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[Feature] Add models trained on Body8 (open-mmlab#2331)
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@Tau-J
Tau-J authored May 4, 2023
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7 changes: 6 additions & 1 deletion docs/en/faq.md
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Expand Up @@ -8,7 +8,12 @@ If the contents here do not cover your issue, please create an issue using the [

Compatibility issue between MMCV and MMPose; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."

Compatible MMPose and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.
Here are the version correspondences between `mmdet`, `mmcv` and `mmpose`:

- mmdet 2.x \<=> mmpose 0.x \<=> mmcv 1.x
- mmdet 3.x \<=> mmpose 1.x \<=> mmcv 2.x

Detailed compatible MMPose and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.

### MMPose 1.x

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7 changes: 6 additions & 1 deletion docs/zh_cn/faq.md
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Expand Up @@ -8,7 +8,12 @@ If the contents here do not cover your issue, please create an issue using the [

Compatibility issue between MMCV and MMPose; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."

Compatible MMPose and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.
Here are the version correspondences between `mmdet`, `mmcv` and `mmpose`:

- mmdet 2.x \<=> mmpose 0.x \<=> mmcv 1.x
- mmdet 3.x \<=> mmpose 1.x \<=> mmcv 2.x

Detailed compatible MMPose and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.

### MMPose 1.x

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77 changes: 63 additions & 14 deletions projects/rtmpose/README.md
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Expand Up @@ -154,17 +154,41 @@ Feel free to join our community group for more help:
- Flip test is used.
- Inference speed measured on more hardware platforms can refer to [Benchmark](./benchmark/README.md)
- If you have datasets you would like us to support, feel free to [contact us](https://docs.google.com/forms/d/e/1FAIpQLSfzwWr3eNlDzhU98qzk2Eph44Zio6hi5r0iSwfO9wSARkHdWg/viewform?usp=sf_link)/[联系我们](https://uua478.fanqier.cn/f/xxmynrki).
-

### Body 2d (17 Keypoints)

| Config | Input Size | AP<sup><br>(COCO) | Params(M) | FLOPS(G) | ORT-Latency(ms)<sup><br>(i7-11700) | TRT-FP16-Latency(ms)<sup><br>(GTX 1660Ti) | ncnn-FP16-Latency(ms)<sup><br>(Snapdragon 865) | Download |
| :--------------: | :--------: | :---------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :-----------------: |
| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) |
| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) |
| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) |
| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) |
| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) |
| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 27.79 | 9.35 | - | 6.05 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) |
#### AIC+COCO

| Config | Input Size | AP<sup><br>(COCO) | [email protected]<sup><br>(Body8) | AUC<sup><br>(Body8) | EPE<sup><br>(Body8) | Params(M) | FLOPS(G) | ORT-Latency(ms)<sup><br>(i7-11700) | TRT-FP16-Latency(ms)<sup><br>(GTX 1660Ti) | ncnn-FP16-Latency(ms)<sup><br>(Snapdragon 865) | Download |
| :---------------------------------------------------------------------------: | :--------: | :---------------: | :---------------------: | :-----------------: | :-----------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
| [RTMPose-t](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 68.5 | 91.28 | 63.38 | 19.87 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth) |
| [RTMPose-s](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 72.2 | 92.95 | 66.19 | 17.32 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-aic-coco_pt-aic-coco_420e-256x192-fcb2599b_20230126.pth) |
| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 75.8 | 94.13 | 68.53 | 15.42 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-256x192-63eb25f7_20230126.pth) |
| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.5 | 94.35 | 68.98 | 15.10 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth) |
| [RTMPose-m](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 77.0 | 94.32 | 69.85 | 14.64 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-aic-coco_pt-aic-coco_420e-384x288-a62a0b32_20230228.pth) |
| [RTMPose-l](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 77.3 | 94.54 | 70.14 | 14.30 | 27.79 | 9.35 | - | 6.05 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-384x288-97d6cb0f_20230228.pth) |

#### Body8

- `*` denotes model trained on 7 public datasets:
- [AI Challenger](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#aic)
- [MS COCO](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#coco)
- [CrowdPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#crowdpose)
- [MPII](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#mpii)
- [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#sub-jhmdb-dataset)
- [Halpe](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_wholebody_keypoint.html#halpe)
- [PoseTrack18](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#posetrack18)
- `Body8` denotes the addition of the [OCHuman](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#ochuman) dataset, in addition to the 7 datasets mentioned above, for evaluation.

| Config | Input Size | AP<sup><br>(COCO) | [email protected]<sup><br>(Body8) | AUC<sup><br>(Body8) | EPE<sup><br>(Body8) | Params(M) | FLOPS(G) | ORT-Latency(ms)<sup><br>(i7-11700) | TRT-FP16-Latency(ms)<sup><br>(GTX 1660Ti) | ncnn-FP16-Latency(ms)<sup><br>(Snapdragon 865) | Download |
| :-----------------------------------------------------------------------------: | :--------: | :---------------: | :---------------------: | :-----------------: | :-----------------: | :-------: | :------: | :--------------------------------: | :---------------------------------------: | :--------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------: |
| [RTMPose-t\*](./rtmpose/body_2d_keypoint/rtmpose-t_8xb256-420e_coco-256x192.py) | 256x192 | 65.9 | 91.44 | 63.18 | 19.45 | 3.34 | 0.36 | 3.20 | 1.06 | 9.02 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-t_simcc-body7_pt-body7_420e-256x192-026a1439_20230504.pth) |
| [RTMPose-s\*](./rtmpose/body_2d_keypoint/rtmpose-s_8xb256-420e_coco-256x192.py) | 256x192 | 69.7 | 92.45 | 65.15 | 17.85 | 5.47 | 0.68 | 4.48 | 1.39 | 13.89 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-s_simcc-body7_pt-body7_420e-256x192-acd4a1ef_20230504.pth) |
| [RTMPose-m\*](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-256x192.py) | 256x192 | 74.9 | 94.25 | 68.59 | 15.12 | 13.59 | 1.93 | 11.06 | 2.29 | 26.44 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-256x192-e48f03d0_20230504.pth) |
| [RTMPose-l\*](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-256x192.py) | 256x192 | 76.7 | 95.08 | 70.14 | 13.79 | 27.66 | 4.16 | 18.85 | 3.46 | 45.37 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-256x192-4dba18fc_20230504.pth) |
| [RTMPose-m\*](./rtmpose/body_2d_keypoint/rtmpose-m_8xb256-420e_coco-384x288.py) | 384x288 | 76.6 | 94.64 | 70.38 | 13.98 | 13.72 | 4.33 | 24.78 | 3.66 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-m_simcc-body7_pt-body7_420e-384x288-65e718c4_20230504.pth) |
| [RTMPose-l\*](./rtmpose/body_2d_keypoint/rtmpose-l_8xb256-420e_coco-384x288.py) | 384x288 | 78.3 | 95.36 | 71.58 | 13.08 | 27.79 | 9.35 | - | 6.05 | - | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-l_simcc-body7_pt-body7_420e-384x288-3f5a1437_20230504.pth) |

#### Model Pruning

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We provide the UDP pretraining configs of the CSPNeXt backbone. Find more details in the [pretrain_cspnext_udp folder](./rtmpose/pretrain_cspnext_udp/).

| Model | Input Size | Params(M) | Flops(G) | AP<sup><br>(GT) | AR<sup><br>(GT) | Download |
| :----------: | :--------: | :-------: | :------: | :-------------: | :-------------: | :-------------------------------------------------------------------------------------------------------------------------------: |
| CSPNeXt-tiny | 256x192 | 6.03 | 1.43 | 65.5 | 68.9 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth) |
| CSPNeXt-s | 256x192 | 8.58 | 1.78 | 70.0 | 73.3 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-s_udp-aic-coco_210e-256x192-92f5a029_20230130.pth) |
| CSPNeXt-m | 256x192 | 13.05 | 3.06 | 74.8 | 77.7 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth) |
| CSPNeXt-l | 256x192 | 32.44 | 5.33 | 77.2 | 79.9 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth) |
#### AIC+COCO

| Model | Input Size | Params(M) | Flops(G) | AP<sup><br>(GT) | AR<sup><br>(GT) | Download |
| :----------: | :--------: | :-------: | :------: | :-------------: | :-------------: | :-----------------------------------------------------------------------------------------------------------------------------: |
| CSPNeXt-tiny | 256x192 | 6.03 | 1.43 | 65.5 | 68.9 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth) |
| CSPNeXt-s | 256x192 | 8.58 | 1.78 | 70.0 | 73.3 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-s_udp-aic-coco_210e-256x192-92f5a029_20230130.pth) |
| CSPNeXt-m | 256x192 | 17.53 | 3.05 | 74.8 | 77.7 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth) |
| CSPNeXt-l | 256x192 | 32.44 | 5.32 | 77.2 | 79.9 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth) |

#### Body8

- `*` denotes model trained on 7 public datasets:
- [AI Challenger](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#aic)
- [MS COCO](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#coco)
- [CrowdPose](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#crowdpose)
- [MPII](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#mpii)
- [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#sub-jhmdb-dataset)
- [Halpe](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_wholebody_keypoint.html#halpe)
- [PoseTrack18](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#posetrack18)
- `Body8` denotes the addition of the [OCHuman](https://mmpose.readthedocs.io/en/latest/dataset_zoo/2d_body_keypoint.html#ochuman) dataset, in addition to the 7 datasets mentioned above, for evaluation.

| Model | Input Size | Params(M) | Flops(G) | AP<sup><br>(COCO) | [email protected]<sup><br>(Body8) | AUC<sup><br>(Body8) | EPE<sup><br>(Body8) | Download |
| :------------: | :--------: | :-------: | :------: | :---------------: | :---------------------: | :-----------------: | :-----------------: | :-------------------------------------------------------------------------------: |
| CSPNeXt-tiny\* | 256x192 | 6.03 | 1.43 | 65.9 | 96.34 | 63.80 | 18.63 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-tiny_udp-body7_210e-256x192-a3775292_20230504.pth) |
| CSPNeXt-s\* | 256x192 | 8.58 | 1.78 | 68.7 | 96.59 | 64.92 | 17.84 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-s_udp-body7_210e-256x192-8c9ccbdb_20230504.pth) |
| CSPNeXt-m\* | 256x192 | 17.53 | 3.05 | 73.7 | 97.42 | 68.19 | 15.12 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-m_udp-body7_210e-256x192-e0c9327b_20230504.pth) |
| CSPNeXt-l\* | 256x192 | 32.44 | 5.32 | 75.7 | 97.76 | 69.57 | 13.96 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-l_udp-body7_210e-256x192-5e9558ef_20230504.pth) |
| CSPNeXt-m\* | 384x288 | 17.53 | 6.86 | 75.8 | 97.60 | 70.18 | 14.04 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-m_udp-body7_210e-384x288-b9bc2b57_20230504.pth) |
| CSPNeXt-l\* | 384x288 | 32.44 | 11.96 | 77.2 | 97.89 | 71.23 | 13.05 | [Model](https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/cspnext-l_udp-body7_210e-384x288-b15bc30d_20230504.pth) |

#### ImageNet

We also provide the ImageNet classification pre-trained weights of the CSPNeXt backbone. Find more details in [RTMDet](https://github.com/open-mmlab/mmdetection/blob/latest/configs/rtmdet/README.md#classification).

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