Spaces:
Build error
Build error
| dataset_type = 'CocoDataset' | |
| data_root = '/home/safouane/Downloads/benchmark_aircraft/data/' | |
| backend_args = None | |
| max_epochs = 500 | |
| metainfo = { | |
| 'classes': ('airplane', ), | |
| 'palette': [ | |
| (0, 128, 255), | |
| ] | |
| } | |
| num_classes = 1 | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='RandomChoice', | |
| transforms=[ | |
| [ | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| ], | |
| [ | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 400, | |
| 1333, | |
| ), | |
| ( | |
| 500, | |
| 1333, | |
| ), | |
| ( | |
| 600, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='absolute_range', | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| allow_negative_crop=True), | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| ], | |
| ]), | |
| dict(type='PackDetInputs'), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='Resize', scale=( | |
| 1333, | |
| 800, | |
| ), keep_ratio=True), | |
| 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=8, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| 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', backend_args=None), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='RandomChoice', | |
| transforms=[ | |
| [ | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| ], | |
| [ | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 400, | |
| 1333, | |
| ), | |
| ( | |
| 500, | |
| 1333, | |
| ), | |
| ( | |
| 600, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| dict( | |
| type='RandomCrop', | |
| crop_type='absolute_range', | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| allow_negative_crop=True), | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| keep_ratio=True), | |
| ], | |
| ]), | |
| dict(type='PackDetInputs'), | |
| ], | |
| backend_args=None)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| 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', backend_args=None), | |
| dict(type='Resize', scale=( | |
| 1333, | |
| 800, | |
| ), keep_ratio=True), | |
| 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=1, | |
| 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', backend_args=None), | |
| dict(type='Resize', scale=( | |
| 1333, | |
| 800, | |
| ), keep_ratio=True), | |
| 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) | |
| default_scope = 'mmdet' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=5), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=5, save_best='auto'), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='DetVisualizationHook')) | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| 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/detr/checkpoints/detr_r50_8xb2-150e_coco_20221023_153551-436d03e8.pth' | |
| resume = False | |
| model = dict( | |
| type='DETR', | |
| num_queries=100, | |
| 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='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(3, ), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True, | |
| style='pytorch', | |
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), | |
| neck=dict( | |
| type='ChannelMapper', | |
| in_channels=[ | |
| 2048, | |
| ], | |
| kernel_size=1, | |
| out_channels=256, | |
| act_cfg=None, | |
| norm_cfg=None, | |
| num_outs=1), | |
| encoder=dict( | |
| num_layers=6, | |
| layer_cfg=dict( | |
| self_attn_cfg=dict( | |
| embed_dims=256, num_heads=8, dropout=0.1, batch_first=True), | |
| ffn_cfg=dict( | |
| embed_dims=256, | |
| feedforward_channels=2048, | |
| num_fcs=2, | |
| ffn_drop=0.1, | |
| act_cfg=dict(type='ReLU', inplace=True)))), | |
| decoder=dict( | |
| num_layers=6, | |
| layer_cfg=dict( | |
| self_attn_cfg=dict( | |
| embed_dims=256, num_heads=8, dropout=0.1, batch_first=True), | |
| cross_attn_cfg=dict( | |
| embed_dims=256, num_heads=8, dropout=0.1, batch_first=True), | |
| ffn_cfg=dict( | |
| embed_dims=256, | |
| feedforward_channels=2048, | |
| num_fcs=2, | |
| ffn_drop=0.1, | |
| act_cfg=dict(type='ReLU', inplace=True))), | |
| return_intermediate=True), | |
| positional_encoding=dict(num_feats=128, normalize=True), | |
| bbox_head=dict( | |
| type='DETRHead', | |
| num_classes=1, | |
| embed_dims=256, | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', | |
| bg_cls_weight=0.1, | |
| use_sigmoid=False, | |
| loss_weight=1.0, | |
| class_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=5.0), | |
| loss_iou=dict(type='GIoULoss', loss_weight=2.0)), | |
| train_cfg=dict( | |
| assigner=dict( | |
| type='HungarianAssigner', | |
| match_costs=[ | |
| dict(type='ClassificationCost', weight=1.0), | |
| dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), | |
| dict(type='IoUCost', iou_mode='giou', weight=2.0), | |
| ])), | |
| test_cfg=dict(max_per_img=100)) | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001), | |
| clip_grad=dict(max_norm=0.1, norm_type=2), | |
| paramwise_cfg=dict( | |
| custom_keys=dict(backbone=dict(lr_mult=0.1, decay_mult=1.0)))) | |
| 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='MultiStepLR', | |
| begin=0, | |
| end=150, | |
| by_epoch=True, | |
| milestones=[ | |
| 100, | |
| ], | |
| gamma=0.1), | |
| ] | |
| auto_scale_lr = dict(base_batch_size=16) | |
| launcher = 'none' | |
| work_dir = './work_dirs/detr_r50_8xb2-150e_coco' | |