mirror of https://github.com/open-mmlab/mmpose
56 lines
4.3 KiB
Markdown
56 lines
4.3 KiB
Markdown
# Top-down heatmap-based pose estimation
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Top-down methods divide the task into two stages: object detection, followed by single-object pose estimation given object bounding boxes. Instead of estimating keypoint coordinates directly, the pose estimator will produce heatmaps which represent the likelihood of being a keypoint, following the paradigm introduced in [Simple Baselines for Human Pose Estimation and Tracking](http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html).
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<div align=center>
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<img src="https://user-images.githubusercontent.com/15977946/146522977-5f355832-e9c1-442f-a34f-9d24fb0aefa8.png" height=400>
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</div>
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## Results and Models
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### COCO-WholeBody-Hand Dataset
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Results on COCO-WholeBody-Hand val set
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| Model | Input Size | PCK@0.2 | AUC | EPE | Details and Download |
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| :--------------: | :--------: | :-----: | :---: | :--: | :----------------------------------------------------------------------------------------------: |
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| HRNetv2-w18+Dark | 256x256 | 0.814 | 0.840 | 4.37 | [hrnetv2_dark_coco_wholebody_hand.md](./coco_wholebody_hand/hrnetv2_dark_coco_wholebody_hand.md) |
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| HRNetv2-w18 | 256x256 | 0.813 | 0.840 | 4.39 | [hrnetv2_coco_wholebody_hand.md](./coco_wholebody_hand/hrnetv2_coco_wholebody_hand.md) |
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| HourglassNet | 256x256 | 0.804 | 0.835 | 4.54 | [hourglass_coco_wholebody_hand.md](./coco_wholebody_hand/hourglass_coco_wholebody_hand.md) |
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| SCNet-50 | 256x256 | 0.803 | 0.834 | 4.55 | [scnet_coco_wholebody_hand.md](./coco_wholebody_hand/scnet_coco_wholebody_hand.md) |
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| ResNet-50 | 256x256 | 0.800 | 0.833 | 4.64 | [resnet_coco_wholebody_hand.md](./coco_wholebody_hand/resnet_coco_wholebody_hand.md) |
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| LiteHRNet-18 | 256x256 | 0.795 | 0.830 | 4.77 | [litehrnet_coco_wholebody_hand.md](./coco_wholebody_hand/litehrnet_coco_wholebody_hand.md) |
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| MobileNet-v2 | 256x256 | 0.795 | 0.829 | 4.77 | [mobilenetv2_coco_wholebody_hand.md](./coco_wholebody_hand/mobilenetv2_coco_wholebody_hand.md) |
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### FreiHand Dataset
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Results on FreiHand val & test set
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| Model | Input Size | PCK@0.2 | AUC | EPE | Details and Download |
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| :-------: | :--------: | :-----: | :---: | :--: | :-------------------------------------------------------: |
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| ResNet-50 | 224x224 | 0.999 | 0.868 | 3.27 | [resnet_freihand2d.md](./freihand2d/resnet_freihand2d.md) |
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### OneHand10K Dataset
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Results on OneHand10K val set
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| Model | Input Size | PCK@0.2 | AUC | EPE | Details and Download |
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| :--------------: | :--------: | :-----: | :---: | :---: | :-------------------------------------------------------------------: |
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| HRNetv2-w18+Dark | 256x256 | 0.990 | 0.572 | 23.96 | [hrnetv2_dark_onehand10k.md](./onehand10k/hrnetv2_dark_onehand10k.md) |
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| HRNetv2-w18+UDP | 256x256 | 0.990 | 0.571 | 23.88 | [hrnetv2_udp_onehand10k.md](./onehand10k/hrnetv2_udp_onehand10k.md) |
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| HRNetv2-w18 | 256x256 | 0.990 | 0.567 | 24.26 | [hrnetv2_onehand10k.md](./onehand10k/hrnetv2_onehand10k.md) |
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| ResNet-50 | 256x256 | 0.989 | 0.555 | 25.16 | [resnet_onehand10k.md](./onehand10k/resnet_onehand10k.md) |
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| MobileNet-v2 | 256x256 | 0.986 | 0.537 | 28.56 | [mobilenetv2_onehand10k.md](./onehand10k/mobilenetv2_onehand10k.md) |
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### RHD Dataset
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Results on RHD test set
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| Model | Input Size | PCK@0.2 | AUC | EPE | Details and Download |
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| :--------------: | :--------: | :-----: | :---: | :--: | :----------------------------------------------------: |
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| HRNetv2-w18+Dark | 256x256 | 0.992 | 0.903 | 2.18 | [hrnetv2_dark_rhd2d.md](./rhd2d/hrnetv2_dark_rhd2d.md) |
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| HRNetv2-w18+UDP | 256x256 | 0.992 | 0.902 | 2.19 | [hrnetv2_udp_rhd2d.md](./rhd2d/hrnetv2_udp_rhd2d.md) |
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| HRNetv2-w18 | 256x256 | 0.992 | 0.902 | 2.21 | [hrnetv2_rhd2d.md](./rhd2d/hrnetv2_rhd2d.md) |
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| ResNet-50 | 256x256 | 0.991 | 0.898 | 2.32 | [resnet_rhd2d.md](./rhd2d/resnet_rhd2d.md) |
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| MobileNet-v2 | 256x256 | 0.985 | 0.883 | 2.79 | [mobilenetv2_rhd2d.md](./rhd2d/mobilenetv2_rhd2d.md) |
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