| auto_scale_lr = dict(base_batch_size=16) | |
| backend_args = None | |
| data_root = '/kaggle/working/mmdetection/dataset/' | |
| 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 = '/kaggle/working/mmdetection/work_dirs/dino-4scale_resnet50_8xb2-12e_coco/epoch_6.pth' | |
| log_level = 'INFO' | |
| log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50) | |
| max_epochs = 12 | |
| metainfo = dict(classes=[ | |
| '000_aveda_shampoo', | |
| '001_binder_clips_median', | |
| '002_binder_clips_small', | |
| '003_bombik_bucket', | |
| '004_bonne_maman_blueberry', | |
| '005_bonne_maman_raspberry', | |
| '006_bonne_maman_strawberry', | |
| '007_costa_caramel', | |
| '008_essential_oil_bergamot', | |
| '009_garlic_toast_spread', | |
| '010_handcream_avocado', | |
| '011_hb_calcium', | |
| '012_hb_grapeseed', | |
| '013_hb_marine_collagen', | |
| '014_hellmanns_mayonnaise', | |
| '015_illy_blend', | |
| '016_japanese_finger_cookies', | |
| '017_john_west_canned_tuna', | |
| '018_kerastase_shampoo', | |
| '019_kiehls_facial_cream', | |
| '020_kiihne_balsamic', | |
| '021_kiihne_honey_mustard', | |
| '022_lindor_matcha', | |
| '023_lindor_salted_caramel', | |
| '024_lush_mask', | |
| '025_pasta_sauce_black_pepper', | |
| '026_pasta_sauce_tomato', | |
| '027_pepsi', | |
| '028_portable_yogurt_machine', | |
| '029_selfile_stick', | |
| '030_sour_lemon_drops', | |
| '031_sticky_notes', | |
| '032_stridex_green', | |
| '033_thermos_flask_cream', | |
| '034_thermos_flask_muji', | |
| '035_thermos_flask_sliver', | |
| '036_tragata_olive_oil', | |
| '037_tulip_luncheon_meat', | |
| '038_unicharm_cotton_pad', | |
| '039_vinda_tissue', | |
| '040_wrigley_doublemint_gum', | |
| '041_baseball_cap_black', | |
| '042_baseball_cap_pink', | |
| '043_bfe_facial_mask', | |
| '044_corgi_doll', | |
| '045_dinosaur_doll', | |
| '046_geo_mocha', | |
| '047_geo_roast_charcoal', | |
| '048_instant_noodle_black', | |
| '049_instant_noodle_red', | |
| '050_nabati_cheese_wafer', | |
| '051_truffettes', | |
| '052_acnes_cream', | |
| '053_aveda_conditioner', | |
| '054_banana_milk_drink', | |
| '055_candle_beast', | |
| '056_china_persimmon', | |
| '057_danisa_butter_cookies', | |
| '058_effaclar_duo', | |
| '059_evelom_cleanser', | |
| '060_glasses_box_blone', | |
| '061_handcream_iris', | |
| '062_handcream_lavender', | |
| '063_handcream_rosewater', | |
| '064_handcream_summer_hill', | |
| '065_hr_serum', | |
| '066_japanese_chocolate', | |
| '067_kerastase_hair_treatment', | |
| '068_kiehls_serum', | |
| '069_korean_beef_marinade', | |
| '070_korean_doenjang', | |
| '071_korean_gochujang', | |
| '072_korean_ssamjang', | |
| '073_loccitane_soap', | |
| '074_marvis_toothpaste_purple', | |
| '075_mouse_thinkpad', | |
| '076_oatly_chocolate', | |
| '077_oatly_original', | |
| '078_ousa_grated_cheese', | |
| '079_polaroid_film', | |
| '080_skinceuticals_be', | |
| '081_skinceuticals_cf', | |
| '082_skinceuticals_phyto', | |
| '083_stapler_black', | |
| '084_stapler_blue', | |
| '085_sunscreen_blue', | |
| '086_tempo_pocket_tissue', | |
| '087_thermos_flask_purple', | |
| '088_uha_matcha', | |
| '089_urban_decay_spray', | |
| '090_vitaboost_multivitamin', | |
| '091_watercolor_penbox', | |
| '092_youthlt_bilberry_complex', | |
| '093_daiso_mod_remover', | |
| '094_kaneyo_kitchen_bleach', | |
| '095_lays_chip_bag_blue', | |
| '096_lays_chip_bag_green', | |
| '097_lays_chip_tube_auburn', | |
| '098_lays_chip_tube_green', | |
| '099_mug_blue', | |
| ]) | |
| model = dict( | |
| as_two_stage=True, | |
| backbone=dict( | |
| depth=50, | |
| frozen_stages=1, | |
| init_cfg=dict(checkpoint='torchvision://resnet50', type='Pretrained'), | |
| norm_cfg=dict(requires_grad=False, type='BN'), | |
| norm_eval=True, | |
| num_stages=4, | |
| out_indices=( | |
| 0, | |
| 1, | |
| 2, | |
| 3, | |
| ), | |
| style='pytorch', | |
| type='ResNet', | |
| with_cp=True), | |
| bbox_head=dict( | |
| loss_bbox=dict(loss_weight=5.0, type='L1Loss'), | |
| loss_cls=dict( | |
| alpha=0.25, | |
| gamma=2.0, | |
| loss_weight=1.0, | |
| type='FocalLoss', | |
| use_sigmoid=True), | |
| loss_iou=dict(loss_weight=2.0, type='GIoULoss'), | |
| num_classes=100, | |
| sync_cls_avg_factor=True, | |
| type='DINOHead'), | |
| data_preprocessor=dict( | |
| bgr_to_rgb=True, | |
| mean=[ | |
| 123.675, | |
| 116.28, | |
| 103.53, | |
| ], | |
| pad_size_divisor=1, | |
| std=[ | |
| 58.395, | |
| 57.12, | |
| 57.375, | |
| ], | |
| type='DetDataPreprocessor'), | |
| decoder=dict( | |
| layer_cfg=dict( | |
| cross_attn_cfg=dict(dropout=0.0, embed_dims=256, num_levels=4), | |
| ffn_cfg=dict( | |
| embed_dims=256, feedforward_channels=2048, ffn_drop=0.0), | |
| self_attn_cfg=dict(dropout=0.0, embed_dims=256, num_heads=8)), | |
| num_layers=6, | |
| post_norm_cfg=None, | |
| return_intermediate=True), | |
| dn_cfg=dict( | |
| box_noise_scale=1.0, | |
| group_cfg=dict(dynamic=True, num_dn_queries=100, num_groups=None), | |
| label_noise_scale=0.5), | |
| encoder=dict( | |
| layer_cfg=dict( | |
| ffn_cfg=dict( | |
| embed_dims=256, feedforward_channels=2048, ffn_drop=0.0), | |
| self_attn_cfg=dict(dropout=0.0, embed_dims=256, num_levels=4)), | |
| num_layers=6), | |
| neck=dict( | |
| act_cfg=None, | |
| in_channels=[ | |
| 256, | |
| 512, | |
| 1024, | |
| 2048, | |
| ], | |
| kernel_size=1, | |
| norm_cfg=dict(num_groups=32, type='GN'), | |
| num_outs=4, | |
| out_channels=256, | |
| type='ChannelMapper'), | |
| num_feature_levels=4, | |
| num_queries=900, | |
| positional_encoding=dict( | |
| normalize=True, num_feats=128, offset=0.0, temperature=20), | |
| test_cfg=dict(max_per_img=300), | |
| train_cfg=dict( | |
| assigner=dict( | |
| match_costs=[ | |
| dict(type='FocalLossCost', weight=2.0), | |
| dict(box_format='xywh', type='BBoxL1Cost', weight=5.0), | |
| dict(iou_mode='giou', type='IoUCost', weight=2.0), | |
| ], | |
| type='HungarianAssigner')), | |
| type='DINO', | |
| with_box_refine=True) | |
| num_levels = 4 | |
| optim_wrapper = dict( | |
| clip_grad=dict(max_norm=0.1, norm_type=2), | |
| optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.0001), | |
| paramwise_cfg=dict(custom_keys=dict(backbone=dict(lr_mult=0.1))), | |
| type='OptimWrapper') | |
| param_scheduler = [ | |
| dict( | |
| begin=0, | |
| by_epoch=True, | |
| end=12, | |
| gamma=0.1, | |
| milestones=[ | |
| 11, | |
| ], | |
| type='MultiStepLR'), | |
| ] | |
| resume = False | |
| test_cfg = dict(type='TestLoop') | |
| test_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| ann_file=None, | |
| backend_args=None, | |
| data_prefix=dict(img=''), | |
| data_root='/kaggle/input/insdet-test/InsDet-Test', | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), 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='/kaggle/working/mmdetection/dataset/annotations/val.json', | |
| backend_args=None, | |
| format_only=False, | |
| metric='bbox', | |
| type='CocoMetric') | |
| test_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), 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=6, type='EpochBasedTrainLoop', val_interval=1) | |
| train_dataloader = dict( | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| batch_size=1, | |
| dataset=dict( | |
| ann_file='annotations/train.json', | |
| backend_args=None, | |
| data_prefix=dict(img='train/'), | |
| data_root='/kaggle/working/mmdetection/dataset/', | |
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | |
| metainfo=dict(classes=[ | |
| '000_aveda_shampoo', | |
| '001_binder_clips_median', | |
| '002_binder_clips_small', | |
| '003_bombik_bucket', | |
| '004_bonne_maman_blueberry', | |
| '005_bonne_maman_raspberry', | |
| '006_bonne_maman_strawberry', | |
| '007_costa_caramel', | |
| '008_essential_oil_bergamot', | |
| '009_garlic_toast_spread', | |
| '010_handcream_avocado', | |
| '011_hb_calcium', | |
| '012_hb_grapeseed', | |
| '013_hb_marine_collagen', | |
| '014_hellmanns_mayonnaise', | |
| '015_illy_blend', | |
| '016_japanese_finger_cookies', | |
| '017_john_west_canned_tuna', | |
| '018_kerastase_shampoo', | |
| '019_kiehls_facial_cream', | |
| '020_kiihne_balsamic', | |
| '021_kiihne_honey_mustard', | |
| '022_lindor_matcha', | |
| '023_lindor_salted_caramel', | |
| '024_lush_mask', | |
| '025_pasta_sauce_black_pepper', | |
| '026_pasta_sauce_tomato', | |
| '027_pepsi', | |
| '028_portable_yogurt_machine', | |
| '029_selfile_stick', | |
| '030_sour_lemon_drops', | |
| '031_sticky_notes', | |
| '032_stridex_green', | |
| '033_thermos_flask_cream', | |
| '034_thermos_flask_muji', | |
| '035_thermos_flask_sliver', | |
| '036_tragata_olive_oil', | |
| '037_tulip_luncheon_meat', | |
| '038_unicharm_cotton_pad', | |
| '039_vinda_tissue', | |
| '040_wrigley_doublemint_gum', | |
| '041_baseball_cap_black', | |
| '042_baseball_cap_pink', | |
| '043_bfe_facial_mask', | |
| '044_corgi_doll', | |
| '045_dinosaur_doll', | |
| '046_geo_mocha', | |
| '047_geo_roast_charcoal', | |
| '048_instant_noodle_black', | |
| '049_instant_noodle_red', | |
| '050_nabati_cheese_wafer', | |
| '051_truffettes', | |
| '052_acnes_cream', | |
| '053_aveda_conditioner', | |
| '054_banana_milk_drink', | |
| '055_candle_beast', | |
| '056_china_persimmon', | |
| '057_danisa_butter_cookies', | |
| '058_effaclar_duo', | |
| '059_evelom_cleanser', | |
| '060_glasses_box_blone', | |
| '061_handcream_iris', | |
| '062_handcream_lavender', | |
| '063_handcream_rosewater', | |
| '064_handcream_summer_hill', | |
| '065_hr_serum', | |
| '066_japanese_chocolate', | |
| '067_kerastase_hair_treatment', | |
| '068_kiehls_serum', | |
| '069_korean_beef_marinade', | |
| '070_korean_doenjang', | |
| '071_korean_gochujang', | |
| '072_korean_ssamjang', | |
| '073_loccitane_soap', | |
| '074_marvis_toothpaste_purple', | |
| '075_mouse_thinkpad', | |
| '076_oatly_chocolate', | |
| '077_oatly_original', | |
| '078_ousa_grated_cheese', | |
| '079_polaroid_film', | |
| '080_skinceuticals_be', | |
| '081_skinceuticals_cf', | |
| '082_skinceuticals_phyto', | |
| '083_stapler_black', | |
| '084_stapler_blue', | |
| '085_sunscreen_blue', | |
| '086_tempo_pocket_tissue', | |
| '087_thermos_flask_purple', | |
| '088_uha_matcha', | |
| '089_urban_decay_spray', | |
| '090_vitaboost_multivitamin', | |
| '091_watercolor_penbox', | |
| '092_youthlt_bilberry_complex', | |
| '093_daiso_mod_remover', | |
| '094_kaneyo_kitchen_bleach', | |
| '095_lays_chip_bag_blue', | |
| '096_lays_chip_bag_green', | |
| '097_lays_chip_tube_auburn', | |
| '098_lays_chip_tube_green', | |
| '099_mug_blue', | |
| ]), | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 400, | |
| 4200, | |
| ), | |
| ( | |
| 500, | |
| 4200, | |
| ), | |
| ( | |
| 600, | |
| 4200, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| dict( | |
| allow_negative_crop=True, | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| crop_type='absolute_range', | |
| type='RandomCrop'), | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| ], | |
| type='RandomChoice'), | |
| dict(type='PackDetInputs'), | |
| ], | |
| type='CocoDataset'), | |
| num_workers=1, | |
| persistent_workers=True, | |
| sampler=dict(shuffle=True, type='DefaultSampler')) | |
| train_pipeline = [ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(type='LoadAnnotations', with_bbox=True), | |
| dict(prob=0.5, type='RandomFlip'), | |
| dict( | |
| transforms=[ | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| [ | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 400, | |
| 4200, | |
| ), | |
| ( | |
| 500, | |
| 4200, | |
| ), | |
| ( | |
| 600, | |
| 4200, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| dict( | |
| allow_negative_crop=True, | |
| crop_size=( | |
| 384, | |
| 600, | |
| ), | |
| crop_type='absolute_range', | |
| type='RandomCrop'), | |
| dict( | |
| keep_ratio=True, | |
| scales=[ | |
| ( | |
| 480, | |
| 1333, | |
| ), | |
| ( | |
| 512, | |
| 1333, | |
| ), | |
| ( | |
| 544, | |
| 1333, | |
| ), | |
| ( | |
| 576, | |
| 1333, | |
| ), | |
| ( | |
| 608, | |
| 1333, | |
| ), | |
| ( | |
| 640, | |
| 1333, | |
| ), | |
| ( | |
| 672, | |
| 1333, | |
| ), | |
| ( | |
| 704, | |
| 1333, | |
| ), | |
| ( | |
| 736, | |
| 1333, | |
| ), | |
| ( | |
| 768, | |
| 1333, | |
| ), | |
| ( | |
| 800, | |
| 1333, | |
| ), | |
| ], | |
| type='RandomChoiceResize'), | |
| ], | |
| ], | |
| type='RandomChoice'), | |
| dict(type='PackDetInputs'), | |
| ] | |
| val_cfg = dict(type='ValLoop') | |
| val_dataloader = dict( | |
| batch_size=1, | |
| dataset=dict( | |
| ann_file='annotations/instances_val2017.json', | |
| backend_args=None, | |
| data_prefix=dict(img='val2017/'), | |
| data_root='data/coco/', | |
| pipeline=[ | |
| dict(backend_args=None, type='LoadImageFromFile'), | |
| dict(keep_ratio=True, scale=( | |
| 1333, | |
| 800, | |
| ), 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='/kaggle/working/mmdetection/dataset/annotations/val.json', | |
| backend_args=None, | |
| format_only=False, | |
| metric='bbox', | |
| type='CocoMetric') | |
| vis_backends = [ | |
| dict(type='LocalVisBackend'), | |
| ] | |
| visualizer = dict( | |
| name='visualizer', | |
| type='DetLocalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| ]) | |
| work_dir = './work_dirs/dino-4scale_resnet50_8xb2-12e_coco' | |