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'