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| default_scope = 'mmdet' | |
| dataset_type = 'CocoDataset' | |
| data_root = '/home/safouane/Downloads/benchmark_aircraft/data/' | |
| backend_args = None | |
| batch_size = 64 | |
| max_epochs = 300 | |
| metainfo = { | |
| 'classes': ('airplane', ), | |
| 'palette': [ | |
| (0, 128, 255), | |
| ] | |
| } | |
| num_classes = 1 | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=10, max_keep_ckpts=3), | |
| 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/rtmdet/checkpoints/rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth' | |
| resume = False | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=10) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0, | |
| end=1000), | |
| dict( | |
| type='CosineAnnealingLR', | |
| eta_min=0.0002, | |
| begin=150, | |
| end=300, | |
| T_max=150, | |
| by_epoch=True, | |
| convert_to_iter_based=True), | |
| ] | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05), | |
| paramwise_cfg=dict( | |
| norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='CachedMosaic', | |
| img_scale=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=114.0, | |
| max_cached_images=20, | |
| random_pop=False), | |
| dict( | |
| type='RandomResize', | |
| scale=( | |
| 1280, | |
| 1280, | |
| ), | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=( | |
| 640, | |
| 640, | |
| )), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='Pad', size=( | |
| 640, | |
| 640, | |
| ), pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| dict( | |
| type='CachedMixUp', | |
| img_scale=( | |
| 640, | |
| 640, | |
| ), | |
| ratio_range=( | |
| 1.0, | |
| 1.0, | |
| ), | |
| max_cached_images=10, | |
| random_pop=False, | |
| pad_val=( | |
| 114, | |
| 114, | |
| 114, | |
| ), | |
| prob=0.5), | |
| dict(type='PackDetInputs'), | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='Resize', scale=( | |
| 640, | |
| 640, | |
| ), keep_ratio=True), | |
| dict(type='Pad', size=( | |
| 640, | |
| 640, | |
| ), pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| 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=64, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=None, | |
| 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='CachedMosaic', | |
| img_scale=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=114.0, | |
| max_cached_images=20, | |
| random_pop=False), | |
| dict( | |
| type='RandomResize', | |
| scale=( | |
| 1280, | |
| 1280, | |
| ), | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=( | |
| 640, | |
| 640, | |
| )), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='Pad', | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| dict( | |
| type='CachedMixUp', | |
| img_scale=( | |
| 640, | |
| 640, | |
| ), | |
| ratio_range=( | |
| 1.0, | |
| 1.0, | |
| ), | |
| max_cached_images=10, | |
| random_pop=False, | |
| pad_val=( | |
| 114, | |
| 114, | |
| 114, | |
| ), | |
| prob=0.5), | |
| dict(type='PackDetInputs'), | |
| ], | |
| backend_args=None), | |
| pin_memory=True) | |
| val_dataloader = dict( | |
| batch_size=64, | |
| 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=( | |
| 640, | |
| 640, | |
| ), keep_ratio=True), | |
| dict( | |
| type='Pad', | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| 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=64, | |
| 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=( | |
| 640, | |
| 640, | |
| ), keep_ratio=True), | |
| dict( | |
| type='Pad', | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| 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) | |
| tta_model = dict( | |
| type='DetTTAModel', | |
| tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100)) | |
| img_scales = [ | |
| ( | |
| 640, | |
| 640, | |
| ), | |
| ( | |
| 320, | |
| 320, | |
| ), | |
| ( | |
| 960, | |
| 960, | |
| ), | |
| ] | |
| tta_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict( | |
| type='TestTimeAug', | |
| transforms=[ | |
| [ | |
| dict(type='Resize', scale=( | |
| 640, | |
| 640, | |
| ), keep_ratio=True), | |
| dict(type='Resize', scale=( | |
| 320, | |
| 320, | |
| ), keep_ratio=True), | |
| dict(type='Resize', scale=( | |
| 960, | |
| 960, | |
| ), keep_ratio=True), | |
| ], | |
| [ | |
| dict(type='RandomFlip', prob=1.0), | |
| dict(type='RandomFlip', prob=0.0), | |
| ], | |
| [ | |
| dict( | |
| type='Pad', | |
| size=( | |
| 960, | |
| 960, | |
| ), | |
| pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| ], | |
| [ | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| ], | |
| [ | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=( | |
| 'img_id', | |
| 'img_path', | |
| 'ori_shape', | |
| 'img_shape', | |
| 'scale_factor', | |
| 'flip', | |
| 'flip_direction', | |
| )), | |
| ], | |
| ]), | |
| ] | |
| model = dict( | |
| type='RTMDet', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[ | |
| 103.53, | |
| 116.28, | |
| 123.675, | |
| ], | |
| std=[ | |
| 57.375, | |
| 57.12, | |
| 58.395, | |
| ], | |
| bgr_to_rgb=False, | |
| batch_augments=None), | |
| backbone=dict( | |
| type='CSPNeXt', | |
| arch='P5', | |
| expand_ratio=0.5, | |
| deepen_factor=0.167, | |
| widen_factor=0.375, | |
| channel_attention=True, | |
| norm_cfg=dict(type='SyncBN'), | |
| act_cfg=dict(type='SiLU', inplace=True), | |
| init_cfg=dict( | |
| type='Pretrained', | |
| prefix='backbone.', | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' | |
| )), | |
| neck=dict( | |
| type='CSPNeXtPAFPN', | |
| in_channels=[ | |
| 96, | |
| 192, | |
| 384, | |
| ], | |
| out_channels=96, | |
| num_csp_blocks=1, | |
| expand_ratio=0.5, | |
| norm_cfg=dict(type='SyncBN'), | |
| act_cfg=dict(type='SiLU', inplace=True)), | |
| bbox_head=dict( | |
| type='RTMDetSepBNHead', | |
| num_classes=1, | |
| in_channels=96, | |
| stacked_convs=2, | |
| feat_channels=96, | |
| anchor_generator=dict( | |
| type='MlvlPointGenerator', offset=0, strides=[ | |
| 8, | |
| 16, | |
| 32, | |
| ]), | |
| bbox_coder=dict(type='DistancePointBBoxCoder'), | |
| loss_cls=dict( | |
| type='QualityFocalLoss', | |
| use_sigmoid=True, | |
| beta=2.0, | |
| loss_weight=1.0), | |
| loss_bbox=dict(type='GIoULoss', loss_weight=2.0), | |
| with_objectness=False, | |
| exp_on_reg=False, | |
| share_conv=True, | |
| pred_kernel_size=1, | |
| norm_cfg=dict(type='SyncBN'), | |
| act_cfg=dict(type='SiLU', inplace=True)), | |
| train_cfg=dict( | |
| assigner=dict(type='DynamicSoftLabelAssigner', topk=13), | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| debug=False), | |
| test_cfg=dict( | |
| nms_pre=30000, | |
| min_bbox_size=0, | |
| score_thr=0.001, | |
| nms=dict(type='nms', iou_threshold=0.65), | |
| max_per_img=300)) | |
| train_pipeline_stage2 = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='RandomResize', | |
| scale=( | |
| 640, | |
| 640, | |
| ), | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=( | |
| 640, | |
| 640, | |
| )), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='Pad', size=( | |
| 640, | |
| 640, | |
| ), pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| dict(type='PackDetInputs'), | |
| ] | |
| stage2_num_epochs = 20 | |
| base_lr = 0.004 | |
| interval = 10 | |
| custom_hooks = [ | |
| dict( | |
| type='EMAHook', | |
| ema_type='ExpMomentumEMA', | |
| momentum=0.0002, | |
| update_buffers=True, | |
| priority=49), | |
| dict( | |
| type='PipelineSwitchHook', | |
| switch_epoch=280, | |
| switch_pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict( | |
| type='RandomResize', | |
| scale=( | |
| 640, | |
| 640, | |
| ), | |
| ratio_range=( | |
| 0.5, | |
| 2.0, | |
| ), | |
| keep_ratio=True), | |
| dict(type='RandomCrop', crop_size=( | |
| 640, | |
| 640, | |
| )), | |
| dict(type='YOLOXHSVRandomAug'), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict( | |
| type='Pad', | |
| size=( | |
| 640, | |
| 640, | |
| ), | |
| pad_val=dict(img=( | |
| 114, | |
| 114, | |
| 114, | |
| ))), | |
| dict(type='PackDetInputs'), | |
| ]), | |
| ] | |
| checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' | |
| launcher = 'none' | |
| work_dir = './work_dirs/rtmdet_tiny_8xb32-300e_coco' | |