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'