| num_batch_size = 4 | |
| num_epochs = 15 | |
| num_frozen_stages = 2 | |
| auto_scale_lr = dict(base_batch_size=2, enable=False) | |
| backend_args = None | |
| data_root = 'C:/vs_code_workspaces/mmdetection/mmdetection/data/ins/v9' | |
| dataset_type = 'CocoDataset' | |
| default_hooks = dict( | |
| checkpoint=dict(interval=1, type='CheckpointHook'), | |
| logger=dict(interval=50, type='LoggerHook'), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| timer=dict(type='IterTimerHook'), | |
| visualization=dict(type='DetVisualizationHook')) | |
| default_scope = 'mmdet' | |
| env_cfg = dict(cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) | |
| launcher = 'none' | |
| load_from = 'C:/vs_code_workspaces/mmdetection/mmdetection/ins_development/resources/add300_frozen2/epoch_9.pth' | |
| log_level = 'INFO' | |
| log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) | |
| metainfo = dict(classes=('waste', ), palette=[ (220, 20, 60, ),]) | |
| model = dict( | |
| backbone=dict( | |
| depth=101, | |
| frozen_stages=num_frozen_stages, | |
| init_cfg=dict(checkpoint='C:/Users/INS/.cache/torch/hub/checkpoints/resnet101-63fe2227.pth', type='Pretrained'), | |
| norm_cfg=dict(requires_grad=True, type='BN'), | |
| norm_eval=True, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3, ), | |
| style='pytorch', | |
| type='ResNet'), | |
| data_preprocessor=dict( | |
| bgr_to_rgb=True, | |
| mean=[123.675, 116.28, 103.53, ], | |
| pad_size_divisor=32, | |
| std=[58.395, 57.12, 57.375, ], | |
| type='DetDataPreprocessor'), | |
| neck=dict(in_channels=[256, 512, 1024, 2048, ], | |
| num_outs=5, | |
| out_channels=256, | |
| type='FPN'), | |
| roi_head=dict( | |
| bbox_head=dict( | |
| bbox_coder=dict( | |
| target_means=[0.0, 0.0, 0.0, 0.0,], | |
| target_stds=[0.1, 0.1, 0.2, 0.2,], | |
| type='DeltaXYWHBBoxCoder'), | |
| fc_out_channels=1024, | |
| in_channels=256, | |
| loss_bbox=dict(loss_weight=1.0, type='L1Loss'), | |
| loss_cls=dict( | |
| loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), | |
| num_classes=1, | |
| reg_class_agnostic=False, | |
| roi_feat_size=7, | |
| type='Shared2FCBBoxHead'), | |
| bbox_roi_extractor=dict( | |
| featmap_strides=[4, 8, 16, 32, ], | |
| out_channels=256, | |
| roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'), | |
| type='SingleRoIExtractor'), | |
| type='StandardRoIHead'), | |
| rpn_head=dict( | |
| anchor_generator=dict( | |
| ratios=[0.5, 1.0, 2.0, ], | |
| scales=[8,], | |
| strides=[4, 8, 16, 32, 64, ], | |
| type='AnchorGenerator'), | |
| bbox_coder=dict( | |
| target_means=[0.0, 0.0, 0.0, 0.0, ], | |
| target_stds=[1.0, 1.0, 1.0, 1.0, ], | |
| type='DeltaXYWHBBoxCoder'), | |
| feat_channels=256, | |
| in_channels=256, | |
| loss_bbox=dict(loss_weight=1.0, type='L1Loss'), | |
| loss_cls=dict(loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True), | |
| type='RPNHead'), | |
| test_cfg=dict( | |
| rcnn=dict( | |
| max_per_img=100, | |
| nms=dict(iou_threshold=0.5, type='nms'), | |
| score_thr=0.05), | |
| rpn=dict( | |
| max_per_img=1000, | |
| min_bbox_size=0, | |
| nms=dict(iou_threshold=0.7, type='nms'), | |
| nms_pre=1000)), | |
| train_cfg=dict( | |
| rcnn=dict( | |
| assigner=dict( | |
| ignore_iof_thr=-1, | |
| match_low_quality=False, | |
| min_pos_iou=0.5, | |
| neg_iou_thr=0.5, | |
| pos_iou_thr=0.5, | |
| type='MaxIoUAssigner'), | |
| debug=False, | |
| pos_weight=-1, | |
| sampler=dict( | |
| add_gt_as_proposals=True, | |
| neg_pos_ub=-1, | |
| num=512, | |
| pos_fraction=0.25, | |
| type='RandomSampler')), | |
| rpn=dict( | |
| allowed_border=-1, | |
| assigner=dict( | |
| ignore_iof_thr=-1, | |
| match_low_quality=True, | |
| min_pos_iou=0.3, | |
| neg_iou_thr=0.3, | |
| pos_iou_thr=0.7, | |
| type='MaxIoUAssigner'), | |
| debug=False, | |
| pos_weight=-1, | |
| sampler=dict( | |
| add_gt_as_proposals=False, | |
| neg_pos_ub=-1, | |
| num=256, | |
| pos_fraction=0.5, | |
| type='RandomSampler')), | |
| rpn_proposal=dict( | |
| max_per_img=1000, | |
| min_bbox_size=0, | |
| nms=dict(iou_threshold=0.7, type='nms'), | |
| nms_pre=2000)), | |
| type='FasterRCNN') | |
| optim_wrapper = dict( | |
| optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001), | |
| type='OptimWrapper') | |
| param_scheduler = [ | |
| dict(begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'), | |
| dict(begin=0, by_epoch=True, end=12, gamma=0.1, milestones=[8, 11, ], type='MultiStepLR'), | |
| ] | |
| resume = False | |
| test_cfg = dict(type='TestLoop') | |
| test_dataloader = dict( | |
| batch_size=num_batch_size, | |
| dataset=dict( | |
| ann_file='test/annotations_coco.json', | |
| backend_args=None, | |
| data_prefix=dict(img='test/'), | |
| data_root=data_root, | |
| metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ), ]), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'), | |
| ], | |
| test_mode=True, | |
| type='CocoDataset'), | |
| drop_last=False, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| test_evaluator = dict( | |
| ann_file='data/ins_annotated_v9/test/annotations_coco.json', | |
| backend_args=None, | |
| format_only=False, | |
| metric='bbox', | |
| type='CocoMetric') | |
| test_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',),type='PackDetInputs'), | |
| ] | |
| train_cfg = dict(max_epochs=num_epochs, type='EpochBasedTrainLoop', val_interval=1) | |
| train_dataloader = dict( | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| batch_size=num_batch_size, | |
| dataset=dict( | |
| ann_file='train/annotations_coco.json', | |
| backend_args=None, | |
| data_prefix=dict(img='train/'), | |
| data_root=data_root, | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PackDetInputs'), | |
| ], | |
| type='CocoDataset'), | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=True, type='DefaultSampler')) | |
| train_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict(type='PackDetInputs'), | |
| ] | |
| val_cfg = dict(type='ValLoop') | |
| val_dataloader = dict( | |
| batch_size=num_batch_size, | |
| dataset=dict( | |
| ann_file='valid/annotations_coco.json', | |
| backend_args=None, | |
| data_prefix=dict(img='valid/'), | |
| data_root=data_root, | |
| metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ),type='PackDetInputs'), | |
| ], | |
| test_mode=True, | |
| type='CocoDataset'), | |
| drop_last=False, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=False, type='DefaultSampler')) | |
| val_evaluator = dict( | |
| ann_file='data/ins_annotated_v9/valid/annotations_coco.json', | |
| backend_args=None, | |
| format_only=False, | |
| metric='bbox', | |
| type='CocoMetric') | |
| val_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'), | |
| ] | |
| vis_backends = [dict(type='LocalVisBackend'), ] | |
| visualizer = dict(name='visualizer', type='DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend'), ]) | |
| work_dir = './ins_development/training/ins_annotated_v9/pretrained/add300/faster/2frozen/e9\\' | |