diff --git a/configs/hand/mobilenet_v2/README.md b/configs/hand/mobilenet_v2/README.md
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+# Simple baselines for human pose estimation and tracking
+
+## Introduction
+```
+@inproceedings{sandler2018mobilenetv2,
+  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
+  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={4510--4520},
+  year={2018}
+}
+```
+
+## Results and models
+
+### 2d Hand Pose Estimation
+
+#### Results on OneHand10K val set.
+
+| Arch  | Input Size | PCK@0.2 |  AUC  |  EPE  | ckpt    | log     |
+| :--- | :--------: | :------: | :------: | :------: |:------: |:------: |
+| [pose_mobilenet_v2](/configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py) | 256x256 | 0.984 | 0.526 | 29.52 | [ckpt](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_onehand10k_256x256-55d34d7d_20201218.pth) | [log](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_onehand10k_256x256_20201218.log.json) |
+
+#### Results on CMU Panoptic (MPII+NZSL val set).
+
+| Arch  | Input Size | PCKh@0.7 |  AUC  |  EPE  | ckpt    | log     |
+| :--- | :--------: | :------: | :------: | :------: |:------: |:------: |
+| [pose_mobilenet_v2](/configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py) | 256x256 | 0.998 | 0.684 | 10.09 | [ckpt](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_panoptic_256x256-b9ec9b68_20201218.pth) | [log](https://download.openmmlab.com/mmpose/top_down/mobilenetv2/mobilenetv2_panoptic_256x256_20201218.log.json) |
diff --git a/configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py b/configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py
new file mode 100644
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+++ b/configs/hand/mobilenet_v2/onehand10k/mobilenetv2_onehand10k_256x256.py
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+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(
+    interval=10, metric=['PCK', 'AUC', 'EPE'], key_indicator='AUC')
+
+optimizer = dict(
+    type='Adam',
+    lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[170, 200])
+total_epochs = 210
+log_config = dict(
+    interval=10,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+
+channel_cfg = dict(
+    num_output_channels=21,
+    dataset_joints=21,
+    dataset_channel=[
+        [
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ],
+    ],
+    inference_channel=[
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+        20
+    ])
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(type='MobileNetV2', widen_factor=1., out_indices=(7, )),
+    keypoint_head=dict(
+        type='TopDownSimpleHead',
+        in_channels=1280,
+        out_channels=channel_cfg['num_output_channels'],
+    ),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        post_process='default',
+        shift_heatmap=True,
+        modulate_kernel=11),
+    loss_pose=dict(type='JointsMSELoss', use_target_weight=True))
+
+data_cfg = dict(
+    image_size=[256, 256],
+    heatmap_size=[64, 64],
+    num_output_channels=channel_cfg['num_output_channels'],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownRandomFlip', flip_prob=0.5),
+    dict(
+        type='TopDownGetRandomScaleRotation', rot_factor=20, scale_factor=0.3),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='TopDownGenerateTarget', sigma=2),
+    dict(
+        type='Collect',
+        keys=['img', 'target', 'target_weight'],
+        meta_keys=[
+            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+            'rotation', 'flip_pairs'
+        ]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='Collect',
+        keys=['img'],
+        meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/onehand10k'
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='OneHand10KDataset',
+        ann_file=f'{data_root}/annotations/onehand10k_train.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='OneHand10KDataset',
+        ann_file=f'{data_root}/annotations/onehand10k_test.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+    test=dict(
+        type='OneHand10KDataset',
+        ann_file=f'{data_root}/annotations/onehand10k_test.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+)
diff --git a/configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py b/configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py
new file mode 100644
index 0000000000..08c2ffe2bc
--- /dev/null
+++ b/configs/hand/mobilenet_v2/panoptic/mobilenetv2_panoptic_256x256.py
@@ -0,0 +1,130 @@
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(
+    interval=10, metric=['PCKh', 'AUC', 'EPE'], key_indicator='AUC')
+
+optimizer = dict(
+    type='Adam',
+    lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[170, 200])
+total_epochs = 210
+log_config = dict(
+    interval=10,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+
+channel_cfg = dict(
+    num_output_channels=21,
+    dataset_joints=21,
+    dataset_channel=[
+        [
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ],
+    ],
+    inference_channel=[
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+        20
+    ])
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(type='MobileNetV2', widen_factor=1., out_indices=(7, )),
+    keypoint_head=dict(
+        type='TopDownSimpleHead',
+        in_channels=1280,
+        out_channels=channel_cfg['num_output_channels'],
+    ),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        post_process='default',
+        shift_heatmap=True,
+        modulate_kernel=11),
+    loss_pose=dict(type='JointsMSELoss', use_target_weight=True))
+
+data_cfg = dict(
+    image_size=[256, 256],
+    heatmap_size=[64, 64],
+    num_output_channels=channel_cfg['num_output_channels'],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownRandomFlip', flip_prob=0.5),
+    dict(
+        type='TopDownGetRandomScaleRotation', rot_factor=20, scale_factor=0.3),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='TopDownGenerateTarget', sigma=2),
+    dict(
+        type='Collect',
+        keys=['img', 'target', 'target_weight'],
+        meta_keys=[
+            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+            'rotation', 'flip_pairs'
+        ]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='Collect',
+        keys=['img'],
+        meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/panoptic'
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='PanopticDataset',
+        ann_file=f'{data_root}/annotations/panoptic_train.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='PanopticDataset',
+        ann_file=f'{data_root}/annotations/panoptic_test.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+    test=dict(
+        type='PanopticDataset',
+        ann_file=f'{data_root}/annotations/panoptic_test.json',
+        img_prefix=f'{data_root}/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+)