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+yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth filter=lfs diff=lfs merge=lfs -text

dwpose_tools/models/rtmw-x_8xb320-270e_cocktail14-384x288.py

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+# common setting
+num_keypoints = 133
+input_size = (288, 384)
+
+# runtime
+max_epochs = 270
+stage2_num_epochs = 10
+base_lr = 5e-4
+train_batch_size = 320
+val_batch_size = 32
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=10)
+randomness = dict(seed=21)
+
+# optimizer
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.1),
+    clip_grad=dict(max_norm=35, norm_type=2),
+    paramwise_cfg=dict(
+        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='LinearLR',
+        start_factor=1.0e-5,
+        by_epoch=False,
+        begin=0,
+        end=1000),
+    dict(
+        # use cosine lr from 150 to 300 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=max_epochs // 2,
+        end=max_epochs,
+        T_max=max_epochs // 2,
+        by_epoch=True,
+        convert_to_iter_based=True),
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=2560)
+
+# codec settings
+codec = dict(
+    type='SimCCLabel',
+    input_size=input_size,
+    sigma=(6., 6.93),
+    simcc_split_ratio=2.0,
+    normalize=False,
+    use_dark=False,
+    decode_visibility=True)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='CSPNeXt',
+        arch='P5',
+        expand_ratio=0.5,
+        deepen_factor=1.33,
+        widen_factor=1.25,
+        channel_attention=True,
+        norm_cfg=dict(type='BN'),
+        act_cfg=dict(type='SiLU'),
+        init_cfg=dict(
+            type='Pretrained',
+            prefix='backbone.',
+            checkpoint='https://download.openmmlab.com/mmpose/v1/'
+            'wholebody_2d_keypoint/rtmpose/ubody/rtmpose-x_simcc-ucoco_pt-aic-coco_270e-384x288-f5b50679_20230822.pth'  # noqa
+        )),
+    neck=dict(
+        type='CSPNeXtPAFPN',
+        in_channels=[320, 640, 1280],
+        out_channels=None,
+        out_indices=(
+            1,
+            2,
+        ),
+        num_csp_blocks=2,
+        expand_ratio=0.5,
+        norm_cfg=dict(type='SyncBN'),
+        act_cfg=dict(type='SiLU', inplace=True)),
+    head=dict(
+        type='RTMWHead',
+        in_channels=1280,
+        out_channels=num_keypoints,
+        input_size=input_size,
+        in_featuremap_size=tuple([s // 32 for s in input_size]),
+        simcc_split_ratio=codec['simcc_split_ratio'],
+        final_layer_kernel_size=7,
+        gau_cfg=dict(
+            hidden_dims=256,
+            s=128,
+            expansion_factor=2,
+            dropout_rate=0.,
+            drop_path=0.,
+            act_fn='SiLU',
+            use_rel_bias=False,
+            pos_enc=False),
+        loss=dict(
+            type='KLDiscretLoss',
+            use_target_weight=True,
+            beta=1.,
+            label_softmax=True,
+            label_beta=10.,
+            mask=list(range(23, 91)),
+            mask_weight=0.5,
+        ),
+        decoder=codec),
+    test_cfg=dict(flip_test=True))
+
+# base dataset settings
+dataset_type = 'CocoWholeBodyDataset'
+data_mode = 'topdown'
+data_root = 'data/'
+
+backend_args = dict(backend='local')
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PhotometricDistortion'),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+            dict(
+                type='CoarseDropout',
+                max_holes=1,
+                max_height=0.4,
+                max_width=0.4,
+                min_holes=1,
+                min_height=0.2,
+                min_width=0.2,
+                p=0.5),
+        ]),
+    dict(
+        type='GenerateTarget',
+        encoder=codec,
+        use_dataset_keypoint_weights=True),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+train_pipeline_stage2 = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[0.5, 1.5],
+        rotate_factor=90),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(
+        type='Albumentation',
+        transforms=[
+            dict(type='Blur', p=0.1),
+            dict(type='MedianBlur', p=0.1),
+        ]),
+    dict(
+        type='GenerateTarget',
+        encoder=codec,
+        use_dataset_keypoint_weights=True),
+    dict(type='PackPoseInputs')
+]
+
+# mapping
+
+aic_coco133 = [(0, 6), (1, 8), (2, 10), (3, 5), (4, 7), (5, 9), (6, 12),
+               (7, 14), (8, 16), (9, 11), (10, 13), (11, 15)]
+
+crowdpose_coco133 = [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (6, 11),
+                     (7, 12), (8, 13), (9, 14), (10, 15), (11, 16)]
+
+mpii_coco133 = [
+    (0, 16),
+    (1, 14),
+    (2, 12),
+    (3, 11),
+    (4, 13),
+    (5, 15),
+    (10, 10),
+    (11, 8),
+    (12, 6),
+    (13, 5),
+    (14, 7),
+    (15, 9),
+]
+
+jhmdb_coco133 = [
+    (3, 6),
+    (4, 5),
+    (5, 12),
+    (6, 11),
+    (7, 8),
+    (8, 7),
+    (9, 14),
+    (10, 13),
+    (11, 10),
+    (12, 9),
+    (13, 16),
+    (14, 15),
+]
+
+halpe_coco133 = [(i, i)
+                 for i in range(17)] + [(20, 17), (21, 20), (22, 18), (23, 21),
+                                        (24, 19),
+                                        (25, 22)] + [(i, i - 3)
+                                                     for i in range(26, 136)]
+
+posetrack_coco133 = [
+    (0, 0),
+    (3, 3),
+    (4, 4),
+    (5, 5),
+    (6, 6),
+    (7, 7),
+    (8, 8),
+    (9, 9),
+    (10, 10),
+    (11, 11),
+    (12, 12),
+    (13, 13),
+    (14, 14),
+    (15, 15),
+    (16, 16),
+]
+
+humanart_coco133 = [(i, i) for i in range(17)] + [(17, 99), (18, 120),
+                                                  (19, 17), (20, 20)]
+
+# train datasets
+dataset_coco = dict(
+    type=dataset_type,
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
+    data_prefix=dict(img='detection/coco/train2017/'),
+    pipeline=[],
+)
+
+dataset_aic = dict(
+    type='AicDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='aic/annotations/aic_train.json',
+    data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint'
+                     '_train_20170902/keypoint_train_images_20170902/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=aic_coco133)
+    ],
+)
+
+dataset_crowdpose = dict(
+    type='CrowdPoseDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='crowdpose/annotations/mmpose_crowdpose_trainval.json',
+    data_prefix=dict(img='pose/CrowdPose/images/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=crowdpose_coco133)
+    ],
+)
+
+dataset_mpii = dict(
+    type='MpiiDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='mpii/annotations/mpii_train.json',
+    data_prefix=dict(img='pose/MPI/images/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=mpii_coco133)
+    ],
+)
+
+dataset_jhmdb = dict(
+    type='JhmdbDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='jhmdb/annotations/Sub1_train.json',
+    data_prefix=dict(img='pose/JHMDB/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=jhmdb_coco133)
+    ],
+)
+
+dataset_halpe = dict(
+    type='HalpeDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='halpe/annotations/halpe_train_v1.json',
+    data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=halpe_coco133)
+    ],
+)
+
+dataset_posetrack = dict(
+    type='PoseTrack18Dataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='posetrack18/annotations/posetrack18_train.json',
+    data_prefix=dict(img='pose/PoseChallenge2018/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=posetrack_coco133)
+    ],
+)
+
+dataset_humanart = dict(
+    type='HumanArt21Dataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='HumanArt/annotations/training_humanart.json',
+    filter_cfg=dict(scenes=['real_human']),
+    data_prefix=dict(img='pose/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=humanart_coco133)
+    ])
+
+ubody_scenes = [
+    'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
+    'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
+    'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
+]
+
+ubody_datasets = []
+for scene in ubody_scenes:
+    each = dict(
+        type='UBody2dDataset',
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file=f'Ubody/annotations/{scene}/train_annotations.json',
+        data_prefix=dict(img='pose/UBody/images/'),
+        pipeline=[],
+        sample_interval=10)
+    ubody_datasets.append(each)
+
+dataset_ubody = dict(
+    type='CombinedDataset',
+    metainfo=dict(from_file='configs/_base_/datasets/ubody2d.py'),
+    datasets=ubody_datasets,
+    pipeline=[],
+    test_mode=False,
+)
+
+face_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale', padding=1.25),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[1.5, 2.0],
+        rotate_factor=0),
+]
+
+wflw_coco133 = [(i * 2, 23 + i)
+                for i in range(17)] + [(33 + i, 40 + i) for i in range(5)] + [
+                    (42 + i, 45 + i) for i in range(5)
+                ] + [(51 + i, 50 + i)
+                     for i in range(9)] + [(60, 59), (61, 60), (63, 61),
+                                           (64, 62), (65, 63), (67, 64),
+                                           (68, 65), (69, 66), (71, 67),
+                                           (72, 68), (73, 69),
+                                           (75, 70)] + [(76 + i, 71 + i)
+                                                        for i in range(20)]
+dataset_wflw = dict(
+    type='WFLWDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='wflw/annotations/face_landmarks_wflw_train.json',
+    data_prefix=dict(img='pose/WFLW/images/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=wflw_coco133), *face_pipeline
+    ],
+)
+
+mapping_300w_coco133 = [(i, 23 + i) for i in range(68)]
+dataset_300w = dict(
+    type='Face300WDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='300w/annotations/face_landmarks_300w_train.json',
+    data_prefix=dict(img='pose/300w/images/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=mapping_300w_coco133), *face_pipeline
+    ],
+)
+
+cofw_coco133 = [(0, 40), (2, 44), (4, 42), (1, 49), (3, 45), (6, 47), (8, 59),
+                (10, 62), (9, 68), (11, 65), (18, 54), (19, 58), (20, 53),
+                (21, 56), (22, 71), (23, 77), (24, 74), (25, 85), (26, 89),
+                (27, 80), (28, 31)]
+dataset_cofw = dict(
+    type='COFWDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='cofw/annotations/cofw_train.json',
+    data_prefix=dict(img='pose/COFW/images/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=cofw_coco133), *face_pipeline
+    ],
+)
+
+lapa_coco133 = [(i * 2, 23 + i) for i in range(17)] + [
+    (33 + i, 40 + i) for i in range(5)
+] + [(42 + i, 45 + i) for i in range(5)] + [
+    (51 + i, 50 + i) for i in range(4)
+] + [(58 + i, 54 + i) for i in range(5)] + [(66, 59), (67, 60), (69, 61),
+                                            (70, 62), (71, 63), (73, 64),
+                                            (75, 65), (76, 66), (78, 67),
+                                            (79, 68), (80, 69),
+                                            (82, 70)] + [(84 + i, 71 + i)
+                                                         for i in range(20)]
+dataset_lapa = dict(
+    type='LapaDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='LaPa/annotations/lapa_trainval.json',
+    data_prefix=dict(img='pose/LaPa/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=lapa_coco133), *face_pipeline
+    ],
+)
+
+dataset_wb = dict(
+    type='CombinedDataset',
+    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+    datasets=[dataset_coco, dataset_halpe, dataset_ubody],
+    pipeline=[],
+    test_mode=False,
+)
+
+dataset_body = dict(
+    type='CombinedDataset',
+    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+    datasets=[
+        dataset_aic,
+        dataset_crowdpose,
+        dataset_mpii,
+        dataset_jhmdb,
+        dataset_posetrack,
+        dataset_humanart,
+    ],
+    pipeline=[],
+    test_mode=False,
+)
+
+dataset_face = dict(
+    type='CombinedDataset',
+    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+    datasets=[
+        dataset_wflw,
+        dataset_300w,
+        dataset_cofw,
+        dataset_lapa,
+    ],
+    pipeline=[],
+    test_mode=False,
+)
+
+hand_pipeline = [
+    dict(type='LoadImage', backend_args=backend_args),
+    dict(type='GetBBoxCenterScale'),
+    dict(
+        type='RandomBBoxTransform',
+        shift_factor=0.,
+        scale_factor=[1.5, 2.0],
+        rotate_factor=0),
+]
+
+interhand_left = [(21, 95), (22, 94), (23, 93), (24, 92), (25, 99), (26, 98),
+                  (27, 97), (28, 96), (29, 103), (30, 102), (31, 101),
+                  (32, 100), (33, 107), (34, 106), (35, 105), (36, 104),
+                  (37, 111), (38, 110), (39, 109), (40, 108), (41, 91)]
+interhand_right = [(i - 21, j + 21) for i, j in interhand_left]
+interhand_coco133 = interhand_right + interhand_left
+
+dataset_interhand2d = dict(
+    type='InterHand2DDoubleDataset',
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='interhand26m/annotations/all/InterHand2.6M_train_data.json',
+    camera_param_file='interhand26m/annotations/all/'
+    'InterHand2.6M_train_camera.json',
+    joint_file='interhand26m/annotations/all/'
+    'InterHand2.6M_train_joint_3d.json',
+    data_prefix=dict(img='interhand2.6m/images/train/'),
+    sample_interval=10,
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=num_keypoints,
+            mapping=interhand_coco133,
+        ), *hand_pipeline
+    ],
+)
+
+dataset_hand = dict(
+    type='CombinedDataset',
+    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+    datasets=[dataset_interhand2d],
+    pipeline=[],
+    test_mode=False,
+)
+
+train_datasets = [dataset_wb, dataset_body, dataset_face, dataset_hand]
+
+# data loaders
+train_dataloader = dict(
+    batch_size=train_batch_size,
+    num_workers=4,
+    pin_memory=False,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
+        datasets=train_datasets,
+        pipeline=train_pipeline,
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    batch_size=val_batch_size,
+    num_workers=4,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type='CocoWholeBodyDataset',
+        ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
+        data_prefix=dict(img='data/detection/coco/val2017/'),
+        pipeline=val_pipeline,
+        bbox_file='data/coco/person_detection_results/'
+        'COCO_val2017_detections_AP_H_56_person.json',
+        test_mode=True))
+
+test_dataloader = val_dataloader
+
+# hooks
+default_hooks = dict(
+    checkpoint=dict(
+        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))
+
+custom_hooks = [
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0002,
+        update_buffers=True,
+        priority=49),
+    dict(
+        type='mmdet.PipelineSwitchHook',
+        switch_epoch=max_epochs - stage2_num_epochs,
+        switch_pipeline=train_pipeline_stage2)
+]
+
+# evaluators
+val_evaluator = dict(
+    type='CocoWholeBodyMetric',
+    ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
+test_evaluator = val_evaluator

dwpose_tools/models/rtmw-x_simcc-cocktail14_pt-ucoco_270e-384x288-f840f204_20231122.pth

@@ -0,0 +1,3 @@
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+oid sha256:f840f2044fe46cb3821b7cea86be83e1f6cba406ccd28f5475ac010412dcda95
+size 369720404

dwpose_tools/models/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3bd2b23e4cd178bfcc756df67e0d0949f3d77e0a73482f6da694c580ed54da1
+size 217289556

dwpose_tools/models/yolox_l_8xb8-300e_coco.py

@@ -0,0 +1,245 @@
+img_scale = (640, 640)  # width, height
+
+# model settings
+model = dict(
+    type='YOLOX',
+    data_preprocessor=dict(
+        type='DetDataPreprocessor',
+        pad_size_divisor=32,
+        batch_augments=[
+            dict(
+                type='BatchSyncRandomResize',
+                random_size_range=(480, 800),
+                size_divisor=32,
+                interval=10)
+        ]),
+    backbone=dict(
+        type='CSPDarknet',
+        deepen_factor=1.0,
+        widen_factor=1.0,
+        out_indices=(2, 3, 4),
+        use_depthwise=False,
+        spp_kernal_sizes=(5, 9, 13),
+        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        act_cfg=dict(type='Swish'),
+    ),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[256, 512, 1024],
+        out_channels=256,
+        num_csp_blocks=3,
+        use_depthwise=False,
+        upsample_cfg=dict(scale_factor=2, mode='nearest'),
+        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        act_cfg=dict(type='Swish')),
+    bbox_head=dict(
+        type='YOLOXHead',
+        num_classes=80,
+        in_channels=256,
+        feat_channels=256,
+        stacked_convs=2,
+        strides=(8, 16, 32),
+        use_depthwise=False,
+        norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+        act_cfg=dict(type='Swish'),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='sum',
+            loss_weight=1.0),
+        loss_bbox=dict(
+            type='IoULoss',
+            mode='square',
+            eps=1e-16,
+            reduction='sum',
+            loss_weight=5.0),
+        loss_obj=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='sum',
+            loss_weight=1.0),
+        loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0)),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+    # In order to align the source code, the threshold of the val phase is
+    # 0.01, and the threshold of the test phase is 0.001.
+    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+# dataset settings
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        # img_scale is (width, height)
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', prob=0.5),
+    # According to the official implementation, multi-scale
+    # training is not considered here but in the
+    # 'mmdet/models/detectors/yolox.py'.
+    # Resize and Pad are for the last 15 epochs when Mosaic,
+    # RandomAffine, and MixUp are closed by YOLOXModeSwitchHook.
+    dict(type='Resize', scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        # If the image is three-channel, the pad value needs
+        # to be set separately for each channel.
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='PackDetInputs')
+]
+
+train_dataset = dict(
+    # use MultiImageMixDataset wrapper to support mosaic and mixup
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_train2017.json',
+        data_prefix=dict(img='train2017/'),
+        pipeline=[
+            dict(type='LoadImageFromFile', backend_args=backend_args),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+        backend_args=backend_args),
+    pipeline=train_pipeline)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    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=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=train_dataset)
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+# training settings
+max_epochs = 300
+num_last_epochs = 15
+interval = 10
+
+train_cfg = dict(max_epochs=max_epochs, val_interval=interval)
+
+# optimizer
+# default 8 gpu
+base_lr = 0.01
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(
+        type='SGD', lr=base_lr, momentum=0.9, weight_decay=5e-4,
+        nesterov=True),
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
+
+# learning rate
+param_scheduler = [
+    dict(
+        # use quadratic formula to warm up 5 epochs
+        # and lr is updated by iteration
+        # TODO: fix default scope in get function
+        type='mmdet.QuadraticWarmupLR',
+        by_epoch=True,
+        begin=0,
+        end=5,
+        convert_to_iter_based=True),
+    dict(
+        # use cosine lr from 5 to 285 epoch
+        type='CosineAnnealingLR',
+        eta_min=base_lr * 0.05,
+        begin=5,
+        T_max=max_epochs - num_last_epochs,
+        end=max_epochs - num_last_epochs,
+        by_epoch=True,
+        convert_to_iter_based=True),
+    dict(
+        # use fixed lr during last 15 epochs
+        type='ConstantLR',
+        by_epoch=True,
+        factor=1,
+        begin=max_epochs - num_last_epochs,
+        end=max_epochs,
+    )
+]
+
+default_hooks = dict(
+    checkpoint=dict(
+        interval=interval,
+        max_keep_ckpts=3  # only keep latest 3 checkpoints
+    ))
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(type='SyncNormHook', priority=48),
+    dict(
+        type='EMAHook',
+        ema_type='ExpMomentumEMA',
+        momentum=0.0001,
+        update_buffers=True,
+        priority=49)
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)