mirror of https://github.com/open-mmlab/mmpose
19 lines
1.7 KiB
Markdown
19 lines
1.7 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 Dataset
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Results on COCO-WholeBody v1.0 val with detector having human AP of 56.4 on COCO val2017 dataset
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| Model | Input Size | Whole AP | Whole AR | Details and Download |
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| :-------: | :--------: | :------: | :------: | :---------------------------------------------------------------------: |
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| RTMPose-m | 256x192 | 0.582 | 0.674 | [rtmpose_coco-wholebody.md](./coco-wholebody/rtmpose_coco-wholebody.md) |
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| RTMPose-l | 256x192 | 0.611 | 0.700 | [rtmpose_coco-wholebody.md](./coco-wholebody/rtmpose_coco-wholebody.md) |
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| RTMPose-l | 384x288 | 0.648 | 0.730 | [rtmpose_coco-wholebody.md](./coco-wholebody/rtmpose_coco-wholebody.md) |
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