mirror of https://github.com/open-mmlab/mmpose
33 lines
1.9 KiB
Markdown
33 lines
1.9 KiB
Markdown
# RTMPose
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Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
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In order to bridge this gap, we empirically study five aspects that affect the performance of multi-person pose estimation algorithms: paradigm, backbone network, localization algorithm, training strategy, and deployment inference, and present a high-performance real-time multi-person pose estimation framework, **RTMPose**, based on MMPose.
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Our RTMPose-m achieves **75.8% AP** on COCO with **90+ FPS** on an Intel i7-11700 CPU and **430+ FPS** on an NVIDIA GTX 1660 Ti GPU, and RTMPose-l achieves **67.0% AP** on COCO-WholeBody with **130+ FPS**, outperforming existing open-source libraries.
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To further evaluate RTMPose's capability in critical real-time applications, we also report the performance after deploying on the mobile device.
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## Results and Models
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### COCO-WholeBody-Face Dataset
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Results on COCO-WholeBody-Face val set
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| Model | Input Size | NME | Details and Download |
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| :-------: | :--------: | :----: | :------------------------------------------------------------------------------------: |
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| RTMPose-m | 256x256 | 0.0466 | [rtmpose_coco_wholebody_face.md](./coco_wholebody_face/rtmpose_coco_wholebody_face.md) |
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### WFLW Dataset
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Results on WFLW dataset
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| Model | Input Size | NME | Details and Download |
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| :-------: | :--------: | :--: | :---------------------------------------: |
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| RTMPose-m | 256x256 | 4.01 | [rtmpose_wflw.md](./wflw/rtmpose_wflw.md) |
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### LaPa Dataset
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Results on LaPa dataset
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| Model | Input Size | NME | Details and Download |
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| :-------: | :--------: | :--: | :---------------------------------------: |
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| RTMPose-m | 256x256 | 1.29 | [rtmpose_lapa.md](./lapa/rtmpose_lapa.md) |
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