334 data_time: 0.0023 memory: 10866 grad_norm: 1202.1203 loss: 340.5084 loss_cls: 95.4564 loss_bbox: 109.4682 loss_dfl: 135.5837 2024/03/18 05:43:57 - mmengine - INFO - Epoch(train) [72][200/925] lr: 2.6750e-05 eta: 1:29:07 time: 0.6335 data_time: 0.0025 memory: 10626 grad_norm: 1244.2483 loss: 335.5908 loss_cls: 94.6382 loss_bbox: 105.3997 loss_dfl: 135.5530 2024/03/18 05:44:27 - mmengine - INFO - Epoch(train) [72][250/925] lr: 2.6750e-05 eta: 1:28:34 time: 0.6040 data_time: 0.0025 memory: 10639 grad_norm: 1325.5926 loss: 339.4118 loss_cls: 96.3670 loss_bbox: 106.6226 loss_dfl: 136.4223 2024/03/18 05:44:59 - mmengine - INFO - Epoch(train) [72][300/925] lr: 2.6750e-05 eta: 1:28:01 time: 0.6217 data_time: 0.0025 memory: 10759 grad_norm: 1254.6043 loss: 336.2965 loss_cls: 95.0456 loss_bbox: 105.9471 loss_dfl: 135.3037 2024/03/18 05:45:15 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:45:30 - mmengine - INFO - Epoch(train) [72][350/925] lr: 2.6750e-05 eta: 1:27:28 time: 0.6374 data_time: 0.0026 memory: 10546 grad_norm: 1338.9959 loss: 345.3370 loss_cls: 99.1407 loss_bbox: 108.3608 loss_dfl: 137.8355 2024/03/18 05:46:02 - mmengine - INFO - Epoch(train) [72][400/925] lr: 2.6750e-05 eta: 1:26:55 time: 0.6243 data_time: 0.0025 memory: 10626 grad_norm: 1303.9668 loss: 337.5129 loss_cls: 94.2355 loss_bbox: 107.9126 loss_dfl: 135.3648 2024/03/18 05:46:32 - mmengine - INFO - Epoch(train) [72][450/925] lr: 2.6750e-05 eta: 1:26:22 time: 0.6100 data_time: 0.0023 memory: 10413 grad_norm: 1146.5383 loss: 337.1874 loss_cls: 95.1009 loss_bbox: 106.7653 loss_dfl: 135.3213 2024/03/18 05:47:04 - mmengine - INFO - Epoch(train) [72][500/925] lr: 2.6750e-05 eta: 1:25:49 time: 0.6380 data_time: 0.0024 memory: 10679 grad_norm: 1289.3885 loss: 333.5852 loss_cls: 93.0951 loss_bbox: 106.6684 loss_dfl: 133.8216 2024/03/18 05:47:35 - mmengine - INFO - Epoch(train) [72][550/925] lr: 2.6750e-05 eta: 1:25:15 time: 0.6178 data_time: 0.0025 memory: 10533 grad_norm: 1244.2084 loss: 339.6362 loss_cls: 95.4559 loss_bbox: 109.2527 loss_dfl: 134.9276 2024/03/18 05:48:05 - mmengine - INFO - Epoch(train) [72][600/925] lr: 2.6750e-05 eta: 1:24:42 time: 0.6067 data_time: 0.0023 memory: 10613 grad_norm: 1201.4082 loss: 342.8007 loss_cls: 98.9637 loss_bbox: 107.4660 loss_dfl: 136.3710 2024/03/18 05:48:37 - mmengine - INFO - Epoch(train) [72][650/925] lr: 2.6750e-05 eta: 1:24:09 time: 0.6261 data_time: 0.0024 memory: 10639 grad_norm: 1283.1160 loss: 338.5761 loss_cls: 95.7284 loss_bbox: 107.8600 loss_dfl: 134.9877 2024/03/18 05:49:08 - mmengine - INFO - Epoch(train) [72][700/925] lr: 2.6750e-05 eta: 1:23:36 time: 0.6288 data_time: 0.0025 memory: 10493 grad_norm: 1287.4664 loss: 337.1290 loss_cls: 93.3680 loss_bbox: 109.0997 loss_dfl: 134.6614 2024/03/18 05:49:39 - mmengine - INFO - Epoch(train) [72][750/925] lr: 2.6750e-05 eta: 1:23:03 time: 0.6092 data_time: 0.0025 memory: 10653 grad_norm: 1241.8997 loss: 336.1327 loss_cls: 94.4342 loss_bbox: 106.6116 loss_dfl: 135.0869 2024/03/18 05:50:10 - mmengine - INFO - Epoch(train) [72][800/925] lr: 2.6750e-05 eta: 1:22:30 time: 0.6321 data_time: 0.0026 memory: 10653 grad_norm: 1194.7369 loss: 339.0255 loss_cls: 94.7624 loss_bbox: 108.7710 loss_dfl: 135.4920 2024/03/18 05:50:42 - mmengine - INFO - Epoch(train) [72][850/925] lr: 2.6750e-05 eta: 1:21:57 time: 0.6287 data_time: 0.0024 memory: 10693 grad_norm: 1144.7107 loss: 333.3667 loss_cls: 92.4427 loss_bbox: 106.0246 loss_dfl: 134.8994 2024/03/18 05:51:12 - mmengine - INFO - Epoch(train) [72][900/925] lr: 2.6750e-05 eta: 1:21:24 time: 0.6096 data_time: 0.0022 memory: 10506 grad_norm: 1287.7176 loss: 343.1341 loss_cls: 95.2628 loss_bbox: 110.9215 loss_dfl: 136.9499 2024/03/18 05:51:28 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:51:29 - mmengine - INFO - Epoch(val) [72][ 50/625] eta: 0:00:13 time: 0.0232 data_time: 0.0007 memory: 10559 2024/03/18 05:51:31 - mmengine - INFO - Epoch(val) [72][100/625] eta: 0:00:12 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:51:32 - mmengine - INFO - Epoch(val) [72][150/625] eta: 0:00:10 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 05:51:33 - mmengine - INFO - Epoch(val) [72][200/625] eta: 0:00:09 time: 0.0230 data_time: 0.0003 memory: 1709 2024/03/18 05:51:34 - mmengine - INFO - Epoch(val) [72][250/625] eta: 0:00:08 time: 0.0231 data_time: 0.0003 memory: 1709 2024/03/18 05:51:35 - mmengine - INFO - Epoch(val) [72][300/625] eta: 0:00:07 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:51:36 - mmengine - INFO - Epoch(val) [72][350/625] eta: 0:00:06 time: 0.0228 data_time: 0.0003 memory: 1709 2024/03/18 05:51:37 - mmengine - INFO - Epoch(val) [72][400/625] eta: 0:00:05 time: 0.0230 data_time: 0.0003 memory: 1709 2024/03/18 05:51:39 - mmengine - INFO - Epoch(val) [72][450/625] eta: 0:00:04 time: 0.0237 data_time: 0.0003 memory: 1709 2024/03/18 05:51:40 - mmengine - INFO - Epoch(val) [72][500/625] eta: 0:00:02 time: 0.0242 data_time: 0.0003 memory: 1709 2024/03/18 05:51:41 - mmengine - INFO - Epoch(val) [72][550/625] eta: 0:00:01 time: 0.0234 data_time: 0.0003 memory: 1709 2024/03/18 05:51:42 - mmengine - INFO - Epoch(val) [72][600/625] eta: 0:00:00 time: 0.0220 data_time: 0.0003 memory: 1709 2024/03/18 05:51:52 - mmengine - INFO - Evaluating bbox... 2024/03/18 05:53:00 - mmengine - INFO - bbox_mAP_copypaste: 0.532 0.702 0.580 0.358 0.580 0.692 2024/03/18 05:53:02 - mmengine - INFO - Epoch(val) [72][625/625] coco/bbox_mAP: 0.5320 coco/bbox_mAP_50: 0.7020 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3580 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6920 data_time: 0.0002 time: 0.0212 2024/03/18 05:53:37 - mmengine - INFO - Epoch(train) [73][ 50/925] lr: 2.4275e-05 eta: 1:20:34 time: 0.6899 data_time: 0.0495 memory: 10653 grad_norm: 1099.9087 loss: 334.9199 loss_cls: 92.3529 loss_bbox: 106.9216 loss_dfl: 135.6454 2024/03/18 05:54:09 - mmengine - INFO - Epoch(train) [73][100/925] lr: 2.4275e-05 eta: 1:20:01 time: 0.6367 data_time: 0.0025 memory: 10613 grad_norm: inf loss: 336.4505 loss_cls: 93.4532 loss_bbox: 108.6156 loss_dfl: 134.3818 2024/03/18 05:54:40 - mmengine - INFO - Epoch(train) [73][150/925] lr: 2.4275e-05 eta: 1:19:28 time: 0.6291 data_time: 0.0025 memory: 10679 grad_norm: 1275.4066 loss: 334.8378 loss_cls: 93.2925 loss_bbox: 105.8662 loss_dfl: 135.6791 2024/03/18 05:55:12 - mmengine - INFO - Epoch(train) [73][200/925] lr: 2.4275e-05 eta: 1:18:55 time: 0.6259 data_time: 0.0023 memory: 10626 grad_norm: 1209.0426 loss: 336.3704 loss_cls: 94.6117 loss_bbox: 107.3634 loss_dfl: 134.3954 2024/03/18 05:55:44 - mmengine - INFO - Epoch(train) [73][250/925] lr: 2.4275e-05 eta: 1:18:22 time: 0.6440 data_time: 0.0023 memory: 10693 grad_norm: 1308.2414 loss: 329.9619 loss_cls: 91.9416 loss_bbox: 102.4594 loss_dfl: 135.5609 2024/03/18 05:56:16 - mmengine - INFO - Epoch(train) [73][300/925] lr: 2.4275e-05 eta: 1:17:49 time: 0.6354 data_time: 0.0026 memory: 10639 grad_norm: 1203.1465 loss: 337.9461 loss_cls: 94.0098 loss_bbox: 107.3588 loss_dfl: 136.5775 2024/03/18 05:56:47 - mmengine - INFO - Epoch(train) [73][350/925] lr: 2.4275e-05 eta: 1:17:16 time: 0.6286 data_time: 0.0024 memory: 10533 grad_norm: 1128.2996 loss: 332.4677 loss_cls: 93.1153 loss_bbox: 102.7090 loss_dfl: 136.6433 2024/03/18 05:57:19 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 05:57:19 - mmengine - INFO - Epoch(train) [73][400/925] lr: 2.4275e-05 eta: 1:16:43 time: 0.6425 data_time: 0.0024 memory: 10759 grad_norm: 1185.0810 loss: 336.0818 loss_cls: 94.5087 loss_bbox: 105.9481 loss_dfl: 135.6249 2024/03/18 05:57:51 - mmengine - INFO - Epoch(train) [73][450/925] lr: 2.4275e-05 eta: 1:16:10 time: 0.6432 data_time: 0.0025 memory: 10626 grad_norm: 1182.1033 loss: 345.2149 loss_cls: 99.0907 loss_bbox: 109.5941 loss_dfl: 136.5301 2024/03/18 05:58:23 - mmengine - INFO - Epoch(train) [73][500/925] lr: 2.4275e-05 eta: 1:15:37 time: 0.6257 data_time: 0.0024 memory: 10613 grad_norm: 1162.6034 loss: 339.1736 loss_cls: 93.9555 loss_bbox: 108.9781 loss_dfl: 136.2400 2024/03/18 05:58:56 - mmengine - INFO - Epoch(train) [73][550/925] lr: 2.4275e-05 eta: 1:15:04 time: 0.6558 data_time: 0.0025 memory: 10586 grad_norm: 1200.5355 loss: 339.3182 loss_cls: 92.8956 loss_bbox: 110.5091 loss_dfl: 135.9135 2024/03/18 05:59:27 - mmengine - INFO - Epoch(train) [73][600/925] lr: 2.4275e-05 eta: 1:14:31 time: 0.6333 data_time: 0.0024 memory: 10586 grad_norm: 1137.4769 loss: 338.8595 loss_cls: 94.8883 loss_bbox: 108.0797 loss_dfl: 135.8915 2024/03/18 05:59:59 - mmengine - INFO - Epoch(train) [73][650/925] lr: 2.4275e-05 eta: 1:13:58 time: 0.6313 data_time: 0.0025 memory: 10519 grad_norm: 1180.7848 loss: 333.7261 loss_cls: 92.6858 loss_bbox: 106.6087 loss_dfl: 134.4316 2024/03/18 06:00:32 - mmengine - INFO - Epoch(train) [73][700/925] lr: 2.4275e-05 eta: 1:13:26 time: 0.6572 data_time: 0.0024 memory: 10586 grad_norm: 1284.6421 loss: 341.2239 loss_cls: 95.4600 loss_bbox: 109.3900 loss_dfl: 136.3739 2024/03/18 06:01:04 - mmengine - INFO - Epoch(train) [73][750/925] lr: 2.4275e-05 eta: 1:12:53 time: 0.6421 data_time: 0.0026 memory: 10693 grad_norm: 1231.9954 loss: 335.7509 loss_cls: 94.3039 loss_bbox: 104.3941 loss_dfl: 137.0529 2024/03/18 06:01:36 - mmengine - INFO - Epoch(train) [73][800/925] lr: 2.4275e-05 eta: 1:12:20 time: 0.6334 data_time: 0.0026 memory: 10866 grad_norm: 1199.8657 loss: 337.5874 loss_cls: 95.6860 loss_bbox: 106.6164 loss_dfl: 135.2850 2024/03/18 06:02:08 - mmengine - INFO - Epoch(train) [73][850/925] lr: 2.4275e-05 eta: 1:11:47 time: 0.6404 data_time: 0.0025 memory: 10573 grad_norm: 1175.3514 loss: 330.1597 loss_cls: 91.8335 loss_bbox: 104.4329 loss_dfl: 133.8933 2024/03/18 06:02:40 - mmengine - INFO - Epoch(train) [73][900/925] lr: 2.4275e-05 eta: 1:11:14 time: 0.6534 data_time: 0.0025 memory: 10666 grad_norm: 1145.5971 loss: 338.5032 loss_cls: 95.1830 loss_bbox: 107.4961 loss_dfl: 135.8241 2024/03/18 06:02:56 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:02:58 - mmengine - INFO - Epoch(val) [73][ 50/625] eta: 0:00:13 time: 0.0233 data_time: 0.0007 memory: 10453 2024/03/18 06:02:59 - mmengine - INFO - Epoch(val) [73][100/625] eta: 0:00:12 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/18 06:03:00 - mmengine - INFO - Epoch(val) [73][150/625] eta: 0:00:10 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/18 06:03:01 - mmengine - INFO - Epoch(val) [73][200/625] eta: 0:00:09 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:03:02 - mmengine - INFO - Epoch(val) [73][250/625] eta: 0:00:08 time: 0.0230 data_time: 0.0003 memory: 1709 2024/03/18 06:03:03 - mmengine - INFO - Epoch(val) [73][300/625] eta: 0:00:07 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:03:04 - mmengine - INFO - Epoch(val) [73][350/625] eta: 0:00:06 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:03:05 - mmengine - INFO - Epoch(val) [73][400/625] eta: 0:00:05 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:03:07 - mmengine - INFO - Epoch(val) [73][450/625] eta: 0:00:03 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:03:08 - mmengine - INFO - Epoch(val) [73][500/625] eta: 0:00:02 time: 0.0226 data_time: 0.0003 memory: 1709 2024/03/18 06:03:09 - mmengine - INFO - Epoch(val) [73][550/625] eta: 0:00:01 time: 0.0226 data_time: 0.0003 memory: 1709 2024/03/18 06:03:10 - mmengine - INFO - Epoch(val) [73][600/625] eta: 0:00:00 time: 0.0222 data_time: 0.0003 memory: 1709 2024/03/18 06:03:20 - mmengine - INFO - Evaluating bbox... 2024/03/18 06:04:17 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.702 0.580 0.359 0.580 0.693 2024/03/18 06:04:19 - mmengine - INFO - Epoch(val) [73][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7020 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6930 data_time: 0.0002 time: 0.0216 2024/03/18 06:04:51 - mmengine - INFO - Epoch(train) [74][ 50/925] lr: 2.1800e-05 eta: 1:10:24 time: 0.6410 data_time: 0.0368 memory: 10586 grad_norm: 1200.0632 loss: 339.1077 loss_cls: 94.3527 loss_bbox: 107.5643 loss_dfl: 137.1907 2024/03/18 06:05:23 - mmengine - INFO - Epoch(train) [74][100/925] lr: 2.1800e-05 eta: 1:09:51 time: 0.6481 data_time: 0.0027 memory: 10506 grad_norm: 1124.0428 loss: 333.9042 loss_cls: 91.7001 loss_bbox: 107.5211 loss_dfl: 134.6831 2024/03/18 06:05:55 - mmengine - INFO - Epoch(train) [74][150/925] lr: 2.1800e-05 eta: 1:09:18 time: 0.6328 data_time: 0.0026 memory: 10639 grad_norm: 1266.1502 loss: 336.1428 loss_cls: 93.6630 loss_bbox: 107.0962 loss_dfl: 135.3835 2024/03/18 06:06:26 - mmengine - INFO - Epoch(train) [74][200/925] lr: 2.1800e-05 eta: 1:08:45 time: 0.6226 data_time: 0.0026 memory: 10533 grad_norm: 1127.3298 loss: 326.2944 loss_cls: 89.7876 loss_bbox: 103.7918 loss_dfl: 132.7151 2024/03/18 06:06:57 - mmengine - INFO - Epoch(train) [74][250/925] lr: 2.1800e-05 eta: 1:08:12 time: 0.6263 data_time: 0.0025 memory: 10506 grad_norm: 1142.0081 loss: 333.5241 loss_cls: 91.9698 loss_bbox: 105.2264 loss_dfl: 136.3279 2024/03/18 06:07:29 - mmengine - INFO - Epoch(train) [74][300/925] lr: 2.1800e-05 eta: 1:07:39 time: 0.6414 data_time: 0.0025 memory: 10773 grad_norm: 1107.7451 loss: 334.4083 loss_cls: 93.2993 loss_bbox: 107.0948 loss_dfl: 134.0142 2024/03/18 06:08:00 - mmengine - INFO - Epoch(train) [74][350/925] lr: 2.1800e-05 eta: 1:07:06 time: 0.6206 data_time: 0.0024 memory: 10719 grad_norm: 1270.8464 loss: 334.6778 loss_cls: 91.8229 loss_bbox: 107.3975 loss_dfl: 135.4574 2024/03/18 06:08:31 - mmengine - INFO - Epoch(train) [74][400/925] lr: 2.1800e-05 eta: 1:06:33 time: 0.6174 data_time: 0.0025 memory: 10613 grad_norm: 1139.0812 loss: 338.0480 loss_cls: 92.8022 loss_bbox: 109.0478 loss_dfl: 136.1980 2024/03/18 06:09:03 - mmengine - INFO - Epoch(train) [74][450/925] lr: 2.1800e-05 eta: 1:06:00 time: 0.6413 data_time: 0.0025 memory: 10719 grad_norm: 1086.7085 loss: 337.0538 loss_cls: 94.1223 loss_bbox: 107.7921 loss_dfl: 135.1395 2024/03/18 06:09:20 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:09:35 - mmengine - INFO - Epoch(train) [74][500/925] lr: 2.1800e-05 eta: 1:05:27 time: 0.6377 data_time: 0.0025 memory: 10653 grad_norm: 1182.9902 loss: 326.7247 loss_cls: 90.0148 loss_bbox: 104.1670 loss_dfl: 132.5429 2024/03/18 06:10:06 - mmengine - INFO - Epoch(train) [74][550/925] lr: 2.1800e-05 eta: 1:04:54 time: 0.6118 data_time: 0.0026 memory: 10586 grad_norm: 1276.4532 loss: 340.6662 loss_cls: 95.3779 loss_bbox: 108.5522 loss_dfl: 136.7361 2024/03/18 06:10:38 - mmengine - INFO - Epoch(train) [74][600/925] lr: 2.1800e-05 eta: 1:04:21 time: 0.6462 data_time: 0.0025 memory: 10599 grad_norm: 1180.2498 loss: 342.1486 loss_cls: 95.9018 loss_bbox: 109.1933 loss_dfl: 137.0535 2024/03/18 06:11:10 - mmengine - INFO - Epoch(train) [74][650/925] lr: 2.1800e-05 eta: 1:03:48 time: 0.6386 data_time: 0.0025 memory: 10666 grad_norm: 1215.2060 loss: 340.0322 loss_cls: 94.8244 loss_bbox: 109.3766 loss_dfl: 135.8311 2024/03/18 06:11:41 - mmengine - INFO - Epoch(train) [74][700/925] lr: 2.1800e-05 eta: 1:03:15 time: 0.6150 data_time: 0.0027 memory: 10573 grad_norm: 1242.4729 loss: 331.9875 loss_cls: 91.4338 loss_bbox: 106.0116 loss_dfl: 134.5421 2024/03/18 06:12:13 - mmengine - INFO - Epoch(train) [74][750/925] lr: 2.1800e-05 eta: 1:02:42 time: 0.6350 data_time: 0.0026 memory: 10679 grad_norm: 1185.8903 loss: 334.6237 loss_cls: 94.1146 loss_bbox: 106.3498 loss_dfl: 134.1594 2024/03/18 06:12:45 - mmengine - INFO - Epoch(train) [74][800/925] lr: 2.1800e-05 eta: 1:02:09 time: 0.6355 data_time: 0.0024 memory: 10546 grad_norm: 1134.1530 loss: 335.7861 loss_cls: 93.3647 loss_bbox: 106.0581 loss_dfl: 136.3634 2024/03/18 06:13:16 - mmengine - INFO - Epoch(train) [74][850/925] lr: 2.1800e-05 eta: 1:01:36 time: 0.6329 data_time: 0.0028 memory: 10586 grad_norm: 1129.7887 loss: 333.2994 loss_cls: 92.0074 loss_bbox: 105.7130 loss_dfl: 135.5790 2024/03/18 06:13:48 - mmengine - INFO - Epoch(train) [74][900/925] lr: 2.1800e-05 eta: 1:01:03 time: 0.6239 data_time: 0.0026 memory: 10626 grad_norm: 1223.7306 loss: 332.6980 loss_cls: 91.9369 loss_bbox: 105.7095 loss_dfl: 135.0516 2024/03/18 06:14:03 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:14:05 - mmengine - INFO - Epoch(val) [74][ 50/625] eta: 0:00:13 time: 0.0234 data_time: 0.0007 memory: 10493 2024/03/18 06:14:06 - mmengine - INFO - Epoch(val) [74][100/625] eta: 0:00:12 time: 0.0232 data_time: 0.0003 memory: 1709 2024/03/18 06:14:07 - mmengine - INFO - Epoch(val) [74][150/625] eta: 0:00:11 time: 0.0238 data_time: 0.0004 memory: 1709 2024/03/18 06:14:08 - mmengine - INFO - Epoch(val) [74][200/625] eta: 0:00:09 time: 0.0235 data_time: 0.0003 memory: 1709 2024/03/18 06:14:10 - mmengine - INFO - Epoch(val) [74][250/625] eta: 0:00:08 time: 0.0235 data_time: 0.0003 memory: 1709 2024/03/18 06:14:11 - mmengine - INFO - Epoch(val) [74][300/625] eta: 0:00:07 time: 0.0235 data_time: 0.0003 memory: 1709 2024/03/18 06:14:12 - mmengine - INFO - Epoch(val) [74][350/625] eta: 0:00:06 time: 0.0237 data_time: 0.0003 memory: 1709 2024/03/18 06:14:13 - mmengine - INFO - Epoch(val) [74][400/625] eta: 0:00:05 time: 0.0261 data_time: 0.0003 memory: 1709 2024/03/18 06:14:15 - mmengine - INFO - Epoch(val) [74][450/625] eta: 0:00:04 time: 0.0262 data_time: 0.0003 memory: 1709 2024/03/18 06:14:16 - mmengine - INFO - Epoch(val) [74][500/625] eta: 0:00:03 time: 0.0262 data_time: 0.0003 memory: 1709 2024/03/18 06:14:17 - mmengine - INFO - Epoch(val) [74][550/625] eta: 0:00:01 time: 0.0265 data_time: 0.0003 memory: 1709 2024/03/18 06:14:18 - mmengine - INFO - Epoch(val) [74][600/625] eta: 0:00:00 time: 0.0255 data_time: 0.0003 memory: 1709 2024/03/18 06:14:28 - mmengine - INFO - Evaluating bbox... 2024/03/18 06:15:33 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.359 0.580 0.693 2024/03/18 06:15:35 - mmengine - INFO - Epoch(val) [74][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6930 data_time: 0.0002 time: 0.0247 2024/03/18 06:16:08 - mmengine - INFO - Epoch(train) [75][ 50/925] lr: 1.9325e-05 eta: 1:00:14 time: 0.6649 data_time: 0.0448 memory: 10599 grad_norm: 1056.3255 loss: 329.1890 loss_cls: 88.0817 loss_bbox: 107.1435 loss_dfl: 133.9638 2024/03/18 06:16:39 - mmengine - INFO - Epoch(train) [75][100/925] lr: 1.9325e-05 eta: 0:59:41 time: 0.6242 data_time: 0.0025 memory: 10719 grad_norm: 1163.0567 loss: 334.2553 loss_cls: 93.1060 loss_bbox: 106.9107 loss_dfl: 134.2386 2024/03/18 06:17:11 - mmengine - INFO - Epoch(train) [75][150/925] lr: 1.9325e-05 eta: 0:59:08 time: 0.6389 data_time: 0.0026 memory: 10613 grad_norm: 1174.0156 loss: 333.6167 loss_cls: 90.1170 loss_bbox: 107.8895 loss_dfl: 135.6102 2024/03/18 06:17:43 - mmengine - INFO - Epoch(train) [75][200/925] lr: 1.9325e-05 eta: 0:58:35 time: 0.6330 data_time: 0.0026 memory: 10533 grad_norm: 1174.4540 loss: 336.5678 loss_cls: 93.7510 loss_bbox: 106.8582 loss_dfl: 135.9586 2024/03/18 06:18:14 - mmengine - INFO - Epoch(train) [75][250/925] lr: 1.9325e-05 eta: 0:58:02 time: 0.6213 data_time: 0.0024 memory: 10733 grad_norm: 1057.7971 loss: 342.5659 loss_cls: 96.4749 loss_bbox: 109.0528 loss_dfl: 137.0382 2024/03/18 06:18:45 - mmengine - INFO - Epoch(train) [75][300/925] lr: 1.9325e-05 eta: 0:57:29 time: 0.6202 data_time: 0.0029 memory: 10653 grad_norm: 1106.4903 loss: 332.0360 loss_cls: 91.1345 loss_bbox: 104.9852 loss_dfl: 135.9163 2024/03/18 06:19:17 - mmengine - INFO - Epoch(train) [75][350/925] lr: 1.9325e-05 eta: 0:56:56 time: 0.6380 data_time: 0.0027 memory: 10746 grad_norm: 1147.8828 loss: 341.4863 loss_cls: 97.0900 loss_bbox: 107.1323 loss_dfl: 137.2640 2024/03/18 06:19:49 - mmengine - INFO - Epoch(train) [75][400/925] lr: 1.9325e-05 eta: 0:56:23 time: 0.6248 data_time: 0.0026 memory: 10639 grad_norm: 1202.9897 loss: 331.9855 loss_cls: 94.3021 loss_bbox: 104.2314 loss_dfl: 133.4520 2024/03/18 06:20:20 - mmengine - INFO - Epoch(train) [75][450/925] lr: 1.9325e-05 eta: 0:55:50 time: 0.6196 data_time: 0.0024 memory: 10666 grad_norm: 1085.1030 loss: 343.6109 loss_cls: 97.7489 loss_bbox: 109.1303 loss_dfl: 136.7316 2024/03/18 06:20:51 - mmengine - INFO - Epoch(train) [75][500/925] lr: 1.9325e-05 eta: 0:55:17 time: 0.6319 data_time: 0.0025 memory: 10493 grad_norm: 1157.1304 loss: 335.7601 loss_cls: 94.1708 loss_bbox: 107.4119 loss_dfl: 134.1773 2024/03/18 06:21:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:21:22 - mmengine - INFO - Epoch(train) [75][550/925] lr: 1.9325e-05 eta: 0:54:44 time: 0.6240 data_time: 0.0024 memory: 10559 grad_norm: 1179.3763 loss: 334.6741 loss_cls: 93.6359 loss_bbox: 106.0330 loss_dfl: 135.0052 2024/03/18 06:21:53 - mmengine - INFO - Epoch(train) [75][600/925] lr: 1.9325e-05 eta: 0:54:11 time: 0.6098 data_time: 0.0026 memory: 10706 grad_norm: 1133.2953 loss: 334.6969 loss_cls: 92.8982 loss_bbox: 106.1196 loss_dfl: 135.6790 2024/03/18 06:22:25 - mmengine - INFO - Epoch(train) [75][650/925] lr: 1.9325e-05 eta: 0:53:39 time: 0.6438 data_time: 0.0024 memory: 10693 grad_norm: 1155.6672 loss: 340.9083 loss_cls: 97.3233 loss_bbox: 107.0026 loss_dfl: 136.5824 2024/03/18 06:22:56 - mmengine - INFO - Epoch(train) [75][700/925] lr: 1.9325e-05 eta: 0:53:06 time: 0.6235 data_time: 0.0025 memory: 10599 grad_norm: 1037.6749 loss: 330.5999 loss_cls: 91.3177 loss_bbox: 104.5880 loss_dfl: 134.6942 2024/03/18 06:23:28 - mmengine - INFO - Epoch(train) [75][750/925] lr: 1.9325e-05 eta: 0:52:33 time: 0.6395 data_time: 0.0031 memory: 10573 grad_norm: 1146.7771 loss: 337.7029 loss_cls: 93.0101 loss_bbox: 108.6575 loss_dfl: 136.0353 2024/03/18 06:24:01 - mmengine - INFO - Epoch(train) [75][800/925] lr: 1.9325e-05 eta: 0:52:00 time: 0.6560 data_time: 0.0034 memory: 10666 grad_norm: 1118.0115 loss: 332.7096 loss_cls: 91.6619 loss_bbox: 105.5585 loss_dfl: 135.4893 2024/03/18 06:24:34 - mmengine - INFO - Epoch(train) [75][850/925] lr: 1.9325e-05 eta: 0:51:27 time: 0.6592 data_time: 0.0032 memory: 10786 grad_norm: 1163.6706 loss: 335.1211 loss_cls: 93.5952 loss_bbox: 106.1369 loss_dfl: 135.3891 2024/03/18 06:25:06 - mmengine - INFO - Epoch(train) [75][900/925] lr: 1.9325e-05 eta: 0:50:54 time: 0.6350 data_time: 0.0036 memory: 10706 grad_norm: 1120.7273 loss: 332.3540 loss_cls: 90.2743 loss_bbox: 106.7661 loss_dfl: 135.3136 2024/03/18 06:25:22 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:25:22 - mmengine - INFO - Saving checkpoint at 75 epochs 2024/03/18 06:25:33 - mmengine - INFO - Epoch(val) [75][ 50/625] eta: 0:00:15 time: 0.0264 data_time: 0.0009 memory: 10613 2024/03/18 06:25:34 - mmengine - INFO - Epoch(val) [75][100/625] eta: 0:00:13 time: 0.0259 data_time: 0.0004 memory: 1709 2024/03/18 06:25:35 - mmengine - INFO - Epoch(val) [75][150/625] eta: 0:00:12 time: 0.0263 data_time: 0.0005 memory: 1709 2024/03/18 06:25:36 - mmengine - INFO - Epoch(val) [75][200/625] eta: 0:00:11 time: 0.0268 data_time: 0.0004 memory: 1709 2024/03/18 06:25:38 - mmengine - INFO - Epoch(val) [75][250/625] eta: 0:00:09 time: 0.0258 data_time: 0.0004 memory: 1709 2024/03/18 06:25:39 - mmengine - INFO - Epoch(val) [75][300/625] eta: 0:00:08 time: 0.0268 data_time: 0.0004 memory: 1709 2024/03/18 06:25:40 - mmengine - INFO - Epoch(val) [75][350/625] eta: 0:00:07 time: 0.0259 data_time: 0.0004 memory: 1709 2024/03/18 06:25:42 - mmengine - INFO - Epoch(val) [75][400/625] eta: 0:00:05 time: 0.0255 data_time: 0.0004 memory: 1709 2024/03/18 06:25:43 - mmengine - INFO - Epoch(val) [75][450/625] eta: 0:00:04 time: 0.0259 data_time: 0.0004 memory: 1709 2024/03/18 06:25:44 - mmengine - INFO - Epoch(val) [75][500/625] eta: 0:00:03 time: 0.0256 data_time: 0.0004 memory: 1709 2024/03/18 06:25:46 - mmengine - INFO - Epoch(val) [75][550/625] eta: 0:00:01 time: 0.0258 data_time: 0.0004 memory: 1709 2024/03/18 06:25:47 - mmengine - INFO - Epoch(val) [75][600/625] eta: 0:00:00 time: 0.0266 data_time: 0.0005 memory: 1709 2024/03/18 06:25:57 - mmengine - INFO - Evaluating bbox... 2024/03/18 06:26:55 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.360 0.580 0.694 2024/03/18 06:26:56 - mmengine - INFO - Epoch(val) [75][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3600 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6940 data_time: 0.0004 time: 0.0262 2024/03/18 06:27:30 - mmengine - INFO - Epoch(train) [76][ 50/925] lr: 1.6850e-05 eta: 0:50:05 time: 0.6692 data_time: 0.0352 memory: 10573 grad_norm: 1189.9750 loss: 336.0813 loss_cls: 92.5038 loss_bbox: 107.4993 loss_dfl: 136.0782 2024/03/18 06:28:01 - mmengine - INFO - Epoch(train) [76][100/925] lr: 1.6850e-05 eta: 0:49:32 time: 0.6246 data_time: 0.0027 memory: 10706 grad_norm: 1072.9150 loss: 340.5790 loss_cls: 95.4069 loss_bbox: 107.9246 loss_dfl: 137.2476 2024/03/18 06:28:31 - mmengine - INFO - Epoch(train) [76][150/925] lr: 1.6850e-05 eta: 0:48:59 time: 0.6039 data_time: 0.0027 memory: 10479 grad_norm: 1026.3573 loss: 333.9567 loss_cls: 93.3620 loss_bbox: 106.2793 loss_dfl: 134.3155 2024/03/18 06:29:02 - mmengine - INFO - Epoch(train) [76][200/925] lr: 1.6850e-05 eta: 0:48:26 time: 0.6194 data_time: 0.0026 memory: 10599 grad_norm: 1171.3182 loss: 326.5466 loss_cls: 90.4338 loss_bbox: 103.2520 loss_dfl: 132.8608 2024/03/18 06:29:34 - mmengine - INFO - Epoch(train) [76][250/925] lr: 1.6850e-05 eta: 0:47:53 time: 0.6331 data_time: 0.0025 memory: 10693 grad_norm: 1114.4501 loss: 334.5771 loss_cls: 92.3194 loss_bbox: 106.3810 loss_dfl: 135.8767 2024/03/18 06:30:05 - mmengine - INFO - Epoch(train) [76][300/925] lr: 1.6850e-05 eta: 0:47:20 time: 0.6127 data_time: 0.0026 memory: 10519 grad_norm: 1065.6118 loss: 334.7752 loss_cls: 92.5880 loss_bbox: 107.6616 loss_dfl: 134.5257 2024/03/18 06:30:37 - mmengine - INFO - Epoch(train) [76][350/925] lr: 1.6850e-05 eta: 0:46:47 time: 0.6379 data_time: 0.0037 memory: 10853 grad_norm: 1109.0568 loss: 332.7378 loss_cls: 90.3651 loss_bbox: 107.2262 loss_dfl: 135.1465 2024/03/18 06:31:10 - mmengine - INFO - Epoch(train) [76][400/925] lr: 1.6850e-05 eta: 0:46:14 time: 0.6699 data_time: 0.0039 memory: 10653 grad_norm: 1096.1265 loss: 331.6498 loss_cls: 92.3655 loss_bbox: 106.1384 loss_dfl: 133.1459 2024/03/18 06:31:43 - mmengine - INFO - Epoch(train) [76][450/925] lr: 1.6850e-05 eta: 0:45:41 time: 0.6518 data_time: 0.0041 memory: 10679 grad_norm: 1133.4617 loss: 334.1803 loss_cls: 90.0918 loss_bbox: 108.5863 loss_dfl: 135.5022 2024/03/18 06:32:15 - mmengine - INFO - Epoch(train) [76][500/925] lr: 1.6850e-05 eta: 0:45:09 time: 0.6493 data_time: 0.0036 memory: 10679 grad_norm: 1140.8292 loss: 329.2812 loss_cls: 89.1846 loss_bbox: 104.7487 loss_dfl: 135.3479 2024/03/18 06:32:50 - mmengine - INFO - Epoch(train) [76][550/925] lr: 1.6850e-05 eta: 0:44:36 time: 0.6857 data_time: 0.0041 memory: 10613 grad_norm: 1089.9034 loss: 332.7900 loss_cls: 92.3492 loss_bbox: 106.7783 loss_dfl: 133.6624 2024/03/18 06:33:23 - mmengine - INFO - Epoch(train) [76][600/925] lr: 1.6850e-05 eta: 0:44:03 time: 0.6588 data_time: 0.0040 memory: 10706 grad_norm: 1152.0023 loss: 333.2224 loss_cls: 93.6978 loss_bbox: 103.9510 loss_dfl: 135.5735 2024/03/18 06:33:40 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:33:55 - mmengine - INFO - Epoch(train) [76][650/925] lr: 1.6850e-05 eta: 0:43:30 time: 0.6478 data_time: 0.0038 memory: 10639 grad_norm: 1162.3331 loss: 339.2365 loss_cls: 94.7985 loss_bbox: 108.8840 loss_dfl: 135.5540 2024/03/18 06:34:27 - mmengine - INFO - Epoch(train) [76][700/925] lr: 1.6850e-05 eta: 0:42:57 time: 0.6331 data_time: 0.0029 memory: 10653 grad_norm: 1179.6900 loss: 322.4027 loss_cls: 87.6640 loss_bbox: 102.4193 loss_dfl: 132.3194 2024/03/18 06:34:58 - mmengine - INFO - Epoch(train) [76][750/925] lr: 1.6850e-05 eta: 0:42:24 time: 0.6235 data_time: 0.0028 memory: 10919 grad_norm: 1074.2334 loss: 328.7626 loss_cls: 90.4659 loss_bbox: 103.9694 loss_dfl: 134.3273 2024/03/18 06:35:28 - mmengine - INFO - Epoch(train) [76][800/925] lr: 1.6850e-05 eta: 0:41:51 time: 0.6030 data_time: 0.0027 memory: 10586 grad_norm: 1170.4035 loss: 336.1183 loss_cls: 94.9693 loss_bbox: 107.7310 loss_dfl: 133.4180 2024/03/18 06:35:59 - mmengine - INFO - Epoch(train) [76][850/925] lr: 1.6850e-05 eta: 0:41:18 time: 0.6180 data_time: 0.0028 memory: 10506 grad_norm: 1081.9712 loss: 323.8156 loss_cls: 89.0647 loss_bbox: 102.0486 loss_dfl: 132.7023 2024/03/18 06:36:31 - mmengine - INFO - Epoch(train) [76][900/925] lr: 1.6850e-05 eta: 0:40:45 time: 0.6371 data_time: 0.0030 memory: 10639 grad_norm: 1158.8271 loss: 332.7765 loss_cls: 91.6778 loss_bbox: 106.2358 loss_dfl: 134.8628 2024/03/18 06:36:46 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:36:48 - mmengine - INFO - Epoch(val) [76][ 50/625] eta: 0:00:15 time: 0.0267 data_time: 0.0007 memory: 10519 2024/03/18 06:36:50 - mmengine - INFO - Epoch(val) [76][100/625] eta: 0:00:14 time: 0.0277 data_time: 0.0003 memory: 1709 2024/03/18 06:36:51 - mmengine - INFO - Epoch(val) [76][150/625] eta: 0:00:12 time: 0.0263 data_time: 0.0003 memory: 1709 2024/03/18 06:36:52 - mmengine - INFO - Epoch(val) [76][200/625] eta: 0:00:11 time: 0.0261 data_time: 0.0003 memory: 1709 2024/03/18 06:36:54 - mmengine - INFO - Epoch(val) [76][250/625] eta: 0:00:09 time: 0.0261 data_time: 0.0003 memory: 1709 2024/03/18 06:36:55 - mmengine - INFO - Epoch(val) [76][300/625] eta: 0:00:08 time: 0.0261 data_time: 0.0003 memory: 1709 2024/03/18 06:36:56 - mmengine - INFO - Epoch(val) [76][350/625] eta: 0:00:07 time: 0.0262 data_time: 0.0003 memory: 1709 2024/03/18 06:36:58 - mmengine - INFO - Epoch(val) [76][400/625] eta: 0:00:06 time: 0.0282 data_time: 0.0004 memory: 1709 2024/03/18 06:36:59 - mmengine - INFO - Epoch(val) [76][450/625] eta: 0:00:04 time: 0.0304 data_time: 0.0004 memory: 1709 2024/03/18 06:37:01 - mmengine - INFO - Epoch(val) [76][500/625] eta: 0:00:03 time: 0.0320 data_time: 0.0004 memory: 1709 2024/03/18 06:37:02 - mmengine - INFO - Epoch(val) [76][550/625] eta: 0:00:02 time: 0.0255 data_time: 0.0003 memory: 1709 2024/03/18 06:37:03 - mmengine - INFO - Epoch(val) [76][600/625] eta: 0:00:00 time: 0.0248 data_time: 0.0002 memory: 1709 2024/03/18 06:37:13 - mmengine - INFO - Evaluating bbox... 2024/03/18 06:38:19 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.359 0.580 0.694 2024/03/18 06:38:21 - mmengine - INFO - Epoch(val) [76][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3590 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6940 data_time: 0.0002 time: 0.0246 2024/03/18 06:38:54 - mmengine - INFO - Epoch(train) [77][ 50/925] lr: 1.4375e-05 eta: 0:39:56 time: 0.6595 data_time: 0.0520 memory: 10639 grad_norm: 1122.8155 loss: 329.4379 loss_cls: 90.8597 loss_bbox: 104.6102 loss_dfl: 133.9681 2024/03/18 06:39:26 - mmengine - INFO - Epoch(train) [77][100/925] lr: 1.4375e-05 eta: 0:39:23 time: 0.6395 data_time: 0.0026 memory: 10559 grad_norm: 1093.5758 loss: 325.9547 loss_cls: 88.6243 loss_bbox: 104.4936 loss_dfl: 132.8368 2024/03/18 06:39:58 - mmengine - INFO - Epoch(train) [77][150/925] lr: 1.4375e-05 eta: 0:38:50 time: 0.6376 data_time: 0.0028 memory: 10613 grad_norm: 1125.1988 loss: 329.9788 loss_cls: 89.5627 loss_bbox: 105.8495 loss_dfl: 134.5666 2024/03/18 06:40:28 - mmengine - INFO - Epoch(train) [77][200/925] lr: 1.4375e-05 eta: 0:38:17 time: 0.6112 data_time: 0.0026 memory: 10959 grad_norm: 1117.7829 loss: 329.6583 loss_cls: 90.3353 loss_bbox: 105.5941 loss_dfl: 133.7289 2024/03/18 06:40:59 - mmengine - INFO - Epoch(train) [77][250/925] lr: 1.4375e-05 eta: 0:37:45 time: 0.6095 data_time: 0.0026 memory: 10653 grad_norm: 1141.7389 loss: 331.7771 loss_cls: 90.3851 loss_bbox: 105.5591 loss_dfl: 135.8329 2024/03/18 06:41:30 - mmengine - INFO - Epoch(train) [77][300/925] lr: 1.4375e-05 eta: 0:37:12 time: 0.6322 data_time: 0.0026 memory: 10613 grad_norm: 1097.1304 loss: 330.3672 loss_cls: 89.8111 loss_bbox: 106.7518 loss_dfl: 133.8044 2024/03/18 06:42:02 - mmengine - INFO - Epoch(train) [77][350/925] lr: 1.4375e-05 eta: 0:36:39 time: 0.6194 data_time: 0.0024 memory: 10599 grad_norm: 1069.3934 loss: 329.8856 loss_cls: 89.6695 loss_bbox: 104.5515 loss_dfl: 135.6646 2024/03/18 06:42:32 - mmengine - INFO - Epoch(train) [77][400/925] lr: 1.4375e-05 eta: 0:36:06 time: 0.6135 data_time: 0.0026 memory: 10493 grad_norm: 1119.5960 loss: 335.3214 loss_cls: 92.3299 loss_bbox: 108.4943 loss_dfl: 134.4972 2024/03/18 06:43:04 - mmengine - INFO - Epoch(train) [77][450/925] lr: 1.4375e-05 eta: 0:35:33 time: 0.6367 data_time: 0.0026 memory: 10586 grad_norm: 1063.7370 loss: 335.6345 loss_cls: 95.4864 loss_bbox: 106.2895 loss_dfl: 133.8586 2024/03/18 06:43:36 - mmengine - INFO - Epoch(train) [77][500/925] lr: 1.4375e-05 eta: 0:35:00 time: 0.6297 data_time: 0.0028 memory: 10586 grad_norm: 1124.3696 loss: 324.9710 loss_cls: 89.3449 loss_bbox: 102.8127 loss_dfl: 132.8134 2024/03/18 06:44:08 - mmengine - INFO - Epoch(train) [77][550/925] lr: 1.4375e-05 eta: 0:34:27 time: 0.6435 data_time: 0.0044 memory: 10666 grad_norm: 1158.0900 loss: 327.7203 loss_cls: 90.0779 loss_bbox: 103.2773 loss_dfl: 134.3651 2024/03/18 06:44:42 - mmengine - INFO - Epoch(train) [77][600/925] lr: 1.4375e-05 eta: 0:33:54 time: 0.6793 data_time: 0.0037 memory: 10666 grad_norm: 1065.1694 loss: 332.2654 loss_cls: 90.6003 loss_bbox: 106.4691 loss_dfl: 135.1960 2024/03/18 06:45:16 - mmengine - INFO - Epoch(train) [77][650/925] lr: 1.4375e-05 eta: 0:33:22 time: 0.6856 data_time: 0.0051 memory: 10546 grad_norm: 1087.2103 loss: 329.7945 loss_cls: 90.5756 loss_bbox: 105.1293 loss_dfl: 134.0896 2024/03/18 06:45:48 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:45:48 - mmengine - INFO - Epoch(train) [77][700/925] lr: 1.4375e-05 eta: 0:32:49 time: 0.6424 data_time: 0.0041 memory: 10613 grad_norm: 1127.7648 loss: 334.6205 loss_cls: 93.3828 loss_bbox: 104.9895 loss_dfl: 136.2482 2024/03/18 06:46:23 - mmengine - INFO - Epoch(train) [77][750/925] lr: 1.4375e-05 eta: 0:32:16 time: 0.6944 data_time: 0.0047 memory: 10653 grad_norm: 1167.8278 loss: 328.9025 loss_cls: 87.6048 loss_bbox: 104.9555 loss_dfl: 136.3421 2024/03/18 06:46:57 - mmengine - INFO - Epoch(train) [77][800/925] lr: 1.4375e-05 eta: 0:31:43 time: 0.6656 data_time: 0.0038 memory: 10839 grad_norm: 1121.3124 loss: 329.0047 loss_cls: 89.8182 loss_bbox: 103.3715 loss_dfl: 135.8150 2024/03/18 06:47:28 - mmengine - INFO - Epoch(train) [77][850/925] lr: 1.4375e-05 eta: 0:31:10 time: 0.6220 data_time: 0.0030 memory: 10599 grad_norm: 1103.8364 loss: 331.0946 loss_cls: 93.7342 loss_bbox: 102.8383 loss_dfl: 134.5221 2024/03/18 06:47:59 - mmengine - INFO - Epoch(train) [77][900/925] lr: 1.4375e-05 eta: 0:30:37 time: 0.6174 data_time: 0.0027 memory: 10693 grad_norm: 1158.7527 loss: 326.4578 loss_cls: 89.6745 loss_bbox: 103.9900 loss_dfl: 132.7933 2024/03/18 06:48:14 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:48:16 - mmengine - INFO - Epoch(val) [77][ 50/625] eta: 0:00:13 time: 0.0240 data_time: 0.0007 memory: 10453 2024/03/18 06:48:17 - mmengine - INFO - Epoch(val) [77][100/625] eta: 0:00:12 time: 0.0239 data_time: 0.0003 memory: 1709 2024/03/18 06:48:18 - mmengine - INFO - Epoch(val) [77][150/625] eta: 0:00:11 time: 0.0232 data_time: 0.0003 memory: 1709 2024/03/18 06:48:20 - mmengine - INFO - Epoch(val) [77][200/625] eta: 0:00:09 time: 0.0225 data_time: 0.0003 memory: 1709 2024/03/18 06:48:21 - mmengine - INFO - Epoch(val) [77][250/625] eta: 0:00:08 time: 0.0222 data_time: 0.0003 memory: 1709 2024/03/18 06:48:22 - mmengine - INFO - Epoch(val) [77][300/625] eta: 0:00:07 time: 0.0224 data_time: 0.0003 memory: 1709 2024/03/18 06:48:23 - mmengine - INFO - Epoch(val) [77][350/625] eta: 0:00:06 time: 0.0223 data_time: 0.0003 memory: 1709 2024/03/18 06:48:24 - mmengine - INFO - Epoch(val) [77][400/625] eta: 0:00:05 time: 0.0223 data_time: 0.0003 memory: 1709 2024/03/18 06:48:25 - mmengine - INFO - Epoch(val) [77][450/625] eta: 0:00:04 time: 0.0232 data_time: 0.0003 memory: 1709 2024/03/18 06:48:27 - mmengine - INFO - Epoch(val) [77][500/625] eta: 0:00:02 time: 0.0244 data_time: 0.0004 memory: 1709 2024/03/18 06:48:28 - mmengine - INFO - Epoch(val) [77][550/625] eta: 0:00:01 time: 0.0265 data_time: 0.0004 memory: 1709 2024/03/18 06:48:29 - mmengine - INFO - Epoch(val) [77][600/625] eta: 0:00:00 time: 0.0274 data_time: 0.0004 memory: 1709 2024/03/18 06:48:40 - mmengine - INFO - Evaluating bbox... 2024/03/18 06:49:43 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.360 0.580 0.694 2024/03/18 06:49:45 - mmengine - INFO - Epoch(val) [77][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3600 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6940 data_time: 0.0004 time: 0.0258 2024/03/18 06:50:18 - mmengine - INFO - Epoch(train) [78][ 50/925] lr: 1.1900e-05 eta: 0:29:48 time: 0.6736 data_time: 0.0487 memory: 10626 grad_norm: 1109.8243 loss: 331.3239 loss_cls: 90.9036 loss_bbox: 105.8905 loss_dfl: 134.5298 2024/03/18 06:50:50 - mmengine - INFO - Epoch(train) [78][100/925] lr: 1.1900e-05 eta: 0:29:15 time: 0.6313 data_time: 0.0025 memory: 10599 grad_norm: 1091.3171 loss: 330.2562 loss_cls: 88.7544 loss_bbox: 106.9412 loss_dfl: 134.5606 2024/03/18 06:51:22 - mmengine - INFO - Epoch(train) [78][150/925] lr: 1.1900e-05 eta: 0:28:43 time: 0.6396 data_time: 0.0027 memory: 10706 grad_norm: inf loss: 335.1117 loss_cls: 92.1104 loss_bbox: 106.9427 loss_dfl: 136.0586 2024/03/18 06:51:54 - mmengine - INFO - Epoch(train) [78][200/925] lr: 1.1900e-05 eta: 0:28:10 time: 0.6463 data_time: 0.0027 memory: 10653 grad_norm: 1105.6008 loss: 322.4847 loss_cls: 86.6824 loss_bbox: 102.8599 loss_dfl: 132.9424 2024/03/18 06:52:26 - mmengine - INFO - Epoch(train) [78][250/925] lr: 1.1900e-05 eta: 0:27:37 time: 0.6288 data_time: 0.0028 memory: 10786 grad_norm: 1179.7733 loss: 326.9549 loss_cls: 88.7384 loss_bbox: 105.2640 loss_dfl: 132.9526 2024/03/18 06:52:58 - mmengine - INFO - Epoch(train) [78][300/925] lr: 1.1900e-05 eta: 0:27:04 time: 0.6394 data_time: 0.0028 memory: 10679 grad_norm: 1174.9632 loss: 326.3266 loss_cls: 89.6512 loss_bbox: 103.1128 loss_dfl: 133.5626 2024/03/18 06:53:30 - mmengine - INFO - Epoch(train) [78][350/925] lr: 1.1900e-05 eta: 0:26:31 time: 0.6479 data_time: 0.0025 memory: 10639 grad_norm: 1154.1692 loss: 329.5823 loss_cls: 89.5869 loss_bbox: 104.6190 loss_dfl: 135.3764 2024/03/18 06:54:02 - mmengine - INFO - Epoch(train) [78][400/925] lr: 1.1900e-05 eta: 0:25:58 time: 0.6334 data_time: 0.0024 memory: 10666 grad_norm: 1162.6975 loss: 331.8969 loss_cls: 91.1546 loss_bbox: 106.4049 loss_dfl: 134.3374 2024/03/18 06:54:33 - mmengine - INFO - Epoch(train) [78][450/925] lr: 1.1900e-05 eta: 0:25:25 time: 0.6203 data_time: 0.0025 memory: 10533 grad_norm: 1213.6666 loss: 333.9286 loss_cls: 91.6453 loss_bbox: 105.8336 loss_dfl: 136.4497 2024/03/18 06:55:06 - mmengine - INFO - Epoch(train) [78][500/925] lr: 1.1900e-05 eta: 0:24:53 time: 0.6518 data_time: 0.0026 memory: 10733 grad_norm: 1100.8083 loss: 335.7486 loss_cls: 93.5195 loss_bbox: 107.6934 loss_dfl: 134.5357 2024/03/18 06:55:37 - mmengine - INFO - Epoch(train) [78][550/925] lr: 1.1900e-05 eta: 0:24:20 time: 0.6345 data_time: 0.0025 memory: 10639 grad_norm: 1043.0954 loss: 325.2624 loss_cls: 87.7047 loss_bbox: 104.0551 loss_dfl: 133.5026 2024/03/18 06:56:08 - mmengine - INFO - Epoch(train) [78][600/925] lr: 1.1900e-05 eta: 0:23:47 time: 0.6175 data_time: 0.0025 memory: 10599 grad_norm: 1076.1935 loss: 337.0876 loss_cls: 91.9778 loss_bbox: 108.1307 loss_dfl: 136.9791 2024/03/18 06:56:42 - mmengine - INFO - Epoch(train) [78][650/925] lr: 1.1900e-05 eta: 0:23:14 time: 0.6690 data_time: 0.0029 memory: 10613 grad_norm: 1117.9868 loss: 328.4287 loss_cls: 89.8632 loss_bbox: 104.1209 loss_dfl: 134.4445 2024/03/18 06:57:17 - mmengine - INFO - Epoch(train) [78][700/925] lr: 1.1900e-05 eta: 0:22:41 time: 0.7064 data_time: 0.0039 memory: 10706 grad_norm: 1034.6167 loss: 325.4326 loss_cls: 89.5438 loss_bbox: 103.2396 loss_dfl: 132.6492 2024/03/18 06:57:51 - mmengine - INFO - Epoch(train) [78][750/925] lr: 1.1900e-05 eta: 0:22:09 time: 0.6770 data_time: 0.0038 memory: 10573 grad_norm: 1092.1906 loss: 331.2328 loss_cls: 90.6127 loss_bbox: 105.2024 loss_dfl: 135.4178 2024/03/18 06:58:09 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:58:26 - mmengine - INFO - Epoch(train) [78][800/925] lr: 1.1900e-05 eta: 0:21:36 time: 0.7047 data_time: 0.0043 memory: 10599 grad_norm: 1066.0475 loss: 322.0685 loss_cls: 87.1263 loss_bbox: 102.0162 loss_dfl: 132.9259 2024/03/18 06:59:03 - mmengine - INFO - Epoch(train) [78][850/925] lr: 1.1900e-05 eta: 0:21:03 time: 0.7202 data_time: 0.0048 memory: 10639 grad_norm: 1126.5854 loss: 330.9704 loss_cls: 90.3961 loss_bbox: 106.1500 loss_dfl: 134.4243 2024/03/18 06:59:37 - mmengine - INFO - Epoch(train) [78][900/925] lr: 1.1900e-05 eta: 0:20:30 time: 0.6921 data_time: 0.0048 memory: 10746 grad_norm: 1062.2178 loss: 329.7272 loss_cls: 89.4935 loss_bbox: 105.7286 loss_dfl: 134.5050 2024/03/18 06:59:53 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 06:59:55 - mmengine - INFO - Epoch(val) [78][ 50/625] eta: 0:00:15 time: 0.0268 data_time: 0.0010 memory: 10573 2024/03/18 06:59:57 - mmengine - INFO - Epoch(val) [78][100/625] eta: 0:00:13 time: 0.0246 data_time: 0.0003 memory: 1709 2024/03/18 06:59:58 - mmengine - INFO - Epoch(val) [78][150/625] eta: 0:00:11 time: 0.0243 data_time: 0.0003 memory: 1709 2024/03/18 06:59:59 - mmengine - INFO - Epoch(val) [78][200/625] eta: 0:00:10 time: 0.0221 data_time: 0.0002 memory: 1709 2024/03/18 07:00:00 - mmengine - INFO - Epoch(val) [78][250/625] eta: 0:00:09 time: 0.0227 data_time: 0.0003 memory: 1709 2024/03/18 07:00:01 - mmengine - INFO - Epoch(val) [78][300/625] eta: 0:00:07 time: 0.0233 data_time: 0.0003 memory: 1709 2024/03/18 07:00:02 - mmengine - INFO - Epoch(val) [78][350/625] eta: 0:00:06 time: 0.0239 data_time: 0.0003 memory: 1709 2024/03/18 07:00:04 - mmengine - INFO - Epoch(val) [78][400/625] eta: 0:00:05 time: 0.0258 data_time: 0.0003 memory: 1709 2024/03/18 07:00:05 - mmengine - INFO - Epoch(val) [78][450/625] eta: 0:00:04 time: 0.0257 data_time: 0.0003 memory: 1709 2024/03/18 07:00:06 - mmengine - INFO - Epoch(val) [78][500/625] eta: 0:00:03 time: 0.0260 data_time: 0.0003 memory: 1709 2024/03/18 07:00:08 - mmengine - INFO - Epoch(val) [78][550/625] eta: 0:00:01 time: 0.0259 data_time: 0.0003 memory: 1709 2024/03/18 07:00:09 - mmengine - INFO - Epoch(val) [78][600/625] eta: 0:00:00 time: 0.0248 data_time: 0.0003 memory: 1709 2024/03/18 07:00:19 - mmengine - INFO - Evaluating bbox... 2024/03/18 07:01:25 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.360 0.579 0.694 2024/03/18 07:01:27 - mmengine - INFO - Epoch(val) [78][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3600 coco/bbox_mAP_m: 0.5790 coco/bbox_mAP_l: 0.6940 data_time: 0.0003 time: 0.0241 2024/03/18 07:02:01 - mmengine - INFO - Epoch(train) [79][ 50/925] lr: 9.4250e-06 eta: 0:19:41 time: 0.6828 data_time: 0.0538 memory: 10519 grad_norm: 1094.7743 loss: 322.5939 loss_cls: 89.1048 loss_bbox: 102.1810 loss_dfl: 131.3081 2024/03/18 07:02:33 - mmengine - INFO - Epoch(train) [79][100/925] lr: 9.4250e-06 eta: 0:19:08 time: 0.6417 data_time: 0.0028 memory: 10586 grad_norm: 1049.5013 loss: 326.2010 loss_cls: 87.8749 loss_bbox: 104.9596 loss_dfl: 133.3664 2024/03/18 07:03:05 - mmengine - INFO - Epoch(train) [79][150/925] lr: 9.4250e-06 eta: 0:18:35 time: 0.6300 data_time: 0.0029 memory: 10719 grad_norm: 1119.7486 loss: 326.8469 loss_cls: 88.8940 loss_bbox: 103.5220 loss_dfl: 134.4308 2024/03/18 07:03:36 - mmengine - INFO - Epoch(train) [79][200/925] lr: 9.4250e-06 eta: 0:18:03 time: 0.6271 data_time: 0.0027 memory: 10519 grad_norm: 1129.5402 loss: 328.3752 loss_cls: 89.4808 loss_bbox: 104.3112 loss_dfl: 134.5833 2024/03/18 07:04:08 - mmengine - INFO - Epoch(train) [79][250/925] lr: 9.4250e-06 eta: 0:17:30 time: 0.6447 data_time: 0.0029 memory: 10546 grad_norm: 1228.8718 loss: 331.6894 loss_cls: 90.0275 loss_bbox: 106.0294 loss_dfl: 135.6325 2024/03/18 07:04:41 - mmengine - INFO - Epoch(train) [79][300/925] lr: 9.4250e-06 eta: 0:16:57 time: 0.6440 data_time: 0.0030 memory: 10733 grad_norm: 1088.7209 loss: 336.6269 loss_cls: 95.1753 loss_bbox: 106.3300 loss_dfl: 135.1216 2024/03/18 07:05:12 - mmengine - INFO - Epoch(train) [79][350/925] lr: 9.4250e-06 eta: 0:16:24 time: 0.6332 data_time: 0.0029 memory: 10613 grad_norm: 1056.3939 loss: 329.1562 loss_cls: 88.8675 loss_bbox: 105.3557 loss_dfl: 134.9330 2024/03/18 07:05:45 - mmengine - INFO - Epoch(train) [79][400/925] lr: 9.4250e-06 eta: 0:15:51 time: 0.6611 data_time: 0.0031 memory: 10666 grad_norm: 1071.5413 loss: 329.3433 loss_cls: 88.7591 loss_bbox: 106.3112 loss_dfl: 134.2730 2024/03/18 07:06:18 - mmengine - INFO - Epoch(train) [79][450/925] lr: 9.4250e-06 eta: 0:15:18 time: 0.6449 data_time: 0.0028 memory: 10679 grad_norm: 1089.7533 loss: 335.6534 loss_cls: 94.0438 loss_bbox: 106.9287 loss_dfl: 134.6808 2024/03/18 07:06:49 - mmengine - INFO - Epoch(train) [79][500/925] lr: 9.4250e-06 eta: 0:14:46 time: 0.6263 data_time: 0.0024 memory: 10546 grad_norm: 1008.4435 loss: 335.3356 loss_cls: 93.5410 loss_bbox: 107.7079 loss_dfl: 134.0867 2024/03/18 07:07:22 - mmengine - INFO - Epoch(train) [79][550/925] lr: 9.4250e-06 eta: 0:14:13 time: 0.6510 data_time: 0.0026 memory: 10853 grad_norm: 1155.0602 loss: 328.7000 loss_cls: 89.5111 loss_bbox: 105.1686 loss_dfl: 134.0203 2024/03/18 07:07:53 - mmengine - INFO - Epoch(train) [79][600/925] lr: 9.4250e-06 eta: 0:13:40 time: 0.6323 data_time: 0.0024 memory: 10706 grad_norm: 1050.4652 loss: 330.5542 loss_cls: 89.9733 loss_bbox: 105.4405 loss_dfl: 135.1404 2024/03/18 07:08:25 - mmengine - INFO - Epoch(train) [79][650/925] lr: 9.4250e-06 eta: 0:13:07 time: 0.6296 data_time: 0.0025 memory: 10613 grad_norm: 1060.5221 loss: 329.7280 loss_cls: 89.5675 loss_bbox: 105.0246 loss_dfl: 135.1358 2024/03/18 07:08:57 - mmengine - INFO - Epoch(train) [79][700/925] lr: 9.4250e-06 eta: 0:12:34 time: 0.6408 data_time: 0.0026 memory: 10519 grad_norm: 1069.0305 loss: 328.4356 loss_cls: 89.4680 loss_bbox: 105.2613 loss_dfl: 133.7064 2024/03/18 07:09:29 - mmengine - INFO - Epoch(train) [79][750/925] lr: 9.4250e-06 eta: 0:12:01 time: 0.6410 data_time: 0.0025 memory: 10519 grad_norm: 1106.2065 loss: 319.9397 loss_cls: 86.1632 loss_bbox: 101.0874 loss_dfl: 132.6891 2024/03/18 07:10:02 - mmengine - INFO - Epoch(train) [79][800/925] lr: 9.4250e-06 eta: 0:11:29 time: 0.6652 data_time: 0.0038 memory: 10506 grad_norm: 1121.8133 loss: 324.1013 loss_cls: 87.0650 loss_bbox: 103.0177 loss_dfl: 134.0187 2024/03/18 07:10:37 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 07:10:37 - mmengine - INFO - Epoch(train) [79][850/925] lr: 9.4250e-06 eta: 0:10:56 time: 0.6931 data_time: 0.0042 memory: 10573 grad_norm: 1079.4465 loss: 325.6497 loss_cls: 85.5295 loss_bbox: 105.2131 loss_dfl: 134.9071 2024/03/18 07:11:13 - mmengine - INFO - Epoch(train) [79][900/925] lr: 9.4250e-06 eta: 0:10:23 time: 0.7155 data_time: 0.0039 memory: 10693 grad_norm: 1151.5563 loss: 324.2350 loss_cls: 86.3812 loss_bbox: 104.0850 loss_dfl: 133.7689 2024/03/18 07:11:29 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 07:11:31 - mmengine - INFO - Epoch(val) [79][ 50/625] eta: 0:00:15 time: 0.0263 data_time: 0.0009 memory: 10746 2024/03/18 07:11:32 - mmengine - INFO - Epoch(val) [79][100/625] eta: 0:00:14 time: 0.0271 data_time: 0.0004 memory: 1709 2024/03/18 07:11:34 - mmengine - INFO - Epoch(val) [79][150/625] eta: 0:00:12 time: 0.0282 data_time: 0.0005 memory: 1709 2024/03/18 07:11:35 - mmengine - INFO - Epoch(val) [79][200/625] eta: 0:00:11 time: 0.0284 data_time: 0.0005 memory: 1709 2024/03/18 07:11:37 - mmengine - INFO - Epoch(val) [79][250/625] eta: 0:00:10 time: 0.0283 data_time: 0.0005 memory: 1709 2024/03/18 07:11:38 - mmengine - INFO - Epoch(val) [79][300/625] eta: 0:00:08 time: 0.0273 data_time: 0.0004 memory: 1709 2024/03/18 07:11:40 - mmengine - INFO - Epoch(val) [79][350/625] eta: 0:00:07 time: 0.0279 data_time: 0.0005 memory: 1709 2024/03/18 07:11:41 - mmengine - INFO - Epoch(val) [79][400/625] eta: 0:00:06 time: 0.0263 data_time: 0.0004 memory: 1709 2024/03/18 07:11:42 - mmengine - INFO - Epoch(val) [79][450/625] eta: 0:00:04 time: 0.0267 data_time: 0.0004 memory: 1709 2024/03/18 07:11:44 - mmengine - INFO - Epoch(val) [79][500/625] eta: 0:00:03 time: 0.0267 data_time: 0.0004 memory: 1709 2024/03/18 07:11:45 - mmengine - INFO - Epoch(val) [79][550/625] eta: 0:00:02 time: 0.0271 data_time: 0.0004 memory: 1709 2024/03/18 07:11:46 - mmengine - INFO - Epoch(val) [79][600/625] eta: 0:00:00 time: 0.0278 data_time: 0.0004 memory: 1709 2024/03/18 07:12:00 - mmengine - INFO - Evaluating bbox... 2024/03/18 07:13:10 - mmengine - INFO - bbox_mAP_copypaste: 0.534 0.704 0.581 0.361 0.580 0.694 2024/03/18 07:13:11 - mmengine - INFO - Epoch(val) [79][625/625] coco/bbox_mAP: 0.5340 coco/bbox_mAP_50: 0.7040 coco/bbox_mAP_75: 0.5810 coco/bbox_mAP_s: 0.3610 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6940 data_time: 0.0006 time: 0.0275 2024/03/18 07:13:45 - mmengine - INFO - Epoch(train) [80][ 50/925] lr: 6.9500e-06 eta: 0:09:34 time: 0.6826 data_time: 0.0368 memory: 10719 grad_norm: 1104.3448 loss: 329.2765 loss_cls: 89.2328 loss_bbox: 105.2370 loss_dfl: 134.8067 2024/03/18 07:14:16 - mmengine - INFO - Epoch(train) [80][100/925] lr: 6.9500e-06 eta: 0:09:01 time: 0.6207 data_time: 0.0028 memory: 10733 grad_norm: 1098.5787 loss: 331.5578 loss_cls: 89.1227 loss_bbox: 107.4741 loss_dfl: 134.9610 2024/03/18 07:14:48 - mmengine - INFO - Epoch(train) [80][150/925] lr: 6.9500e-06 eta: 0:08:28 time: 0.6385 data_time: 0.0031 memory: 10679 grad_norm: 1072.3540 loss: 328.7232 loss_cls: 90.9987 loss_bbox: 104.3710 loss_dfl: 133.3536 2024/03/18 07:15:19 - mmengine - INFO - Epoch(train) [80][200/925] lr: 6.9500e-06 eta: 0:07:55 time: 0.6270 data_time: 0.0028 memory: 10666 grad_norm: 1099.6188 loss: 330.7662 loss_cls: 89.4963 loss_bbox: 106.0395 loss_dfl: 135.2304 2024/03/18 07:15:51 - mmengine - INFO - Epoch(train) [80][250/925] lr: 6.9500e-06 eta: 0:07:22 time: 0.6299 data_time: 0.0027 memory: 10719 grad_norm: 1053.4487 loss: 328.5982 loss_cls: 91.2327 loss_bbox: 103.0954 loss_dfl: 134.2702 2024/03/18 07:16:24 - mmengine - INFO - Epoch(train) [80][300/925] lr: 6.9500e-06 eta: 0:06:50 time: 0.6558 data_time: 0.0030 memory: 10599 grad_norm: inf loss: 328.0084 loss_cls: 88.7434 loss_bbox: 105.0246 loss_dfl: 134.2404 2024/03/18 07:16:56 - mmengine - INFO - Epoch(train) [80][350/925] lr: 6.9500e-06 eta: 0:06:17 time: 0.6346 data_time: 0.0030 memory: 10733 grad_norm: 1161.0832 loss: 331.1976 loss_cls: 90.8693 loss_bbox: 104.6146 loss_dfl: 135.7138 2024/03/18 07:17:27 - mmengine - INFO - Epoch(train) [80][400/925] lr: 6.9500e-06 eta: 0:05:44 time: 0.6205 data_time: 0.0027 memory: 10613 grad_norm: 1071.0837 loss: 330.6463 loss_cls: 89.1287 loss_bbox: 106.5156 loss_dfl: 135.0021 2024/03/18 07:17:59 - mmengine - INFO - Epoch(train) [80][450/925] lr: 6.9500e-06 eta: 0:05:11 time: 0.6482 data_time: 0.0032 memory: 10586 grad_norm: 1131.1613 loss: 331.7903 loss_cls: 89.5013 loss_bbox: 107.6944 loss_dfl: 134.5945 2024/03/18 07:18:31 - mmengine - INFO - Epoch(train) [80][500/925] lr: 6.9500e-06 eta: 0:04:38 time: 0.6374 data_time: 0.0027 memory: 10693 grad_norm: 1127.7830 loss: 328.3544 loss_cls: 89.4497 loss_bbox: 104.7927 loss_dfl: 134.1120 2024/03/18 07:19:03 - mmengine - INFO - Epoch(train) [80][550/925] lr: 6.9500e-06 eta: 0:04:06 time: 0.6454 data_time: 0.0027 memory: 10586 grad_norm: 1111.9327 loss: 329.8952 loss_cls: 92.0349 loss_bbox: 104.3349 loss_dfl: 133.5253 2024/03/18 07:19:35 - mmengine - INFO - Epoch(train) [80][600/925] lr: 6.9500e-06 eta: 0:03:33 time: 0.6337 data_time: 0.0026 memory: 10746 grad_norm: 1012.0396 loss: 327.3315 loss_cls: 88.9129 loss_bbox: 104.0727 loss_dfl: 134.3459 2024/03/18 07:20:07 - mmengine - INFO - Epoch(train) [80][650/925] lr: 6.9500e-06 eta: 0:03:00 time: 0.6412 data_time: 0.0027 memory: 10653 grad_norm: 1002.0340 loss: 337.7784 loss_cls: 92.5586 loss_bbox: 109.4673 loss_dfl: 135.7525 2024/03/18 07:20:39 - mmengine - INFO - Epoch(train) [80][700/925] lr: 6.9500e-06 eta: 0:02:27 time: 0.6315 data_time: 0.0027 memory: 10733 grad_norm: 1065.9160 loss: 322.5615 loss_cls: 86.7350 loss_bbox: 103.1241 loss_dfl: 132.7024 2024/03/18 07:21:10 - mmengine - INFO - Epoch(train) [80][750/925] lr: 6.9500e-06 eta: 0:01:54 time: 0.6268 data_time: 0.0028 memory: 10599 grad_norm: 1072.5342 loss: 327.6869 loss_cls: 88.1951 loss_bbox: 105.3415 loss_dfl: 134.1502 2024/03/18 07:21:42 - mmengine - INFO - Epoch(train) [80][800/925] lr: 6.9500e-06 eta: 0:01:22 time: 0.6447 data_time: 0.0027 memory: 10626 grad_norm: 1094.5879 loss: 339.4781 loss_cls: 94.3310 loss_bbox: 108.3207 loss_dfl: 136.8264 2024/03/18 07:22:14 - mmengine - INFO - Epoch(train) [80][850/925] lr: 6.9500e-06 eta: 0:00:49 time: 0.6284 data_time: 0.0025 memory: 10693 grad_norm: 1152.8371 loss: 332.2058 loss_cls: 90.4415 loss_bbox: 107.0237 loss_dfl: 134.7406 2024/03/18 07:22:46 - mmengine - INFO - Epoch(train) [80][900/925] lr: 6.9500e-06 eta: 0:00:16 time: 0.6357 data_time: 0.0030 memory: 10666 grad_norm: 1030.1728 loss: 333.7563 loss_cls: 89.9201 loss_bbox: 107.1495 loss_dfl: 136.6868 2024/03/18 07:23:02 - mmengine - INFO - Exp name: yolo_world_v2_l_vlpan_bn_2e-4_80e_8gpus_mask-refine_finetune_coco_20240317_171126 2024/03/18 07:23:03 - mmengine - INFO - Saving checkpoint at 80 epochs 2024/03/18 07:23:15 - mmengine - INFO - Epoch(val) [80][ 50/625] eta: 0:00:15 time: 0.0273 data_time: 0.0009 memory: 10479 2024/03/18 07:23:16 - mmengine - INFO - Epoch(val) [80][100/625] eta: 0:00:13 time: 0.0256 data_time: 0.0004 memory: 1709 2024/03/18 07:23:17 - mmengine - INFO - Epoch(val) [80][150/625] eta: 0:00:12 time: 0.0258 data_time: 0.0004 memory: 1709 2024/03/18 07:23:18 - mmengine - INFO - Epoch(val) [80][200/625] eta: 0:00:11 time: 0.0256 data_time: 0.0004 memory: 1709 2024/03/18 07:23:20 - mmengine - INFO - Epoch(val) [80][250/625] eta: 0:00:09 time: 0.0257 data_time: 0.0004 memory: 1709 2024/03/18 07:23:21 - mmengine - INFO - Epoch(val) [80][300/625] eta: 0:00:08 time: 0.0247 data_time: 0.0004 memory: 1709 2024/03/18 07:23:22 - mmengine - INFO - Epoch(val) [80][350/625] eta: 0:00:07 time: 0.0257 data_time: 0.0004 memory: 1709 2024/03/18 07:23:24 - mmengine - INFO - Epoch(val) [80][400/625] eta: 0:00:05 time: 0.0256 data_time: 0.0004 memory: 1709 2024/03/18 07:23:25 - mmengine - INFO - Epoch(val) [80][450/625] eta: 0:00:04 time: 0.0254 data_time: 0.0004 memory: 1709 2024/03/18 07:23:26 - mmengine - INFO - Epoch(val) [80][500/625] eta: 0:00:03 time: 0.0257 data_time: 0.0004 memory: 1709 2024/03/18 07:23:27 - mmengine - INFO - Epoch(val) [80][550/625] eta: 0:00:01 time: 0.0256 data_time: 0.0004 memory: 1709 2024/03/18 07:23:29 - mmengine - INFO - Epoch(val) [80][600/625] eta: 0:00:00 time: 0.0257 data_time: 0.0004 memory: 1709 2024/03/18 07:23:40 - mmengine - INFO - Evaluating bbox... 2024/03/18 07:24:49 - mmengine - INFO - bbox_mAP_copypaste: 0.533 0.703 0.580 0.361 0.580 0.693 2024/03/18 07:24:51 - mmengine - INFO - Epoch(val) [80][625/625] coco/bbox_mAP: 0.5330 coco/bbox_mAP_50: 0.7030 coco/bbox_mAP_75: 0.5800 coco/bbox_mAP_s: 0.3610 coco/bbox_mAP_m: 0.5800 coco/bbox_mAP_l: 0.6930 data_time: 0.0004 time: 0.0270