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| dataset_type = 'CocoDataset' | |
| data_root = '/home/safouane/Downloads/benchmark_aircraft/data/' | |
| backend_args = None | |
| max_epochs = 500 | |
| metainfo = dict( | |
| classes=('airplane', ), palette=[ | |
| ( | |
| 0, | |
| 0, | |
| 255, | |
| ), | |
| ]) | |
| num_classes = 1 | |
| batch_size = 128 | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='Expand', | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| to_rgb=True, | |
| ratio_range=( | |
| 1, | |
| 4, | |
| )), | |
| dict( | |
| type='MinIoURandomCrop', | |
| min_ious=( | |
| 0.1, | |
| 0.3, | |
| 0.5, | |
| 0.7, | |
| 0.9, | |
| ), | |
| min_crop_size=0.3), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=False), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='PhotoMetricDistortion', | |
| brightness_delta=32, | |
| contrast_range=( | |
| 0.5, | |
| 1.5, | |
| ), | |
| saturation_range=( | |
| 0.5, | |
| 1.5, | |
| ), | |
| hue_delta=18), | |
| dict(type='PackDetInputs'), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=False), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| )), | |
| ] | |
| train_dataloader = dict( | |
| batch_size=128, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=None, | |
| dataset=dict( | |
| type='RepeatDataset', | |
| times=5, | |
| dataset=dict( | |
| type='CocoDataset', | |
| metainfo=dict(classes=('airplane', ), palette=[ | |
| ( | |
| 220, | |
| 20, | |
| 60, | |
| ), | |
| ]), | |
| data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
| ann_file='train/__coco.json', | |
| data_prefix=dict(img='train/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='Expand', | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| to_rgb=True, | |
| ratio_range=( | |
| 1, | |
| 4, | |
| )), | |
| dict( | |
| type='MinIoURandomCrop', | |
| min_ious=( | |
| 0.1, | |
| 0.3, | |
| 0.5, | |
| 0.7, | |
| 0.9, | |
| ), | |
| min_crop_size=0.3), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=False), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='PhotoMetricDistortion', | |
| brightness_delta=32, | |
| contrast_range=( | |
| 0.5, | |
| 1.5, | |
| ), | |
| saturation_range=( | |
| 0.5, | |
| 1.5, | |
| ), | |
| hue_delta=18), | |
| dict(type='PackDetInputs'), | |
| ]))) | |
| val_dataloader = dict( | |
| batch_size=128, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| metainfo=dict(classes=('airplane', ), palette=[ | |
| ( | |
| 220, | |
| 20, | |
| 60, | |
| ), | |
| ]), | |
| data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
| ann_file='val/__coco.json', | |
| data_prefix=dict(img='val/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=False), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| )), | |
| ], | |
| backend_args=None)) | |
| test_dataloader = dict( | |
| batch_size=128, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| metainfo=dict(classes=('airplane', ), palette=[ | |
| ( | |
| 220, | |
| 20, | |
| 60, | |
| ), | |
| ]), | |
| data_root='/home/safouane/Downloads/benchmark_aircraft/data/', | |
| ann_file='test/__coco.json', | |
| data_prefix=dict(img='test/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=False), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| )), | |
| ], | |
| backend_args=None)) | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file='/home/safouane/Downloads/benchmark_aircraft/data/val/__coco.json', | |
| metric='bbox', | |
| format_only=False, | |
| backend_args=None) | |
| test_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file= | |
| '/home/safouane/Downloads/benchmark_aircraft/data/test/__coco.json', | |
| metric='bbox', | |
| format_only=False, | |
| backend_args=None) | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=1) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), | |
| dict( | |
| type='CosineAnnealingLR', | |
| begin=0, | |
| T_max=120, | |
| end=120, | |
| by_epoch=True, | |
| eta_min=0), | |
| ] | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4e-05)) | |
| auto_scale_lr = dict(enable=False, base_batch_size=64) | |
| default_scope = 'mmdet' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=20, save_best='auto'), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='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='DetLocalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| dict(type='TensorboardVisBackend'), | |
| ], | |
| name='visualizer') | |
| log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) | |
| log_level = 'INFO' | |
| load_from = '/home/safouane/Downloads/benchmark_aircraft/mmdetection/configs/ssd/checkpoints/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth' | |
| resume = False | |
| data_preprocessor = dict( | |
| type='DetDataPreprocessor', | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| bgr_to_rgb=True, | |
| pad_size_divisor=1) | |
| model = dict( | |
| type='SingleStageDetector', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| bgr_to_rgb=True, | |
| pad_size_divisor=1), | |
| backbone=dict( | |
| type='MobileNetV2', | |
| out_indices=( | |
| 4, | |
| 7, | |
| ), | |
| norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), | |
| init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)), | |
| neck=dict( | |
| type='SSDNeck', | |
| in_channels=( | |
| 96, | |
| 1280, | |
| ), | |
| out_channels=( | |
| 96, | |
| 1280, | |
| 512, | |
| 256, | |
| 256, | |
| 128, | |
| ), | |
| level_strides=( | |
| 2, | |
| 2, | |
| 2, | |
| 2, | |
| ), | |
| level_paddings=( | |
| 1, | |
| 1, | |
| 1, | |
| 1, | |
| ), | |
| l2_norm_scale=None, | |
| use_depthwise=True, | |
| norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), | |
| act_cfg=dict(type='ReLU6'), | |
| init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)), | |
| bbox_head=dict( | |
| type='SSDHead', | |
| in_channels=( | |
| 96, | |
| 1280, | |
| 512, | |
| 256, | |
| 256, | |
| 128, | |
| ), | |
| num_classes=1, | |
| use_depthwise=True, | |
| norm_cfg=dict(type='BN', eps=0.001, momentum=0.03), | |
| act_cfg=dict(type='ReLU6'), | |
| init_cfg=dict(type='Normal', layer='Conv2d', std=0.001), | |
| anchor_generator=dict( | |
| type='SSDAnchorGenerator', | |
| scale_major=False, | |
| strides=[ | |
| 16, | |
| 32, | |
| 64, | |
| 107, | |
| 160, | |
| 320, | |
| ], | |
| ratios=[ | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| [ | |
| 2, | |
| 3, | |
| ], | |
| ], | |
| min_sizes=[ | |
| 48, | |
| 100, | |
| 150, | |
| 202, | |
| 253, | |
| 304, | |
| ], | |
| max_sizes=[ | |
| 100, | |
| 150, | |
| 202, | |
| 253, | |
| 304, | |
| 320, | |
| ]), | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[ | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| 0.0, | |
| ], | |
| target_stds=[ | |
| 0.1, | |
| 0.1, | |
| 0.2, | |
| 0.2, | |
| ])), | |
| train_cfg=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.0, | |
| ignore_iof_thr=-1, | |
| gt_max_assign_all=False), | |
| sampler=dict(type='PseudoSampler'), | |
| smoothl1_beta=1.0, | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| neg_pos_ratio=3, | |
| debug=False), | |
| test_cfg=dict( | |
| nms_pre=1000, | |
| nms=dict(type='nms', iou_threshold=0.45), | |
| min_bbox_size=0, | |
| score_thr=0.02, | |
| max_per_img=200)) | |
| input_size = 320 | |
| custom_hooks = [ | |
| dict(type='NumClassCheckHook'), | |
| dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW'), | |
| ] | |
| launcher = 'none' | |
| work_dir = './work_dirs/ssdlite_mobilenetv2-scratch_8xb24-600e_coco' | |