mirror of https://github.com/open-mmlab/mmpose
58 lines
4.1 KiB
Markdown
58 lines
4.1 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 Dataset
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Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset
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| Model | Input Size | AP | AR | Details and Download |
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| :----------------: | :--------: | :---: | :---: | :---------------------------------------: |
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| RTMPose-t | 256x192 | 0.682 | 0.736 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-s | 256x192 | 0.716 | 0.768 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-m | 256x192 | 0.746 | 0.795 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-l | 256x192 | 0.758 | 0.806 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-t-aic-coco | 256x192 | 0.685 | 0.738 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-s-aic-coco | 256x192 | 0.722 | 0.772 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-m-aic-coco | 256x192 | 0.758 | 0.806 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-l-aic-coco | 256x192 | 0.765 | 0.813 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-m-aic-coco | 384x288 | 0.770 | 0.816 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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| RTMPose-l-aic-coco | 384x288 | 0.773 | 0.819 | [rtmpose_coco.md](./coco/rtmpose_coco.md) |
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### MPII Dataset
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| Model | Input Size | PCKh@0.5 | PCKh@0.1 | Details and Download |
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| :-------: | :--------: | :------: | :------: | :---------------------------------------: |
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| RTMPose-m | 256x256 | 0.907 | 0.348 | [rtmpose_mpii.md](./mpii/rtmpose_mpii.md) |
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### CrowdPose Dataset
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Results on CrowdPose test with [YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) human detector
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| Model | Input Size | AP | AR | Details and Download |
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| :-------: | :--------: | :---: | :---: | :------------------------------------------------------: |
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| RTMPose-m | 256x192 | 0.706 | 0.788 | [rtmpose_crowdpose.md](./crowdpose/rtmpose_crowdpose.md) |
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### Human-Art Dataset
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Results on Human-Art validation dataset with detector having human AP of 56.2 on Human-Art validation dataset
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| Model | Input Size | AP | AR | Details and Download |
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| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: |
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| RTMPose-s | 256x192 | 0.311 | 0.381 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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| RTMPose-m | 256x192 | 0.355 | 0.417 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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| RTMPose-l | 256x192 | 0.378 | 0.442 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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Results on Human-Art validation dataset with ground-truth bounding-box
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| Model | Input Size | AP | AR | Details and Download |
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| :-------: | :--------: | :---: | :---: | :---------------------------------------------------: |
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| RTMPose-s | 256x192 | 0.698 | 0.732 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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| RTMPose-m | 256x192 | 0.728 | 0.759 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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| RTMPose-l | 256x192 | 0.753 | 0.783 | [rtmpose_humanart.md](./humanart/rtmpose_humanart.md) |
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