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Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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third_party/hamer/third-party/ViTPose/MANIFEST.in
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include requirements/*.txt
include mmpose/.mim/model-index.yml
recursive-include mmpose/.mim/configs *.py *.yml
recursive-include mmpose/.mim/tools *.py *.sh
recursive-include mmpose/.mim/demo *.py
third_party/hamer/third-party/ViTPose/README.md
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<h1 align="left">ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation<a href="https://arxiv.org/abs/2204.12484"><img src="https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg" ></a> </h1>
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vitpose-simple-vision-transformer-baselines/pose-estimation-on-coco-test-dev)](https://paperswithcode.com/sota/pose-estimation-on-coco-test-dev?p=vitpose-simple-vision-transformer-baselines)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vitpose-simple-vision-transformer-baselines/pose-estimation-on-aic)](https://paperswithcode.com/sota/pose-estimation-on-aic?p=vitpose-simple-vision-transformer-baselines)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vitpose-simple-vision-transformer-baselines/pose-estimation-on-crowdpose)](https://paperswithcode.com/sota/pose-estimation-on-crowdpose?p=vitpose-simple-vision-transformer-baselines)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vitpose-simple-vision-transformer-baselines/pose-estimation-on-ochuman)](https://paperswithcode.com/sota/pose-estimation-on-ochuman?p=vitpose-simple-vision-transformer-baselines)
<p align="center">
<a href="#Results">Results</a> |
<a href="#Updates">Updates</a> |
<a href="#Usage">Usage</a> |
<a href='#Todo'>Todo</a> |
<a href="#Acknowledge">Acknowledge</a>
</p>
<p align="center">
<a href="https://giphy.com/gifs/UfPQB1qKir7Vqem6sL/fullscreen"><img src="https://media.giphy.com/media/ZewXwZuixYKS2lZmNL/giphy.gif"></a> <a href="https://giphy.com/gifs/DCvf1DrWZgbwPa8bWZ/fullscreen"><img src="https://media.giphy.com/media/2AEeuicbIjwqp2mbug/giphy.gif"></a>
</p>
<p align="center">
<a href="https://giphy.com/gifs/r3GaZz7H1H6zpuIvPI/fullscreen"><img src="https://media.giphy.com/media/13oe6zo6b2B7CdsOac/giphy.gif"></a> <a href="https://giphy.com/gifs/FjzrGJxsOzZAXaW7Vi/fullscreen"><img src="https://media.giphy.com/media/4JLERHxOEgH0tt5DZO/giphy.gif"></a>
</p>
This branch contains the pytorch implementation of <a href="https://arxiv.org/abs/2204.12484">ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation</a> and <a href="https://arxiv.org/abs/2212.04246">ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation</a>. It obtains 81.1 AP on MS COCO Keypoint test-dev set.
<img src="figures/Throughput.png" class="left" width='80%'>
## Web Demo
- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for video: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/hysts/ViTPose_video) and images [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Gradio-Blocks/ViTPose)
## MAE Pre-trained model
- The small size MAE pre-trained model can be found in [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccZeiFjh4DJ7gjYyg?e=iTMdMq).
- The base, large, and huge pre-trained models using MAE can be found in the [MAE official repo](https://github.com/facebookresearch/mae).
## Results from this repo on MS COCO val set (single-task training)
Using detection results from a detector that obtains 56 mAP on person. The configs here are for both training and test.
> With classic decoder
| Model | Pretrain | Resolution | AP | AR | config | log | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-S | MAE | 256x192 | 73.8 | 79.2 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_small_coco_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcchdNXBAh7ClS14pA?e=dKXmJ6) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccifT1XlGRatxg3vw?e=9wz7BY) |
| ViTPose-B | MAE | 256x192 | 75.8 | 81.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py) | [log](logs/vitpose-b.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSMjp1_NrV3VRSmK?e=Q1uZKs) |
| ViTPose-L | MAE | 256x192 | 78.3 | 83.5 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py) | [log](logs/vitpose-l.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSd9k_kuktPtiP4F?e=K7DGYT) |
| ViTPose-H | MAE | 256x192 | 79.1 | 84.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_huge_coco_256x192.py) | [log](logs/vitpose-h.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgShLMI-kkmvNfF_h?e=dEhGHe) |
> With simple decoder
| Model | Pretrain | Resolution | AP | AR | config | log | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-S | MAE | 256x192 | 73.5 | 78.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_small_simple_coco_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccfkqELJqE67kpRtw?e=InSjJP) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccgb_50jIgiYkHvdw?e=D7RbH2) |
| ViTPose-B | MAE | 256x192 | 75.5 | 80.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_simple_coco_256x192.py) | [log](logs/vitpose-b-simple.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSRPKrD5PmDRiv0R?e=jifvOe) |
| ViTPose-L | MAE | 256x192 | 78.2 | 83.4 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_simple_coco_256x192.py) | [log](logs/vitpose-l-simple.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSVS6DP2LmKwZ3sm?e=MmCvDT) |
| ViTPose-H | MAE | 256x192 | 78.9 | 84.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_huge_simple_coco_256x192.py) | [log](logs/vitpose-h-simple.log.json) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSbHyN2mjh2n2LyG?e=y0FgMK) |
## Results with multi-task training
**Note** \* There may exist duplicate images in the crowdpose training set and the validation images in other datasets, as discussed in [issue #24](https://github.com/ViTAE-Transformer/ViTPose/issues/24). Please be careful when using these models for evaluation. We provide the results without the crowpose dataset for reference.
### Human datasets (MS COCO, AIC, MPII, CrowdPose)
> Results on MS COCO val set
Using detection results from a detector that obtains 56 mAP on person. Note the configs here are only for evaluation.
| Model | Dataset | Resolution | AP | AR | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-B | COCO+AIC+MPII | 256x192 | 77.1 | 82.2 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcccwaTZ8xCFFM3Sjg?e=chmiK5) |
| ViTPose-L | COCO+AIC+MPII | 256x192 | 78.7 | 83.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccdOLQqSo6E87GfMw?e=TEurgW) |
| ViTPose-H | COCO+AIC+MPII | 256x192 | 79.5 | 84.5 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_huge_coco_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccmHofkmfJDQDukVw?e=gRK224) |
| ViTPose-G | COCO+AIC+MPII | 576x432 | 81.0 | 85.6 | | |
| ViTPose-B* | COCO+AIC+MPII+CrowdPose | 256x192 | 77.5 | 82.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py) |[Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSrlMB093JzJtqq-?e=Jr5S3R) |
| ViTPose-L* | COCO+AIC+MPII+CrowdPose | 256x192 | 79.1 | 84.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTBm3dCVmBUbHYT6?e=fHUrTq) |
| ViTPose-H* | COCO+AIC+MPII+CrowdPose | 256x192 | 79.8 | 84.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_huge_coco_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgS5rLeRAJiWobCdh?e=41GsDd) |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 75.8 | 82.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vitPose+_small_coco+aic+mpii+ap10k+apt36k+wholebody_256x192_udp.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 77.0 | 82.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vitPose+_base_coco+aic+mpii+ap10k+apt36k+wholebody_256x192_udp.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 78.6 | 84.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vitPose+_large_coco+aic+mpii+ap10k+apt36k+wholebody_256x192_udp.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 79.4 | 84.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vitPose+_huge_coco+aic+mpii+ap10k+apt36k+wholebody_256x192_udp.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
> Results on OCHuman test set
Using groundtruth bounding boxes. Note the configs here are only for evaluation.
| Model | Dataset | Resolution | AP | AR | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-B | COCO+AIC+MPII | 256x192 | 88.0 | 89.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_base_ochuman_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcccwaTZ8xCFFM3Sjg?e=chmiK5) |
| ViTPose-L | COCO+AIC+MPII | 256x192 | 90.9 | 92.2 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_large_ochuman_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccdOLQqSo6E87GfMw?e=TEurgW) |
| ViTPose-H | COCO+AIC+MPII | 256x192 | 90.9 | 92.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_huge_ochuman_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccmHofkmfJDQDukVw?e=gRK224) |
| ViTPose-G | COCO+AIC+MPII | 576x432 | 93.3 | 94.3 | | |
| ViTPose-B* | COCO+AIC+MPII+CrowdPose | 256x192 | 88.2 | 90.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_base_ochuman_256x192.py) |[Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSrlMB093JzJtqq-?e=Jr5S3R) |
| ViTPose-L* | COCO+AIC+MPII+CrowdPose | 256x192 | 91.5 | 92.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_large_ochuman_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTBm3dCVmBUbHYT6?e=fHUrTq) |
| ViTPose-H* | COCO+AIC+MPII+CrowdPose | 256x192 | 91.6 | 92.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_huge_ochuman_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgS5rLeRAJiWobCdh?e=41GsDd) |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 78.4 | 80.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_small_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 82.6 | 84.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_base_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 85.7 | 87.5 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_large_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 85.7 | 87.4 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_huge_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
> Results on MPII val set
Using groundtruth bounding boxes. Note the configs here are only for evaluation. The metric is PCKh.
| Model | Dataset | Resolution | Mean | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-B | COCO+AIC+MPII | 256x192 | 93.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_base_mpii_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcccwaTZ8xCFFM3Sjg?e=chmiK5) |
| ViTPose-L | COCO+AIC+MPII | 256x192 | 94.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_large_mpii_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccdOLQqSo6E87GfMw?e=TEurgW) |
| ViTPose-H | COCO+AIC+MPII | 256x192 | 94.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_huge_mpii_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccmHofkmfJDQDukVw?e=gRK224) |
| ViTPose-G | COCO+AIC+MPII | 576x432 | 94.3 | | |
| ViTPose-B* | COCO+AIC+MPII+CrowdPose | 256x192 | 93.4 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_base_mpii_256x192.py) |[Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSy_OSEm906wd2LB?e=GOSg14) |
| ViTPose-L* | COCO+AIC+MPII+CrowdPose | 256x192 | 93.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_large_mpii_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTM32I6Kpjr-esl6?e=qvh0Yl) |
| ViTPose-H* | COCO+AIC+MPII+CrowdPose | 256x192 | 94.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_huge_mpii_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTT90XEQBKy-scIH?e=D2WhTS) |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 92.7 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_small_mpii_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 92.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_base_mpii_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 94.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_large_mpii_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 94.2 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/ViTPose_huge_mpii_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
> Results on AI Challenger test set
Using groundtruth bounding boxes. Note the configs here are only for evaluation.
| Model | Dataset | Resolution | AP | AR | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-B | COCO+AIC+MPII | 256x192 | 32.0 | 36.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_base_aic_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcccwaTZ8xCFFM3Sjg?e=chmiK5) |
| ViTPose-L | COCO+AIC+MPII | 256x192 | 34.5 | 39.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_large_aic_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccdOLQqSo6E87GfMw?e=TEurgW) |
| ViTPose-H | COCO+AIC+MPII | 256x192 | 35.4 | 39.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_huge_aic_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccmHofkmfJDQDukVw?e=gRK224) |
| ViTPose-G | COCO+AIC+MPII | 576x432 | 43.2 | 47.1 | | |
| ViTPose-B* | COCO+AIC+MPII+CrowdPose | 256x192 | 31.9 | 36.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_base_aic_256x192.py) |[Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgSlvdVaXTC92SHYH?e=j7iqcp) |
| ViTPose-L* | COCO+AIC+MPII+CrowdPose | 256x192 | 34.6 | 39.0 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_large_aic_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTF06FX3FSAm0MOH?e=rYts9F) |
| ViTPose-H* | COCO+AIC+MPII+CrowdPose | 256x192 | 35.3 | 39.8 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/ViTPose_huge_aic_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgS1MRmb2mcow_K04?e=q9jPab) |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 29.7 | 34.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_small_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 31.8 | 36.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_base_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 34.3 | 38.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_large_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 34.8 | 39.1 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/ViTPose_huge_ochuman_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
> Results on CrowdPose test set
Using YOLOv3 human detector. Note the configs here are only for evaluation.
| Model | Dataset | Resolution | AP | AP(H) | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: | :----: |
| ViTPose-B* | COCO+AIC+MPII+CrowdPose | 256x192 | 74.7 | 63.3 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/crowdpose/ViTPose_base_crowdpose_256x192.py) |[Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgStrrCb91cPlaxJx?e=6Xobo6) |
| ViTPose-L* | COCO+AIC+MPII+CrowdPose | 256x192 | 76.6 | 65.9 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/crowdpose/ViTPose_large_crowdpose_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgTK3dug-r7c6GFyu?e=1ZBpEG) |
| ViTPose-H* | COCO+AIC+MPII+CrowdPose | 256x192 | 76.3 | 65.6 | [config](configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/crowdpose/ViTPose_huge_crowdpose_256x192.py) | [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgS-oAvEV4MTD--Xr?e=EeW2Fu) |
### Animal datasets (AP10K, APT36K)
> Results on AP-10K test set
| Model | Dataset | Resolution | AP | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 71.4 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_small_ap10k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 74.5 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_base_ap10k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 80.4 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_large_ap10k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 82.4 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_huge_ap10k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
> Results on APT-36K val set
| Model | Dataset | Resolution | AP | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 74.2 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_small_apt36k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 75.9 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_base_apt36k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 80.8 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_large_apt36k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 82.3 | [config](configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_huge_apt36k_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
### WholeBody dataset
| Model | Dataset | Resolution | AP | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: |
| **ViTPose+-S** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 54.4 | [config](configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_small_wholebody_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccqO1JBHtBjNaeCbQ?e=ZN5NSz) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccrwORr61gT9E4n8g?e=kz9sz5) |
| **ViTPose+-B** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 57.4 | [config](cconfigs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_base_wholebody_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccjj9lgPTlkGT1HTw?e=OlS5zv) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgcckRZk1bIAuRa_E1w?e=ylDB2G) |
| **ViTPose+-L** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 60.6 | [config](configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_large_wholebody_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgccp7HJf4QMeQQpeyA?e=JagPNt) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccs1SNFUGSTsmRJ8w?e=a9zKwZ) |
| **ViTPose+-H** | COCO+AIC+MPII+AP10K+APT36K+WholeBody | 256x192 | 61.2 | [config](configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/ViTPose_huge_wholebody_256x192.py) | [log](https://1drv.ms/u/s!AimBgYV7JjTlgcclxZOlwRJdqpIIjA?e=nFQgVC) \| [Onedrive](https://1drv.ms/u/s!AimBgYV7JjTlgccoXv8rCUgVe7oD9Q?e=ZBw6gR) |
### Transfer results on the hand dataset (InterHand2.6M)
| Model | Dataset | Resolution | AUC | config | weight |
| :----: | :----: | :----: | :----: | :----: | :----: |
| **ViTPose+-S** | COCO+AIC+MPII+WholeBody | 256x192 | 86.5 | [config](configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/interhand2d/ViTPose_small_interhand2d_all_256x192.py) | Coming Soon |
| **ViTPose+-B** | COCO+AIC+MPII+WholeBody | 256x192 | 87.0 | [config](configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/interhand2d/ViTPose_base_interhand2d_all_256x192.py) | Coming Soon |
| **ViTPose+-L** | COCO+AIC+MPII+WholeBody | 256x192 | 87.5 | [config](configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/interhand2d/ViTPose_large_interhand2d_all_256x192.py) | Coming Soon |
| **ViTPose+-H** | COCO+AIC+MPII+WholeBody | 256x192 | 87.6 | [config](configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/interhand2d/ViTPose_huge_interhand2d_all_256x192.py) | Coming Soon |
## Updates
> [2023-01-10] Update ViTPose+! It uses MoE strategies to jointly deal with human, animal, and wholebody pose estimation tasks.
> [2022-05-24] Upload the single-task training code, single-task pre-trained models, and multi-task pretrained models.
> [2022-05-06] Upload the logs for the base, large, and huge models!
> [2022-04-27] Our ViTPose with ViTAE-G obtains 81.1 AP on COCO test-dev set!
> Applications of ViTAE Transformer include: [image classification](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Image-Classification) | [object detection](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Object-Detection) | [semantic segmentation](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Semantic-Segmentation) | [animal pose segmentation](https://github.com/ViTAE-Transformer/ViTAE-Transformer/tree/main/Animal-Pose-Estimation) | [remote sensing](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing) | [matting](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Matting) | [VSA](https://github.com/ViTAE-Transformer/ViTAE-VSA) | [ViTDet](https://github.com/ViTAE-Transformer/ViTDet)
## Usage
We use PyTorch 1.9.0 or NGC docker 21.06, and mmcv 1.3.9 for the experiments.
```bash
git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout v1.3.9
MMCV_WITH_OPS=1 pip install -e .
cd ..
git clone https://github.com/ViTAE-Transformer/ViTPose.git
cd ViTPose
pip install -v -e .
```
After install the two repos, install timm and einops, i.e.,
```bash
pip install timm==0.4.9 einops
```
After downloading the pretrained models, please conduct the experiments by running
```bash
# for single machine
bash tools/dist_train.sh <Config PATH> <NUM GPUs> --cfg-options model.pretrained=<Pretrained PATH> --seed 0
# for multiple machines
python -m torch.distributed.launch --nnodes <Num Machines> --node_rank <Rank of Machine> --nproc_per_node <GPUs Per Machine> --master_addr <Master Addr> --master_port <Master Port> tools/train.py <Config PATH> --cfg-options model.pretrained=<Pretrained PATH> --launcher pytorch --seed 0
```
To test the pretrained models performance, please run
```bash
bash tools/dist_test.sh <Config PATH> <Checkpoint PATH> <NUM GPUs>
```
For ViTPose+ pre-trained models, please first re-organize the pre-trained weights using
```bash
python tools/model_split.py --source <Pretrained PATH>
```
## Todo
This repo current contains modifications including:
- [x] Upload configs and pretrained models
- [x] More models with SOTA results
- [x] Upload multi-task training config
## Acknowledge
We acknowledge the excellent implementation from [mmpose](https://github.com/open-mmlab/mmdetection) and [MAE](https://github.com/facebookresearch/mae).
## Citing ViTPose
For ViTPose
```
@inproceedings{
xu2022vitpose,
title={Vi{TP}ose: Simple Vision Transformer Baselines for Human Pose Estimation},
author={Yufei Xu and Jing Zhang and Qiming Zhang and Dacheng Tao},
booktitle={Advances in Neural Information Processing Systems},
year={2022},
}
```
For ViTPose+
```
@article{xu2022vitpose+,
title={ViTPose+: Vision Transformer Foundation Model for Generic Body Pose Estimation},
author={Xu, Yufei and Zhang, Jing and Zhang, Qiming and Tao, Dacheng},
journal={arXiv preprint arXiv:2212.04246},
year={2022}
}
```
For ViTAE and ViTAEv2, please refer to:
```
@article{xu2021vitae,
title={Vitae: Vision transformer advanced by exploring intrinsic inductive bias},
author={Xu, Yufei and Zhang, Qiming and Zhang, Jing and Tao, Dacheng},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
}
@article{zhang2022vitaev2,
title={ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond},
author={Zhang, Qiming and Xu, Yufei and Zhang, Jing and Tao, Dacheng},
journal={arXiv preprint arXiv:2202.10108},
year={2022}
}
```
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/300w.py
Vendored
+384
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@@ -0,0 +1,384 @@
dataset_info = dict(
dataset_name='300w',
paper_info=dict(
author='Sagonas, Christos and Antonakos, Epameinondas '
'and Tzimiropoulos, Georgios and Zafeiriou, Stefanos '
'and Pantic, Maja',
title='300 faces in-the-wild challenge: '
'Database and results',
container='Image and vision computing',
year='2016',
homepage='https://ibug.doc.ic.ac.uk/resources/300-W/',
),
keypoint_info={
0:
dict(
name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-16'),
1:
dict(
name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-15'),
2:
dict(
name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-14'),
3:
dict(
name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-13'),
4:
dict(
name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-12'),
5:
dict(
name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-11'),
6:
dict(
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-10'),
7:
dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-9'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap=''),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-7'),
10:
dict(
name='kpt-10', id=10, color=[255, 255, 255], type='',
swap='kpt-6'),
11:
dict(
name='kpt-11', id=11, color=[255, 255, 255], type='',
swap='kpt-5'),
12:
dict(
name='kpt-12', id=12, color=[255, 255, 255], type='',
swap='kpt-4'),
13:
dict(
name='kpt-13', id=13, color=[255, 255, 255], type='',
swap='kpt-3'),
14:
dict(
name='kpt-14', id=14, color=[255, 255, 255], type='',
swap='kpt-2'),
15:
dict(
name='kpt-15', id=15, color=[255, 255, 255], type='',
swap='kpt-1'),
16:
dict(
name='kpt-16', id=16, color=[255, 255, 255], type='',
swap='kpt-0'),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-26'),
18:
dict(
name='kpt-18',
id=18,
color=[255, 255, 255],
type='',
swap='kpt-25'),
19:
dict(
name='kpt-19',
id=19,
color=[255, 255, 255],
type='',
swap='kpt-24'),
20:
dict(
name='kpt-20',
id=20,
color=[255, 255, 255],
type='',
swap='kpt-23'),
21:
dict(
name='kpt-21',
id=21,
color=[255, 255, 255],
type='',
swap='kpt-22'),
22:
dict(
name='kpt-22',
id=22,
color=[255, 255, 255],
type='',
swap='kpt-21'),
23:
dict(
name='kpt-23',
id=23,
color=[255, 255, 255],
type='',
swap='kpt-20'),
24:
dict(
name='kpt-24',
id=24,
color=[255, 255, 255],
type='',
swap='kpt-19'),
25:
dict(
name='kpt-25',
id=25,
color=[255, 255, 255],
type='',
swap='kpt-18'),
26:
dict(
name='kpt-26',
id=26,
color=[255, 255, 255],
type='',
swap='kpt-17'),
27:
dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap=''),
29:
dict(name='kpt-29', id=29, color=[255, 255, 255], type='', swap=''),
30:
dict(name='kpt-30', id=30, color=[255, 255, 255], type='', swap=''),
31:
dict(
name='kpt-31',
id=31,
color=[255, 255, 255],
type='',
swap='kpt-35'),
32:
dict(
name='kpt-32',
id=32,
color=[255, 255, 255],
type='',
swap='kpt-34'),
33:
dict(name='kpt-33', id=33, color=[255, 255, 255], type='', swap=''),
34:
dict(
name='kpt-34',
id=34,
color=[255, 255, 255],
type='',
swap='kpt-32'),
35:
dict(
name='kpt-35',
id=35,
color=[255, 255, 255],
type='',
swap='kpt-31'),
36:
dict(
name='kpt-36',
id=36,
color=[255, 255, 255],
type='',
swap='kpt-45'),
37:
dict(
name='kpt-37',
id=37,
color=[255, 255, 255],
type='',
swap='kpt-44'),
38:
dict(
name='kpt-38',
id=38,
color=[255, 255, 255],
type='',
swap='kpt-43'),
39:
dict(
name='kpt-39',
id=39,
color=[255, 255, 255],
type='',
swap='kpt-42'),
40:
dict(
name='kpt-40',
id=40,
color=[255, 255, 255],
type='',
swap='kpt-47'),
41:
dict(
name='kpt-41',
id=41,
color=[255, 255, 255],
type='',
swap='kpt-46'),
42:
dict(
name='kpt-42',
id=42,
color=[255, 255, 255],
type='',
swap='kpt-39'),
43:
dict(
name='kpt-43',
id=43,
color=[255, 255, 255],
type='',
swap='kpt-38'),
44:
dict(
name='kpt-44',
id=44,
color=[255, 255, 255],
type='',
swap='kpt-37'),
45:
dict(
name='kpt-45',
id=45,
color=[255, 255, 255],
type='',
swap='kpt-36'),
46:
dict(
name='kpt-46',
id=46,
color=[255, 255, 255],
type='',
swap='kpt-41'),
47:
dict(
name='kpt-47',
id=47,
color=[255, 255, 255],
type='',
swap='kpt-40'),
48:
dict(
name='kpt-48',
id=48,
color=[255, 255, 255],
type='',
swap='kpt-54'),
49:
dict(
name='kpt-49',
id=49,
color=[255, 255, 255],
type='',
swap='kpt-53'),
50:
dict(
name='kpt-50',
id=50,
color=[255, 255, 255],
type='',
swap='kpt-52'),
51:
dict(name='kpt-51', id=51, color=[255, 255, 255], type='', swap=''),
52:
dict(
name='kpt-52',
id=52,
color=[255, 255, 255],
type='',
swap='kpt-50'),
53:
dict(
name='kpt-53',
id=53,
color=[255, 255, 255],
type='',
swap='kpt-49'),
54:
dict(
name='kpt-54',
id=54,
color=[255, 255, 255],
type='',
swap='kpt-48'),
55:
dict(
name='kpt-55',
id=55,
color=[255, 255, 255],
type='',
swap='kpt-59'),
56:
dict(
name='kpt-56',
id=56,
color=[255, 255, 255],
type='',
swap='kpt-58'),
57:
dict(name='kpt-57', id=57, color=[255, 255, 255], type='', swap=''),
58:
dict(
name='kpt-58',
id=58,
color=[255, 255, 255],
type='',
swap='kpt-56'),
59:
dict(
name='kpt-59',
id=59,
color=[255, 255, 255],
type='',
swap='kpt-55'),
60:
dict(
name='kpt-60',
id=60,
color=[255, 255, 255],
type='',
swap='kpt-64'),
61:
dict(
name='kpt-61',
id=61,
color=[255, 255, 255],
type='',
swap='kpt-63'),
62:
dict(name='kpt-62', id=62, color=[255, 255, 255], type='', swap=''),
63:
dict(
name='kpt-63',
id=63,
color=[255, 255, 255],
type='',
swap='kpt-61'),
64:
dict(
name='kpt-64',
id=64,
color=[255, 255, 255],
type='',
swap='kpt-60'),
65:
dict(
name='kpt-65',
id=65,
color=[255, 255, 255],
type='',
swap='kpt-67'),
66:
dict(name='kpt-66', id=66, color=[255, 255, 255], type='', swap=''),
67:
dict(
name='kpt-67',
id=67,
color=[255, 255, 255],
type='',
swap='kpt-65'),
},
skeleton_info={},
joint_weights=[1.] * 68,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/aflw.py
Vendored
+83
View File
@@ -0,0 +1,83 @@
dataset_info = dict(
dataset_name='aflw',
paper_info=dict(
author='Koestinger, Martin and Wohlhart, Paul and '
'Roth, Peter M and Bischof, Horst',
title='Annotated facial landmarks in the wild: '
'A large-scale, real-world database for facial '
'landmark localization',
container='2011 IEEE international conference on computer '
'vision workshops (ICCV workshops)',
year='2011',
homepage='https://www.tugraz.at/institute/icg/research/'
'team-bischof/lrs/downloads/aflw/',
),
keypoint_info={
0:
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-5'),
1:
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-4'),
2:
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'),
3:
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'),
4:
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-1'),
5:
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-0'),
6:
dict(
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-11'),
7:
dict(
name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-10'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'),
10:
dict(
name='kpt-10', id=10, color=[255, 255, 255], type='',
swap='kpt-7'),
11:
dict(
name='kpt-11', id=11, color=[255, 255, 255], type='',
swap='kpt-6'),
12:
dict(
name='kpt-12',
id=12,
color=[255, 255, 255],
type='',
swap='kpt-14'),
13:
dict(name='kpt-13', id=13, color=[255, 255, 255], type='', swap=''),
14:
dict(
name='kpt-14',
id=14,
color=[255, 255, 255],
type='',
swap='kpt-12'),
15:
dict(
name='kpt-15',
id=15,
color=[255, 255, 255],
type='',
swap='kpt-17'),
16:
dict(name='kpt-16', id=16, color=[255, 255, 255], type='', swap=''),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-15'),
18:
dict(name='kpt-18', id=18, color=[255, 255, 255], type='', swap='')
},
skeleton_info={},
joint_weights=[1.] * 19,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/aic.py
Vendored
+140
View File
@@ -0,0 +1,140 @@
dataset_info = dict(
dataset_name='aic',
paper_info=dict(
author='Wu, Jiahong and Zheng, He and Zhao, Bo and '
'Li, Yixin and Yan, Baoming and Liang, Rui and '
'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and '
'Fu, Yanwei and others',
title='Ai challenger: A large-scale dataset for going '
'deeper in image understanding',
container='arXiv',
year='2017',
homepage='https://github.com/AIChallenger/AI_Challenger_2017',
),
keypoint_info={
0:
dict(
name='right_shoulder',
id=0,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
1:
dict(
name='right_elbow',
id=1,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
2:
dict(
name='right_wrist',
id=2,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
3:
dict(
name='left_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
4:
dict(
name='left_elbow',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
5:
dict(
name='left_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
6:
dict(
name='right_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
7:
dict(
name='right_knee',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
8:
dict(
name='right_ankle',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
9:
dict(
name='left_hip',
id=9,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
10:
dict(
name='left_knee',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
11:
dict(
name='left_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
12:
dict(
name='head_top',
id=12,
color=[51, 153, 255],
type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('right_wrist', 'right_elbow'), id=0, color=[255, 128, 0]),
1: dict(
link=('right_elbow', 'right_shoulder'), id=1, color=[255, 128, 0]),
2: dict(link=('right_shoulder', 'neck'), id=2, color=[51, 153, 255]),
3: dict(link=('neck', 'left_shoulder'), id=3, color=[51, 153, 255]),
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6: dict(link=('right_ankle', 'right_knee'), id=6, color=[255, 128, 0]),
7: dict(link=('right_knee', 'right_hip'), id=7, color=[255, 128, 0]),
8: dict(link=('right_hip', 'left_hip'), id=8, color=[51, 153, 255]),
9: dict(link=('left_hip', 'left_knee'), id=9, color=[0, 255, 0]),
10: dict(link=('left_knee', 'left_ankle'), id=10, color=[0, 255, 0]),
11: dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]),
12: dict(
link=('right_shoulder', 'right_hip'), id=12, color=[51, 153, 255]),
13:
dict(link=('left_shoulder', 'left_hip'), id=13, color=[51, 153, 255])
},
joint_weights=[
1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.
],
# 'https://github.com/AIChallenger/AI_Challenger_2017/blob/master/'
# 'Evaluation/keypoint_eval/keypoint_eval.py#L50'
# delta = 2 x sigma
sigmas=[
0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144,
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081,
0.01291456, 0.01236173
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/aic_info.py
Vendored
+140
View File
@@ -0,0 +1,140 @@
aic_info = dict(
dataset_name='aic',
paper_info=dict(
author='Wu, Jiahong and Zheng, He and Zhao, Bo and '
'Li, Yixin and Yan, Baoming and Liang, Rui and '
'Wang, Wenjia and Zhou, Shipei and Lin, Guosen and '
'Fu, Yanwei and others',
title='Ai challenger: A large-scale dataset for going '
'deeper in image understanding',
container='arXiv',
year='2017',
homepage='https://github.com/AIChallenger/AI_Challenger_2017',
),
keypoint_info={
0:
dict(
name='right_shoulder',
id=0,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
1:
dict(
name='right_elbow',
id=1,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
2:
dict(
name='right_wrist',
id=2,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
3:
dict(
name='left_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
4:
dict(
name='left_elbow',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
5:
dict(
name='left_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
6:
dict(
name='right_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
7:
dict(
name='right_knee',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
8:
dict(
name='right_ankle',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
9:
dict(
name='left_hip',
id=9,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
10:
dict(
name='left_knee',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
11:
dict(
name='left_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
12:
dict(
name='head_top',
id=12,
color=[51, 153, 255],
type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('right_wrist', 'right_elbow'), id=0, color=[255, 128, 0]),
1: dict(
link=('right_elbow', 'right_shoulder'), id=1, color=[255, 128, 0]),
2: dict(link=('right_shoulder', 'neck'), id=2, color=[51, 153, 255]),
3: dict(link=('neck', 'left_shoulder'), id=3, color=[51, 153, 255]),
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6: dict(link=('right_ankle', 'right_knee'), id=6, color=[255, 128, 0]),
7: dict(link=('right_knee', 'right_hip'), id=7, color=[255, 128, 0]),
8: dict(link=('right_hip', 'left_hip'), id=8, color=[51, 153, 255]),
9: dict(link=('left_hip', 'left_knee'), id=9, color=[0, 255, 0]),
10: dict(link=('left_knee', 'left_ankle'), id=10, color=[0, 255, 0]),
11: dict(link=('head_top', 'neck'), id=11, color=[51, 153, 255]),
12: dict(
link=('right_shoulder', 'right_hip'), id=12, color=[51, 153, 255]),
13:
dict(link=('left_shoulder', 'left_hip'), id=13, color=[51, 153, 255])
},
joint_weights=[
1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.2, 1.5, 1., 1.
],
# 'https://github.com/AIChallenger/AI_Challenger_2017/blob/master/'
# 'Evaluation/keypoint_eval/keypoint_eval.py#L50'
# delta = 2 x sigma
sigmas=[
0.01388152, 0.01515228, 0.01057665, 0.01417709, 0.01497891, 0.01402144,
0.03909642, 0.03686941, 0.01981803, 0.03843971, 0.03412318, 0.02415081,
0.01291456, 0.01236173
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/animalpose.py
Vendored
+166
View File
@@ -0,0 +1,166 @@
dataset_info = dict(
dataset_name='animalpose',
paper_info=dict(
author='Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and '
'Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing',
title='Cross-Domain Adaptation for Animal Pose Estimation',
container='The IEEE International Conference on '
'Computer Vision (ICCV)',
year='2019',
homepage='https://sites.google.com/view/animal-pose/',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(
name='L_EarBase',
id=2,
color=[0, 255, 0],
type='upper',
swap='R_EarBase'),
3:
dict(
name='R_EarBase',
id=3,
color=[255, 128, 0],
type='upper',
swap='L_EarBase'),
4:
dict(name='Nose', id=4, color=[51, 153, 255], type='upper', swap=''),
5:
dict(name='Throat', id=5, color=[51, 153, 255], type='upper', swap=''),
6:
dict(
name='TailBase', id=6, color=[51, 153, 255], type='lower',
swap=''),
7:
dict(
name='Withers', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='L_F_Elbow',
id=8,
color=[0, 255, 0],
type='upper',
swap='R_F_Elbow'),
9:
dict(
name='R_F_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_F_Elbow'),
10:
dict(
name='L_B_Elbow',
id=10,
color=[0, 255, 0],
type='lower',
swap='R_B_Elbow'),
11:
dict(
name='R_B_Elbow',
id=11,
color=[255, 128, 0],
type='lower',
swap='L_B_Elbow'),
12:
dict(
name='L_F_Knee',
id=12,
color=[0, 255, 0],
type='upper',
swap='R_F_Knee'),
13:
dict(
name='R_F_Knee',
id=13,
color=[255, 128, 0],
type='upper',
swap='L_F_Knee'),
14:
dict(
name='L_B_Knee',
id=14,
color=[0, 255, 0],
type='lower',
swap='R_B_Knee'),
15:
dict(
name='R_B_Knee',
id=15,
color=[255, 128, 0],
type='lower',
swap='L_B_Knee'),
16:
dict(
name='L_F_Paw',
id=16,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
17:
dict(
name='R_F_Paw',
id=17,
color=[255, 128, 0],
type='upper',
swap='L_F_Paw'),
18:
dict(
name='L_B_Paw',
id=18,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
19:
dict(
name='R_B_Paw',
id=19,
color=[255, 128, 0],
type='lower',
swap='L_B_Paw')
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[51, 153, 255]),
1: dict(link=('L_Eye', 'L_EarBase'), id=1, color=[0, 255, 0]),
2: dict(link=('R_Eye', 'R_EarBase'), id=2, color=[255, 128, 0]),
3: dict(link=('L_Eye', 'Nose'), id=3, color=[0, 255, 0]),
4: dict(link=('R_Eye', 'Nose'), id=4, color=[255, 128, 0]),
5: dict(link=('Nose', 'Throat'), id=5, color=[51, 153, 255]),
6: dict(link=('Throat', 'Withers'), id=6, color=[51, 153, 255]),
7: dict(link=('TailBase', 'Withers'), id=7, color=[51, 153, 255]),
8: dict(link=('Throat', 'L_F_Elbow'), id=8, color=[0, 255, 0]),
9: dict(link=('L_F_Elbow', 'L_F_Knee'), id=9, color=[0, 255, 0]),
10: dict(link=('L_F_Knee', 'L_F_Paw'), id=10, color=[0, 255, 0]),
11: dict(link=('Throat', 'R_F_Elbow'), id=11, color=[255, 128, 0]),
12: dict(link=('R_F_Elbow', 'R_F_Knee'), id=12, color=[255, 128, 0]),
13: dict(link=('R_F_Knee', 'R_F_Paw'), id=13, color=[255, 128, 0]),
14: dict(link=('TailBase', 'L_B_Elbow'), id=14, color=[0, 255, 0]),
15: dict(link=('L_B_Elbow', 'L_B_Knee'), id=15, color=[0, 255, 0]),
16: dict(link=('L_B_Knee', 'L_B_Paw'), id=16, color=[0, 255, 0]),
17: dict(link=('TailBase', 'R_B_Elbow'), id=17, color=[255, 128, 0]),
18: dict(link=('R_B_Elbow', 'R_B_Knee'), id=18, color=[255, 128, 0]),
19: dict(link=('R_B_Knee', 'R_B_Paw'), id=19, color=[255, 128, 0])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2,
1.5, 1.5, 1.5, 1.5
],
# Note: The original paper did not provide enough information about
# the sigmas. We modified from 'https://github.com/cocodataset/'
# 'cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py#L523'
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.10, 0.10, 0.10, 0.107, 0.107,
0.107, 0.107, 0.087, 0.087, 0.087, 0.087, 0.089, 0.089, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/ap10k.py
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dataset_info = dict(
dataset_name='ap10k',
paper_info=dict(
author='Yu, Hang and Xu, Yufei and Zhang, Jing and '
'Zhao, Wei and Guan, Ziyu and Tao, Dacheng',
title='AP-10K: A Benchmark for Animal Pose Estimation in the Wild',
container='35th Conference on Neural Information Processing Systems '
'(NeurIPS 2021) Track on Datasets and Bench-marks.',
year='2021',
homepage='https://github.com/AlexTheBad/AP-10K',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(name='Nose', id=2, color=[51, 153, 255], type='upper', swap=''),
3:
dict(name='Neck', id=3, color=[51, 153, 255], type='upper', swap=''),
4:
dict(
name='Root of tail',
id=4,
color=[51, 153, 255],
type='lower',
swap=''),
5:
dict(
name='L_Shoulder',
id=5,
color=[51, 153, 255],
type='upper',
swap='R_Shoulder'),
6:
dict(
name='L_Elbow',
id=6,
color=[51, 153, 255],
type='upper',
swap='R_Elbow'),
7:
dict(
name='L_F_Paw',
id=7,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
8:
dict(
name='R_Shoulder',
id=8,
color=[0, 255, 0],
type='upper',
swap='L_Shoulder'),
9:
dict(
name='R_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_Elbow'),
10:
dict(
name='R_F_Paw',
id=10,
color=[0, 255, 0],
type='lower',
swap='L_F_Paw'),
11:
dict(
name='L_Hip',
id=11,
color=[255, 128, 0],
type='lower',
swap='R_Hip'),
12:
dict(
name='L_Knee',
id=12,
color=[255, 128, 0],
type='lower',
swap='R_Knee'),
13:
dict(
name='L_B_Paw',
id=13,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
14:
dict(
name='R_Hip', id=14, color=[0, 255, 0], type='lower',
swap='L_Hip'),
15:
dict(
name='R_Knee',
id=15,
color=[0, 255, 0],
type='lower',
swap='L_Knee'),
16:
dict(
name='R_B_Paw',
id=16,
color=[0, 255, 0],
type='lower',
swap='L_B_Paw'),
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[0, 0, 255]),
1: dict(link=('L_Eye', 'Nose'), id=1, color=[0, 0, 255]),
2: dict(link=('R_Eye', 'Nose'), id=2, color=[0, 0, 255]),
3: dict(link=('Nose', 'Neck'), id=3, color=[0, 255, 0]),
4: dict(link=('Neck', 'Root of tail'), id=4, color=[0, 255, 0]),
5: dict(link=('Neck', 'L_Shoulder'), id=5, color=[0, 255, 255]),
6: dict(link=('L_Shoulder', 'L_Elbow'), id=6, color=[0, 255, 255]),
7: dict(link=('L_Elbow', 'L_F_Paw'), id=6, color=[0, 255, 255]),
8: dict(link=('Neck', 'R_Shoulder'), id=7, color=[6, 156, 250]),
9: dict(link=('R_Shoulder', 'R_Elbow'), id=8, color=[6, 156, 250]),
10: dict(link=('R_Elbow', 'R_F_Paw'), id=9, color=[6, 156, 250]),
11: dict(link=('Root of tail', 'L_Hip'), id=10, color=[0, 255, 255]),
12: dict(link=('L_Hip', 'L_Knee'), id=11, color=[0, 255, 255]),
13: dict(link=('L_Knee', 'L_B_Paw'), id=12, color=[0, 255, 255]),
14: dict(link=('Root of tail', 'R_Hip'), id=13, color=[6, 156, 250]),
15: dict(link=('R_Hip', 'R_Knee'), id=14, color=[6, 156, 250]),
16: dict(link=('R_Knee', 'R_B_Paw'), id=15, color=[6, 156, 250]),
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.079, 0.072, 0.062, 0.079, 0.072,
0.062, 0.107, 0.087, 0.089, 0.107, 0.087, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/ap10k_info.py
Vendored
+142
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ap10k_info = dict(
dataset_name='ap10k',
paper_info=dict(
author='Yu, Hang and Xu, Yufei and Zhang, Jing and '
'Zhao, Wei and Guan, Ziyu and Tao, Dacheng',
title='AP-10K: A Benchmark for Animal Pose Estimation in the Wild',
container='35th Conference on Neural Information Processing Systems '
'(NeurIPS 2021) Track on Datasets and Bench-marks.',
year='2021',
homepage='https://github.com/AlexTheBad/AP-10K',
),
keypoint_info={
0:
dict(
name='L_Eye', id=0, color=[0, 255, 0], type='upper', swap='R_Eye'),
1:
dict(
name='R_Eye',
id=1,
color=[255, 128, 0],
type='upper',
swap='L_Eye'),
2:
dict(name='Nose', id=2, color=[51, 153, 255], type='upper', swap=''),
3:
dict(name='Neck', id=3, color=[51, 153, 255], type='upper', swap=''),
4:
dict(
name='Root of tail',
id=4,
color=[51, 153, 255],
type='lower',
swap=''),
5:
dict(
name='L_Shoulder',
id=5,
color=[51, 153, 255],
type='upper',
swap='R_Shoulder'),
6:
dict(
name='L_Elbow',
id=6,
color=[51, 153, 255],
type='upper',
swap='R_Elbow'),
7:
dict(
name='L_F_Paw',
id=7,
color=[0, 255, 0],
type='upper',
swap='R_F_Paw'),
8:
dict(
name='R_Shoulder',
id=8,
color=[0, 255, 0],
type='upper',
swap='L_Shoulder'),
9:
dict(
name='R_Elbow',
id=9,
color=[255, 128, 0],
type='upper',
swap='L_Elbow'),
10:
dict(
name='R_F_Paw',
id=10,
color=[0, 255, 0],
type='lower',
swap='L_F_Paw'),
11:
dict(
name='L_Hip',
id=11,
color=[255, 128, 0],
type='lower',
swap='R_Hip'),
12:
dict(
name='L_Knee',
id=12,
color=[255, 128, 0],
type='lower',
swap='R_Knee'),
13:
dict(
name='L_B_Paw',
id=13,
color=[0, 255, 0],
type='lower',
swap='R_B_Paw'),
14:
dict(
name='R_Hip', id=14, color=[0, 255, 0], type='lower',
swap='L_Hip'),
15:
dict(
name='R_Knee',
id=15,
color=[0, 255, 0],
type='lower',
swap='L_Knee'),
16:
dict(
name='R_B_Paw',
id=16,
color=[0, 255, 0],
type='lower',
swap='L_B_Paw'),
},
skeleton_info={
0: dict(link=('L_Eye', 'R_Eye'), id=0, color=[0, 0, 255]),
1: dict(link=('L_Eye', 'Nose'), id=1, color=[0, 0, 255]),
2: dict(link=('R_Eye', 'Nose'), id=2, color=[0, 0, 255]),
3: dict(link=('Nose', 'Neck'), id=3, color=[0, 255, 0]),
4: dict(link=('Neck', 'Root of tail'), id=4, color=[0, 255, 0]),
5: dict(link=('Neck', 'L_Shoulder'), id=5, color=[0, 255, 255]),
6: dict(link=('L_Shoulder', 'L_Elbow'), id=6, color=[0, 255, 255]),
7: dict(link=('L_Elbow', 'L_F_Paw'), id=6, color=[0, 255, 255]),
8: dict(link=('Neck', 'R_Shoulder'), id=7, color=[6, 156, 250]),
9: dict(link=('R_Shoulder', 'R_Elbow'), id=8, color=[6, 156, 250]),
10: dict(link=('R_Elbow', 'R_F_Paw'), id=9, color=[6, 156, 250]),
11: dict(link=('Root of tail', 'L_Hip'), id=10, color=[0, 255, 255]),
12: dict(link=('L_Hip', 'L_Knee'), id=11, color=[0, 255, 255]),
13: dict(link=('L_Knee', 'L_B_Paw'), id=12, color=[0, 255, 255]),
14: dict(link=('Root of tail', 'R_Hip'), id=13, color=[6, 156, 250]),
15: dict(link=('R_Hip', 'R_Knee'), id=14, color=[6, 156, 250]),
16: dict(link=('R_Knee', 'R_B_Paw'), id=15, color=[6, 156, 250]),
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.025, 0.025, 0.026, 0.035, 0.035, 0.079, 0.072, 0.062, 0.079, 0.072,
0.062, 0.107, 0.087, 0.089, 0.107, 0.087, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/atrw.py
Vendored
+144
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dataset_info = dict(
dataset_name='atrw',
paper_info=dict(
author='Li, Shuyuan and Li, Jianguo and Tang, Hanlin '
'and Qian, Rui and Lin, Weiyao',
title='ATRW: A Benchmark for Amur Tiger '
'Re-identification in the Wild',
container='Proceedings of the 28th ACM '
'International Conference on Multimedia',
year='2020',
homepage='https://cvwc2019.github.io/challenge.html',
),
keypoint_info={
0:
dict(
name='left_ear',
id=0,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
1:
dict(
name='right_ear',
id=1,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
2:
dict(name='nose', id=2, color=[51, 153, 255], type='upper', swap=''),
3:
dict(
name='right_shoulder',
id=3,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
4:
dict(
name='right_front_paw',
id=4,
color=[255, 128, 0],
type='upper',
swap='left_front_paw'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='left_front_paw',
id=6,
color=[0, 255, 0],
type='upper',
swap='right_front_paw'),
7:
dict(
name='right_hip',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
8:
dict(
name='right_knee',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
9:
dict(
name='right_back_paw',
id=9,
color=[255, 128, 0],
type='lower',
swap='left_back_paw'),
10:
dict(
name='left_hip',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
11:
dict(
name='left_knee',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
12:
dict(
name='left_back_paw',
id=12,
color=[0, 255, 0],
type='lower',
swap='right_back_paw'),
13:
dict(name='tail', id=13, color=[51, 153, 255], type='lower', swap=''),
14:
dict(
name='center', id=14, color=[51, 153, 255], type='lower', swap=''),
},
skeleton_info={
0:
dict(link=('left_ear', 'nose'), id=0, color=[51, 153, 255]),
1:
dict(link=('right_ear', 'nose'), id=1, color=[51, 153, 255]),
2:
dict(link=('nose', 'center'), id=2, color=[51, 153, 255]),
3:
dict(
link=('left_shoulder', 'left_front_paw'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_shoulder', 'center'), id=4, color=[0, 255, 0]),
5:
dict(
link=('right_shoulder', 'right_front_paw'),
id=5,
color=[255, 128, 0]),
6:
dict(link=('right_shoulder', 'center'), id=6, color=[255, 128, 0]),
7:
dict(link=('tail', 'center'), id=7, color=[51, 153, 255]),
8:
dict(link=('right_back_paw', 'right_knee'), id=8, color=[255, 128, 0]),
9:
dict(link=('right_knee', 'right_hip'), id=9, color=[255, 128, 0]),
10:
dict(link=('right_hip', 'tail'), id=10, color=[255, 128, 0]),
11:
dict(link=('left_back_paw', 'left_knee'), id=11, color=[0, 255, 0]),
12:
dict(link=('left_knee', 'left_hip'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_hip', 'tail'), id=13, color=[0, 255, 0]),
},
joint_weights=[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
sigmas=[
0.0277, 0.0823, 0.0831, 0.0202, 0.0716, 0.0263, 0.0646, 0.0302, 0.0440,
0.0316, 0.0333, 0.0547, 0.0263, 0.0683, 0.0539
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/coco.py
Vendored
+181
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dataset_info = dict(
dataset_name='coco',
paper_info=dict(
author='Lin, Tsung-Yi and Maire, Michael and '
'Belongie, Serge and Hays, James and '
'Perona, Pietro and Ramanan, Deva and '
r'Doll{\'a}r, Piotr and Zitnick, C Lawrence',
title='Microsoft coco: Common objects in context',
container='European conference on computer vision',
year='2014',
homepage='http://cocodataset.org/',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/coco_wholebody.py
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third_party/hamer/third-party/ViTPose/configs/_base_/datasets/coco_wholebody_face.py
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dataset_info = dict(
dataset_name='coco_wholebody_face',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={
0:
dict(
name='face-0',
id=0,
color=[255, 255, 255],
type='',
swap='face-16'),
1:
dict(
name='face-1',
id=1,
color=[255, 255, 255],
type='',
swap='face-15'),
2:
dict(
name='face-2',
id=2,
color=[255, 255, 255],
type='',
swap='face-14'),
3:
dict(
name='face-3',
id=3,
color=[255, 255, 255],
type='',
swap='face-13'),
4:
dict(
name='face-4',
id=4,
color=[255, 255, 255],
type='',
swap='face-12'),
5:
dict(
name='face-5',
id=5,
color=[255, 255, 255],
type='',
swap='face-11'),
6:
dict(
name='face-6',
id=6,
color=[255, 255, 255],
type='',
swap='face-10'),
7:
dict(
name='face-7', id=7, color=[255, 255, 255], type='',
swap='face-9'),
8:
dict(name='face-8', id=8, color=[255, 255, 255], type='', swap=''),
9:
dict(
name='face-9', id=9, color=[255, 255, 255], type='',
swap='face-7'),
10:
dict(
name='face-10',
id=10,
color=[255, 255, 255],
type='',
swap='face-6'),
11:
dict(
name='face-11',
id=11,
color=[255, 255, 255],
type='',
swap='face-5'),
12:
dict(
name='face-12',
id=12,
color=[255, 255, 255],
type='',
swap='face-4'),
13:
dict(
name='face-13',
id=13,
color=[255, 255, 255],
type='',
swap='face-3'),
14:
dict(
name='face-14',
id=14,
color=[255, 255, 255],
type='',
swap='face-2'),
15:
dict(
name='face-15',
id=15,
color=[255, 255, 255],
type='',
swap='face-1'),
16:
dict(
name='face-16',
id=16,
color=[255, 255, 255],
type='',
swap='face-0'),
17:
dict(
name='face-17',
id=17,
color=[255, 255, 255],
type='',
swap='face-26'),
18:
dict(
name='face-18',
id=18,
color=[255, 255, 255],
type='',
swap='face-25'),
19:
dict(
name='face-19',
id=19,
color=[255, 255, 255],
type='',
swap='face-24'),
20:
dict(
name='face-20',
id=20,
color=[255, 255, 255],
type='',
swap='face-23'),
21:
dict(
name='face-21',
id=21,
color=[255, 255, 255],
type='',
swap='face-22'),
22:
dict(
name='face-22',
id=22,
color=[255, 255, 255],
type='',
swap='face-21'),
23:
dict(
name='face-23',
id=23,
color=[255, 255, 255],
type='',
swap='face-20'),
24:
dict(
name='face-24',
id=24,
color=[255, 255, 255],
type='',
swap='face-19'),
25:
dict(
name='face-25',
id=25,
color=[255, 255, 255],
type='',
swap='face-18'),
26:
dict(
name='face-26',
id=26,
color=[255, 255, 255],
type='',
swap='face-17'),
27:
dict(name='face-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='face-28', id=28, color=[255, 255, 255], type='', swap=''),
29:
dict(name='face-29', id=29, color=[255, 255, 255], type='', swap=''),
30:
dict(name='face-30', id=30, color=[255, 255, 255], type='', swap=''),
31:
dict(
name='face-31',
id=31,
color=[255, 255, 255],
type='',
swap='face-35'),
32:
dict(
name='face-32',
id=32,
color=[255, 255, 255],
type='',
swap='face-34'),
33:
dict(name='face-33', id=33, color=[255, 255, 255], type='', swap=''),
34:
dict(
name='face-34',
id=34,
color=[255, 255, 255],
type='',
swap='face-32'),
35:
dict(
name='face-35',
id=35,
color=[255, 255, 255],
type='',
swap='face-31'),
36:
dict(
name='face-36',
id=36,
color=[255, 255, 255],
type='',
swap='face-45'),
37:
dict(
name='face-37',
id=37,
color=[255, 255, 255],
type='',
swap='face-44'),
38:
dict(
name='face-38',
id=38,
color=[255, 255, 255],
type='',
swap='face-43'),
39:
dict(
name='face-39',
id=39,
color=[255, 255, 255],
type='',
swap='face-42'),
40:
dict(
name='face-40',
id=40,
color=[255, 255, 255],
type='',
swap='face-47'),
41:
dict(
name='face-41',
id=41,
color=[255, 255, 255],
type='',
swap='face-46'),
42:
dict(
name='face-42',
id=42,
color=[255, 255, 255],
type='',
swap='face-39'),
43:
dict(
name='face-43',
id=43,
color=[255, 255, 255],
type='',
swap='face-38'),
44:
dict(
name='face-44',
id=44,
color=[255, 255, 255],
type='',
swap='face-37'),
45:
dict(
name='face-45',
id=45,
color=[255, 255, 255],
type='',
swap='face-36'),
46:
dict(
name='face-46',
id=46,
color=[255, 255, 255],
type='',
swap='face-41'),
47:
dict(
name='face-47',
id=47,
color=[255, 255, 255],
type='',
swap='face-40'),
48:
dict(
name='face-48',
id=48,
color=[255, 255, 255],
type='',
swap='face-54'),
49:
dict(
name='face-49',
id=49,
color=[255, 255, 255],
type='',
swap='face-53'),
50:
dict(
name='face-50',
id=50,
color=[255, 255, 255],
type='',
swap='face-52'),
51:
dict(name='face-51', id=52, color=[255, 255, 255], type='', swap=''),
52:
dict(
name='face-52',
id=52,
color=[255, 255, 255],
type='',
swap='face-50'),
53:
dict(
name='face-53',
id=53,
color=[255, 255, 255],
type='',
swap='face-49'),
54:
dict(
name='face-54',
id=54,
color=[255, 255, 255],
type='',
swap='face-48'),
55:
dict(
name='face-55',
id=55,
color=[255, 255, 255],
type='',
swap='face-59'),
56:
dict(
name='face-56',
id=56,
color=[255, 255, 255],
type='',
swap='face-58'),
57:
dict(name='face-57', id=57, color=[255, 255, 255], type='', swap=''),
58:
dict(
name='face-58',
id=58,
color=[255, 255, 255],
type='',
swap='face-56'),
59:
dict(
name='face-59',
id=59,
color=[255, 255, 255],
type='',
swap='face-55'),
60:
dict(
name='face-60',
id=60,
color=[255, 255, 255],
type='',
swap='face-64'),
61:
dict(
name='face-61',
id=61,
color=[255, 255, 255],
type='',
swap='face-63'),
62:
dict(name='face-62', id=62, color=[255, 255, 255], type='', swap=''),
63:
dict(
name='face-63',
id=63,
color=[255, 255, 255],
type='',
swap='face-61'),
64:
dict(
name='face-64',
id=64,
color=[255, 255, 255],
type='',
swap='face-60'),
65:
dict(
name='face-65',
id=65,
color=[255, 255, 255],
type='',
swap='face-67'),
66:
dict(name='face-66', id=66, color=[255, 255, 255], type='', swap=''),
67:
dict(
name='face-67',
id=67,
color=[255, 255, 255],
type='',
swap='face-65')
},
skeleton_info={},
joint_weights=[1.] * 68,
# 'https://github.com/jin-s13/COCO-WholeBody/blob/master/'
# 'evaluation/myeval_wholebody.py#L177'
sigmas=[
0.042, 0.043, 0.044, 0.043, 0.040, 0.035, 0.031, 0.025, 0.020, 0.023,
0.029, 0.032, 0.037, 0.038, 0.043, 0.041, 0.045, 0.013, 0.012, 0.011,
0.011, 0.012, 0.012, 0.011, 0.011, 0.013, 0.015, 0.009, 0.007, 0.007,
0.007, 0.012, 0.009, 0.008, 0.016, 0.010, 0.017, 0.011, 0.009, 0.011,
0.009, 0.007, 0.013, 0.008, 0.011, 0.012, 0.010, 0.034, 0.008, 0.008,
0.009, 0.008, 0.008, 0.007, 0.010, 0.008, 0.009, 0.009, 0.009, 0.007,
0.007, 0.008, 0.011, 0.008, 0.008, 0.008, 0.01, 0.008
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/coco_wholebody_hand.py
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dataset_info = dict(
dataset_name='coco_wholebody_hand',
paper_info=dict(
author='Jin, Sheng and Xu, Lumin and Xu, Jin and '
'Wang, Can and Liu, Wentao and '
'Qian, Chen and Ouyang, Wanli and Luo, Ping',
title='Whole-Body Human Pose Estimation in the Wild',
container='Proceedings of the European '
'Conference on Computer Vision (ECCV)',
year='2020',
homepage='https://github.com/jin-s13/COCO-WholeBody/',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[
0.029, 0.022, 0.035, 0.037, 0.047, 0.026, 0.025, 0.024, 0.035, 0.018,
0.024, 0.022, 0.026, 0.017, 0.021, 0.021, 0.032, 0.02, 0.019, 0.022,
0.031
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/coco_wholebody_info.py
Vendored
+1154
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third_party/hamer/third-party/ViTPose/configs/_base_/datasets/cofw.py
Vendored
+134
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dataset_info = dict(
dataset_name='cofw',
paper_info=dict(
author='Burgos-Artizzu, Xavier P and Perona, '
r'Pietro and Doll{\'a}r, Piotr',
title='Robust face landmark estimation under occlusion',
container='Proceedings of the IEEE international '
'conference on computer vision',
year='2013',
homepage='http://www.vision.caltech.edu/xpburgos/ICCV13/',
),
keypoint_info={
0:
dict(name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-1'),
1:
dict(name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-0'),
2:
dict(name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-3'),
3:
dict(name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-2'),
4:
dict(name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-6'),
5:
dict(name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-7'),
6:
dict(name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-4'),
7:
dict(name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-5'),
8:
dict(name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-9'),
9:
dict(name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-8'),
10:
dict(
name='kpt-10',
id=10,
color=[255, 255, 255],
type='',
swap='kpt-11'),
11:
dict(
name='kpt-11',
id=11,
color=[255, 255, 255],
type='',
swap='kpt-10'),
12:
dict(
name='kpt-12',
id=12,
color=[255, 255, 255],
type='',
swap='kpt-14'),
13:
dict(
name='kpt-13',
id=13,
color=[255, 255, 255],
type='',
swap='kpt-15'),
14:
dict(
name='kpt-14',
id=14,
color=[255, 255, 255],
type='',
swap='kpt-12'),
15:
dict(
name='kpt-15',
id=15,
color=[255, 255, 255],
type='',
swap='kpt-13'),
16:
dict(
name='kpt-16',
id=16,
color=[255, 255, 255],
type='',
swap='kpt-17'),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-16'),
18:
dict(
name='kpt-18',
id=18,
color=[255, 255, 255],
type='',
swap='kpt-19'),
19:
dict(
name='kpt-19',
id=19,
color=[255, 255, 255],
type='',
swap='kpt-18'),
20:
dict(name='kpt-20', id=20, color=[255, 255, 255], type='', swap=''),
21:
dict(name='kpt-21', id=21, color=[255, 255, 255], type='', swap=''),
22:
dict(
name='kpt-22',
id=22,
color=[255, 255, 255],
type='',
swap='kpt-23'),
23:
dict(
name='kpt-23',
id=23,
color=[255, 255, 255],
type='',
swap='kpt-22'),
24:
dict(name='kpt-24', id=24, color=[255, 255, 255], type='', swap=''),
25:
dict(name='kpt-25', id=25, color=[255, 255, 255], type='', swap=''),
26:
dict(name='kpt-26', id=26, color=[255, 255, 255], type='', swap=''),
27:
dict(name='kpt-27', id=27, color=[255, 255, 255], type='', swap=''),
28:
dict(name='kpt-28', id=28, color=[255, 255, 255], type='', swap='')
},
skeleton_info={},
joint_weights=[1.] * 29,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/crowdpose.py
Vendored
+147
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dataset_info = dict(
dataset_name='crowdpose',
paper_info=dict(
author='Li, Jiefeng and Wang, Can and Zhu, Hao and '
'Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu',
title='CrowdPose: Efficient Crowded Scenes Pose Estimation '
'and A New Benchmark',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2019',
homepage='https://github.com/Jeff-sjtu/CrowdPose',
),
keypoint_info={
0:
dict(
name='left_shoulder',
id=0,
color=[51, 153, 255],
type='upper',
swap='right_shoulder'),
1:
dict(
name='right_shoulder',
id=1,
color=[51, 153, 255],
type='upper',
swap='left_shoulder'),
2:
dict(
name='left_elbow',
id=2,
color=[51, 153, 255],
type='upper',
swap='right_elbow'),
3:
dict(
name='right_elbow',
id=3,
color=[51, 153, 255],
type='upper',
swap='left_elbow'),
4:
dict(
name='left_wrist',
id=4,
color=[51, 153, 255],
type='upper',
swap='right_wrist'),
5:
dict(
name='right_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='left_wrist'),
6:
dict(
name='left_hip',
id=6,
color=[255, 128, 0],
type='lower',
swap='right_hip'),
7:
dict(
name='right_hip',
id=7,
color=[0, 255, 0],
type='lower',
swap='left_hip'),
8:
dict(
name='left_knee',
id=8,
color=[255, 128, 0],
type='lower',
swap='right_knee'),
9:
dict(
name='right_knee',
id=9,
color=[0, 255, 0],
type='lower',
swap='left_knee'),
10:
dict(
name='left_ankle',
id=10,
color=[255, 128, 0],
type='lower',
swap='right_ankle'),
11:
dict(
name='right_ankle',
id=11,
color=[0, 255, 0],
type='lower',
swap='left_ankle'),
12:
dict(
name='top_head', id=12, color=[255, 128, 0], type='upper',
swap=''),
13:
dict(name='neck', id=13, color=[0, 255, 0], type='upper', swap='')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('top_head', 'neck'), id=12, color=[51, 153, 255]),
13:
dict(link=('right_shoulder', 'neck'), id=13, color=[51, 153, 255]),
14:
dict(link=('left_shoulder', 'neck'), id=14, color=[51, 153, 255])
},
joint_weights=[
0.2, 0.2, 0.2, 1.3, 1.5, 0.2, 1.3, 1.5, 0.2, 0.2, 0.5, 0.2, 0.2, 0.5
],
sigmas=[
0.079, 0.079, 0.072, 0.072, 0.062, 0.062, 0.107, 0.107, 0.087, 0.087,
0.089, 0.089, 0.079, 0.079
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/deepfashion_full.py
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dataset_info = dict(
dataset_name='deepfashion_full',
paper_info=dict(
author='Liu, Ziwei and Luo, Ping and Qiu, Shi '
'and Wang, Xiaogang and Tang, Xiaoou',
title='DeepFashion: Powering Robust Clothes Recognition '
'and Retrieval with Rich Annotations',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2016',
homepage='http://mmlab.ie.cuhk.edu.hk/projects/'
'DeepFashion/LandmarkDetection.html',
),
keypoint_info={
0:
dict(
name='left collar',
id=0,
color=[255, 255, 255],
type='',
swap='right collar'),
1:
dict(
name='right collar',
id=1,
color=[255, 255, 255],
type='',
swap='left collar'),
2:
dict(
name='left sleeve',
id=2,
color=[255, 255, 255],
type='',
swap='right sleeve'),
3:
dict(
name='right sleeve',
id=3,
color=[255, 255, 255],
type='',
swap='left sleeve'),
4:
dict(
name='left waistline',
id=0,
color=[255, 255, 255],
type='',
swap='right waistline'),
5:
dict(
name='right waistline',
id=1,
color=[255, 255, 255],
type='',
swap='left waistline'),
6:
dict(
name='left hem',
id=2,
color=[255, 255, 255],
type='',
swap='right hem'),
7:
dict(
name='right hem',
id=3,
color=[255, 255, 255],
type='',
swap='left hem'),
},
skeleton_info={},
joint_weights=[1.] * 8,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/deepfashion_lower.py
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dataset_info = dict(
dataset_name='deepfashion_lower',
paper_info=dict(
author='Liu, Ziwei and Luo, Ping and Qiu, Shi '
'and Wang, Xiaogang and Tang, Xiaoou',
title='DeepFashion: Powering Robust Clothes Recognition '
'and Retrieval with Rich Annotations',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2016',
homepage='http://mmlab.ie.cuhk.edu.hk/projects/'
'DeepFashion/LandmarkDetection.html',
),
keypoint_info={
0:
dict(
name='left waistline',
id=0,
color=[255, 255, 255],
type='',
swap='right waistline'),
1:
dict(
name='right waistline',
id=1,
color=[255, 255, 255],
type='',
swap='left waistline'),
2:
dict(
name='left hem',
id=2,
color=[255, 255, 255],
type='',
swap='right hem'),
3:
dict(
name='right hem',
id=3,
color=[255, 255, 255],
type='',
swap='left hem'),
},
skeleton_info={},
joint_weights=[1.] * 4,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/deepfashion_upper.py
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dataset_info = dict(
dataset_name='deepfashion_upper',
paper_info=dict(
author='Liu, Ziwei and Luo, Ping and Qiu, Shi '
'and Wang, Xiaogang and Tang, Xiaoou',
title='DeepFashion: Powering Robust Clothes Recognition '
'and Retrieval with Rich Annotations',
container='Proceedings of IEEE Conference on Computer '
'Vision and Pattern Recognition (CVPR)',
year='2016',
homepage='http://mmlab.ie.cuhk.edu.hk/projects/'
'DeepFashion/LandmarkDetection.html',
),
keypoint_info={
0:
dict(
name='left collar',
id=0,
color=[255, 255, 255],
type='',
swap='right collar'),
1:
dict(
name='right collar',
id=1,
color=[255, 255, 255],
type='',
swap='left collar'),
2:
dict(
name='left sleeve',
id=2,
color=[255, 255, 255],
type='',
swap='right sleeve'),
3:
dict(
name='right sleeve',
id=3,
color=[255, 255, 255],
type='',
swap='left sleeve'),
4:
dict(
name='left hem',
id=4,
color=[255, 255, 255],
type='',
swap='right hem'),
5:
dict(
name='right hem',
id=5,
color=[255, 255, 255],
type='',
swap='left hem'),
},
skeleton_info={},
joint_weights=[1.] * 6,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/fly.py
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dataset_info = dict(
dataset_name='fly',
paper_info=dict(
author='Pereira, Talmo D and Aldarondo, Diego E and '
'Willmore, Lindsay and Kislin, Mikhail and '
'Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W',
title='Fast animal pose estimation using deep neural networks',
container='Nature methods',
year='2019',
homepage='https://github.com/jgraving/DeepPoseKit-Data',
),
keypoint_info={
0:
dict(name='head', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='eyeL', id=1, color=[255, 255, 255], type='', swap='eyeR'),
2:
dict(name='eyeR', id=2, color=[255, 255, 255], type='', swap='eyeL'),
3:
dict(name='neck', id=3, color=[255, 255, 255], type='', swap=''),
4:
dict(name='thorax', id=4, color=[255, 255, 255], type='', swap=''),
5:
dict(name='abdomen', id=5, color=[255, 255, 255], type='', swap=''),
6:
dict(
name='forelegR1',
id=6,
color=[255, 255, 255],
type='',
swap='forelegL1'),
7:
dict(
name='forelegR2',
id=7,
color=[255, 255, 255],
type='',
swap='forelegL2'),
8:
dict(
name='forelegR3',
id=8,
color=[255, 255, 255],
type='',
swap='forelegL3'),
9:
dict(
name='forelegR4',
id=9,
color=[255, 255, 255],
type='',
swap='forelegL4'),
10:
dict(
name='midlegR1',
id=10,
color=[255, 255, 255],
type='',
swap='midlegL1'),
11:
dict(
name='midlegR2',
id=11,
color=[255, 255, 255],
type='',
swap='midlegL2'),
12:
dict(
name='midlegR3',
id=12,
color=[255, 255, 255],
type='',
swap='midlegL3'),
13:
dict(
name='midlegR4',
id=13,
color=[255, 255, 255],
type='',
swap='midlegL4'),
14:
dict(
name='hindlegR1',
id=14,
color=[255, 255, 255],
type='',
swap='hindlegL1'),
15:
dict(
name='hindlegR2',
id=15,
color=[255, 255, 255],
type='',
swap='hindlegL2'),
16:
dict(
name='hindlegR3',
id=16,
color=[255, 255, 255],
type='',
swap='hindlegL3'),
17:
dict(
name='hindlegR4',
id=17,
color=[255, 255, 255],
type='',
swap='hindlegL4'),
18:
dict(
name='forelegL1',
id=18,
color=[255, 255, 255],
type='',
swap='forelegR1'),
19:
dict(
name='forelegL2',
id=19,
color=[255, 255, 255],
type='',
swap='forelegR2'),
20:
dict(
name='forelegL3',
id=20,
color=[255, 255, 255],
type='',
swap='forelegR3'),
21:
dict(
name='forelegL4',
id=21,
color=[255, 255, 255],
type='',
swap='forelegR4'),
22:
dict(
name='midlegL1',
id=22,
color=[255, 255, 255],
type='',
swap='midlegR1'),
23:
dict(
name='midlegL2',
id=23,
color=[255, 255, 255],
type='',
swap='midlegR2'),
24:
dict(
name='midlegL3',
id=24,
color=[255, 255, 255],
type='',
swap='midlegR3'),
25:
dict(
name='midlegL4',
id=25,
color=[255, 255, 255],
type='',
swap='midlegR4'),
26:
dict(
name='hindlegL1',
id=26,
color=[255, 255, 255],
type='',
swap='hindlegR1'),
27:
dict(
name='hindlegL2',
id=27,
color=[255, 255, 255],
type='',
swap='hindlegR2'),
28:
dict(
name='hindlegL3',
id=28,
color=[255, 255, 255],
type='',
swap='hindlegR3'),
29:
dict(
name='hindlegL4',
id=29,
color=[255, 255, 255],
type='',
swap='hindlegR4'),
30:
dict(
name='wingL', id=30, color=[255, 255, 255], type='', swap='wingR'),
31:
dict(
name='wingR', id=31, color=[255, 255, 255], type='', swap='wingL'),
},
skeleton_info={
0: dict(link=('eyeL', 'head'), id=0, color=[255, 255, 255]),
1: dict(link=('eyeR', 'head'), id=1, color=[255, 255, 255]),
2: dict(link=('neck', 'head'), id=2, color=[255, 255, 255]),
3: dict(link=('thorax', 'neck'), id=3, color=[255, 255, 255]),
4: dict(link=('abdomen', 'thorax'), id=4, color=[255, 255, 255]),
5: dict(link=('forelegR2', 'forelegR1'), id=5, color=[255, 255, 255]),
6: dict(link=('forelegR3', 'forelegR2'), id=6, color=[255, 255, 255]),
7: dict(link=('forelegR4', 'forelegR3'), id=7, color=[255, 255, 255]),
8: dict(link=('midlegR2', 'midlegR1'), id=8, color=[255, 255, 255]),
9: dict(link=('midlegR3', 'midlegR2'), id=9, color=[255, 255, 255]),
10: dict(link=('midlegR4', 'midlegR3'), id=10, color=[255, 255, 255]),
11:
dict(link=('hindlegR2', 'hindlegR1'), id=11, color=[255, 255, 255]),
12:
dict(link=('hindlegR3', 'hindlegR2'), id=12, color=[255, 255, 255]),
13:
dict(link=('hindlegR4', 'hindlegR3'), id=13, color=[255, 255, 255]),
14:
dict(link=('forelegL2', 'forelegL1'), id=14, color=[255, 255, 255]),
15:
dict(link=('forelegL3', 'forelegL2'), id=15, color=[255, 255, 255]),
16:
dict(link=('forelegL4', 'forelegL3'), id=16, color=[255, 255, 255]),
17: dict(link=('midlegL2', 'midlegL1'), id=17, color=[255, 255, 255]),
18: dict(link=('midlegL3', 'midlegL2'), id=18, color=[255, 255, 255]),
19: dict(link=('midlegL4', 'midlegL3'), id=19, color=[255, 255, 255]),
20:
dict(link=('hindlegL2', 'hindlegL1'), id=20, color=[255, 255, 255]),
21:
dict(link=('hindlegL3', 'hindlegL2'), id=21, color=[255, 255, 255]),
22:
dict(link=('hindlegL4', 'hindlegL3'), id=22, color=[255, 255, 255]),
23: dict(link=('wingL', 'neck'), id=23, color=[255, 255, 255]),
24: dict(link=('wingR', 'neck'), id=24, color=[255, 255, 255])
},
joint_weights=[1.] * 32,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/freihand2d.py
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dataset_info = dict(
dataset_name='freihand',
paper_info=dict(
author='Zimmermann, Christian and Ceylan, Duygu and '
'Yang, Jimei and Russell, Bryan and '
'Argus, Max and Brox, Thomas',
title='Freihand: A dataset for markerless capture of hand pose '
'and shape from single rgb images',
container='Proceedings of the IEEE International '
'Conference on Computer Vision',
year='2019',
homepage='https://lmb.informatik.uni-freiburg.de/projects/freihand/',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/h36m.py
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dataset_info = dict(
dataset_name='h36m',
paper_info=dict(
author='Ionescu, Catalin and Papava, Dragos and '
'Olaru, Vlad and Sminchisescu, Cristian',
title='Human3.6M: Large Scale Datasets and Predictive '
'Methods for 3D Human Sensing in Natural Environments',
container='IEEE Transactions on Pattern Analysis and '
'Machine Intelligence',
year='2014',
homepage='http://vision.imar.ro/human3.6m/description.php',
),
keypoint_info={
0:
dict(name='root', id=0, color=[51, 153, 255], type='lower', swap=''),
1:
dict(
name='right_hip',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
2:
dict(
name='right_knee',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
3:
dict(
name='right_foot',
id=3,
color=[255, 128, 0],
type='lower',
swap='left_foot'),
4:
dict(
name='left_hip',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
5:
dict(
name='left_knee',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
6:
dict(
name='left_foot',
id=6,
color=[0, 255, 0],
type='lower',
swap='right_foot'),
7:
dict(name='spine', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(name='thorax', id=8, color=[51, 153, 255], type='upper', swap=''),
9:
dict(
name='neck_base',
id=9,
color=[51, 153, 255],
type='upper',
swap=''),
10:
dict(name='head', id=10, color=[51, 153, 255], type='upper', swap=''),
11:
dict(
name='left_shoulder',
id=11,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
12:
dict(
name='left_elbow',
id=12,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
13:
dict(
name='left_wrist',
id=13,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
14:
dict(
name='right_shoulder',
id=14,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
15:
dict(
name='right_elbow',
id=15,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
16:
dict(
name='right_wrist',
id=16,
color=[255, 128, 0],
type='upper',
swap='left_wrist')
},
skeleton_info={
0:
dict(link=('root', 'left_hip'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_hip', 'left_knee'), id=1, color=[0, 255, 0]),
2:
dict(link=('left_knee', 'left_foot'), id=2, color=[0, 255, 0]),
3:
dict(link=('root', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('right_hip', 'right_knee'), id=4, color=[255, 128, 0]),
5:
dict(link=('right_knee', 'right_foot'), id=5, color=[255, 128, 0]),
6:
dict(link=('root', 'spine'), id=6, color=[51, 153, 255]),
7:
dict(link=('spine', 'thorax'), id=7, color=[51, 153, 255]),
8:
dict(link=('thorax', 'neck_base'), id=8, color=[51, 153, 255]),
9:
dict(link=('neck_base', 'head'), id=9, color=[51, 153, 255]),
10:
dict(link=('thorax', 'left_shoulder'), id=10, color=[0, 255, 0]),
11:
dict(link=('left_shoulder', 'left_elbow'), id=11, color=[0, 255, 0]),
12:
dict(link=('left_elbow', 'left_wrist'), id=12, color=[0, 255, 0]),
13:
dict(link=('thorax', 'right_shoulder'), id=13, color=[255, 128, 0]),
14:
dict(
link=('right_shoulder', 'right_elbow'), id=14, color=[255, 128,
0]),
15:
dict(link=('right_elbow', 'right_wrist'), id=15, color=[255, 128, 0])
},
joint_weights=[1.] * 17,
sigmas=[],
stats_info=dict(bbox_center=(528., 427.), bbox_scale=400.))
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/halpe.py
Vendored
+1157
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third_party/hamer/third-party/ViTPose/configs/_base_/datasets/horse10.py
Vendored
+201
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dataset_info = dict(
dataset_name='horse10',
paper_info=dict(
author='Mathis, Alexander and Biasi, Thomas and '
'Schneider, Steffen and '
'Yuksekgonul, Mert and Rogers, Byron and '
'Bethge, Matthias and '
'Mathis, Mackenzie W',
title='Pretraining boosts out-of-domain robustness '
'for pose estimation',
container='Proceedings of the IEEE/CVF Winter Conference on '
'Applications of Computer Vision',
year='2021',
homepage='http://www.mackenziemathislab.org/horse10',
),
keypoint_info={
0:
dict(name='Nose', id=0, color=[255, 153, 255], type='upper', swap=''),
1:
dict(name='Eye', id=1, color=[255, 153, 255], type='upper', swap=''),
2:
dict(
name='Nearknee',
id=2,
color=[255, 102, 255],
type='upper',
swap=''),
3:
dict(
name='Nearfrontfetlock',
id=3,
color=[255, 102, 255],
type='upper',
swap=''),
4:
dict(
name='Nearfrontfoot',
id=4,
color=[255, 102, 255],
type='upper',
swap=''),
5:
dict(
name='Offknee', id=5, color=[255, 102, 255], type='upper',
swap=''),
6:
dict(
name='Offfrontfetlock',
id=6,
color=[255, 102, 255],
type='upper',
swap=''),
7:
dict(
name='Offfrontfoot',
id=7,
color=[255, 102, 255],
type='upper',
swap=''),
8:
dict(
name='Shoulder',
id=8,
color=[255, 153, 255],
type='upper',
swap=''),
9:
dict(
name='Midshoulder',
id=9,
color=[255, 153, 255],
type='upper',
swap=''),
10:
dict(
name='Elbow', id=10, color=[255, 153, 255], type='upper', swap=''),
11:
dict(
name='Girth', id=11, color=[255, 153, 255], type='upper', swap=''),
12:
dict(
name='Wither', id=12, color=[255, 153, 255], type='upper',
swap=''),
13:
dict(
name='Nearhindhock',
id=13,
color=[255, 51, 255],
type='lower',
swap=''),
14:
dict(
name='Nearhindfetlock',
id=14,
color=[255, 51, 255],
type='lower',
swap=''),
15:
dict(
name='Nearhindfoot',
id=15,
color=[255, 51, 255],
type='lower',
swap=''),
16:
dict(name='Hip', id=16, color=[255, 153, 255], type='lower', swap=''),
17:
dict(
name='Stifle', id=17, color=[255, 153, 255], type='lower',
swap=''),
18:
dict(
name='Offhindhock',
id=18,
color=[255, 51, 255],
type='lower',
swap=''),
19:
dict(
name='Offhindfetlock',
id=19,
color=[255, 51, 255],
type='lower',
swap=''),
20:
dict(
name='Offhindfoot',
id=20,
color=[255, 51, 255],
type='lower',
swap=''),
21:
dict(
name='Ischium',
id=21,
color=[255, 153, 255],
type='lower',
swap='')
},
skeleton_info={
0:
dict(link=('Nose', 'Eye'), id=0, color=[255, 153, 255]),
1:
dict(link=('Eye', 'Wither'), id=1, color=[255, 153, 255]),
2:
dict(link=('Wither', 'Hip'), id=2, color=[255, 153, 255]),
3:
dict(link=('Hip', 'Ischium'), id=3, color=[255, 153, 255]),
4:
dict(link=('Ischium', 'Stifle'), id=4, color=[255, 153, 255]),
5:
dict(link=('Stifle', 'Girth'), id=5, color=[255, 153, 255]),
6:
dict(link=('Girth', 'Elbow'), id=6, color=[255, 153, 255]),
7:
dict(link=('Elbow', 'Shoulder'), id=7, color=[255, 153, 255]),
8:
dict(link=('Shoulder', 'Midshoulder'), id=8, color=[255, 153, 255]),
9:
dict(link=('Midshoulder', 'Wither'), id=9, color=[255, 153, 255]),
10:
dict(
link=('Nearknee', 'Nearfrontfetlock'),
id=10,
color=[255, 102, 255]),
11:
dict(
link=('Nearfrontfetlock', 'Nearfrontfoot'),
id=11,
color=[255, 102, 255]),
12:
dict(
link=('Offknee', 'Offfrontfetlock'), id=12, color=[255, 102, 255]),
13:
dict(
link=('Offfrontfetlock', 'Offfrontfoot'),
id=13,
color=[255, 102, 255]),
14:
dict(
link=('Nearhindhock', 'Nearhindfetlock'),
id=14,
color=[255, 51, 255]),
15:
dict(
link=('Nearhindfetlock', 'Nearhindfoot'),
id=15,
color=[255, 51, 255]),
16:
dict(
link=('Offhindhock', 'Offhindfetlock'),
id=16,
color=[255, 51, 255]),
17:
dict(
link=('Offhindfetlock', 'Offhindfoot'),
id=17,
color=[255, 51, 255])
},
joint_weights=[1.] * 22,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/interhand2d.py
Vendored
+142
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@@ -0,0 +1,142 @@
dataset_info = dict(
dataset_name='interhand2d',
paper_info=dict(
author='Moon, Gyeongsik and Yu, Shoou-I and Wen, He and '
'Shiratori, Takaaki and Lee, Kyoung Mu',
title='InterHand2.6M: A dataset and baseline for 3D '
'interacting hand pose estimation from a single RGB image',
container='arXiv',
year='2020',
homepage='https://mks0601.github.io/InterHand2.6M/',
),
keypoint_info={
0:
dict(name='thumb4', id=0, color=[255, 128, 0], type='', swap=''),
1:
dict(name='thumb3', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb1', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(
name='forefinger4', id=4, color=[255, 153, 255], type='', swap=''),
5:
dict(
name='forefinger3', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger1', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='middle_finger4',
id=8,
color=[102, 178, 255],
type='',
swap=''),
9:
dict(
name='middle_finger3',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger1',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='ring_finger4', id=12, color=[255, 51, 51], type='', swap=''),
13:
dict(
name='ring_finger3', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger1', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(name='pinky_finger4', id=16, color=[0, 255, 0], type='', swap=''),
17:
dict(name='pinky_finger3', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger1', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='wrist', id=20, color=[255, 255, 255], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/interhand3d.py
Vendored
+487
View File
@@ -0,0 +1,487 @@
dataset_info = dict(
dataset_name='interhand3d',
paper_info=dict(
author='Moon, Gyeongsik and Yu, Shoou-I and Wen, He and '
'Shiratori, Takaaki and Lee, Kyoung Mu',
title='InterHand2.6M: A dataset and baseline for 3D '
'interacting hand pose estimation from a single RGB image',
container='arXiv',
year='2020',
homepage='https://mks0601.github.io/InterHand2.6M/',
),
keypoint_info={
0:
dict(
name='right_thumb4',
id=0,
color=[255, 128, 0],
type='',
swap='left_thumb4'),
1:
dict(
name='right_thumb3',
id=1,
color=[255, 128, 0],
type='',
swap='left_thumb3'),
2:
dict(
name='right_thumb2',
id=2,
color=[255, 128, 0],
type='',
swap='left_thumb2'),
3:
dict(
name='right_thumb1',
id=3,
color=[255, 128, 0],
type='',
swap='left_thumb1'),
4:
dict(
name='right_forefinger4',
id=4,
color=[255, 153, 255],
type='',
swap='left_forefinger4'),
5:
dict(
name='right_forefinger3',
id=5,
color=[255, 153, 255],
type='',
swap='left_forefinger3'),
6:
dict(
name='right_forefinger2',
id=6,
color=[255, 153, 255],
type='',
swap='left_forefinger2'),
7:
dict(
name='right_forefinger1',
id=7,
color=[255, 153, 255],
type='',
swap='left_forefinger1'),
8:
dict(
name='right_middle_finger4',
id=8,
color=[102, 178, 255],
type='',
swap='left_middle_finger4'),
9:
dict(
name='right_middle_finger3',
id=9,
color=[102, 178, 255],
type='',
swap='left_middle_finger3'),
10:
dict(
name='right_middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap='left_middle_finger2'),
11:
dict(
name='right_middle_finger1',
id=11,
color=[102, 178, 255],
type='',
swap='left_middle_finger1'),
12:
dict(
name='right_ring_finger4',
id=12,
color=[255, 51, 51],
type='',
swap='left_ring_finger4'),
13:
dict(
name='right_ring_finger3',
id=13,
color=[255, 51, 51],
type='',
swap='left_ring_finger3'),
14:
dict(
name='right_ring_finger2',
id=14,
color=[255, 51, 51],
type='',
swap='left_ring_finger2'),
15:
dict(
name='right_ring_finger1',
id=15,
color=[255, 51, 51],
type='',
swap='left_ring_finger1'),
16:
dict(
name='right_pinky_finger4',
id=16,
color=[0, 255, 0],
type='',
swap='left_pinky_finger4'),
17:
dict(
name='right_pinky_finger3',
id=17,
color=[0, 255, 0],
type='',
swap='left_pinky_finger3'),
18:
dict(
name='right_pinky_finger2',
id=18,
color=[0, 255, 0],
type='',
swap='left_pinky_finger2'),
19:
dict(
name='right_pinky_finger1',
id=19,
color=[0, 255, 0],
type='',
swap='left_pinky_finger1'),
20:
dict(
name='right_wrist',
id=20,
color=[255, 255, 255],
type='',
swap='left_wrist'),
21:
dict(
name='left_thumb4',
id=21,
color=[255, 128, 0],
type='',
swap='right_thumb4'),
22:
dict(
name='left_thumb3',
id=22,
color=[255, 128, 0],
type='',
swap='right_thumb3'),
23:
dict(
name='left_thumb2',
id=23,
color=[255, 128, 0],
type='',
swap='right_thumb2'),
24:
dict(
name='left_thumb1',
id=24,
color=[255, 128, 0],
type='',
swap='right_thumb1'),
25:
dict(
name='left_forefinger4',
id=25,
color=[255, 153, 255],
type='',
swap='right_forefinger4'),
26:
dict(
name='left_forefinger3',
id=26,
color=[255, 153, 255],
type='',
swap='right_forefinger3'),
27:
dict(
name='left_forefinger2',
id=27,
color=[255, 153, 255],
type='',
swap='right_forefinger2'),
28:
dict(
name='left_forefinger1',
id=28,
color=[255, 153, 255],
type='',
swap='right_forefinger1'),
29:
dict(
name='left_middle_finger4',
id=29,
color=[102, 178, 255],
type='',
swap='right_middle_finger4'),
30:
dict(
name='left_middle_finger3',
id=30,
color=[102, 178, 255],
type='',
swap='right_middle_finger3'),
31:
dict(
name='left_middle_finger2',
id=31,
color=[102, 178, 255],
type='',
swap='right_middle_finger2'),
32:
dict(
name='left_middle_finger1',
id=32,
color=[102, 178, 255],
type='',
swap='right_middle_finger1'),
33:
dict(
name='left_ring_finger4',
id=33,
color=[255, 51, 51],
type='',
swap='right_ring_finger4'),
34:
dict(
name='left_ring_finger3',
id=34,
color=[255, 51, 51],
type='',
swap='right_ring_finger3'),
35:
dict(
name='left_ring_finger2',
id=35,
color=[255, 51, 51],
type='',
swap='right_ring_finger2'),
36:
dict(
name='left_ring_finger1',
id=36,
color=[255, 51, 51],
type='',
swap='right_ring_finger1'),
37:
dict(
name='left_pinky_finger4',
id=37,
color=[0, 255, 0],
type='',
swap='right_pinky_finger4'),
38:
dict(
name='left_pinky_finger3',
id=38,
color=[0, 255, 0],
type='',
swap='right_pinky_finger3'),
39:
dict(
name='left_pinky_finger2',
id=39,
color=[0, 255, 0],
type='',
swap='right_pinky_finger2'),
40:
dict(
name='left_pinky_finger1',
id=40,
color=[0, 255, 0],
type='',
swap='right_pinky_finger1'),
41:
dict(
name='left_wrist',
id=41,
color=[255, 255, 255],
type='',
swap='right_wrist'),
},
skeleton_info={
0:
dict(link=('right_wrist', 'right_thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_thumb1', 'right_thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_thumb2', 'right_thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_thumb3', 'right_thumb4'), id=3, color=[255, 128, 0]),
4:
dict(
link=('right_wrist', 'right_forefinger1'),
id=4,
color=[255, 153, 255]),
5:
dict(
link=('right_forefinger1', 'right_forefinger2'),
id=5,
color=[255, 153, 255]),
6:
dict(
link=('right_forefinger2', 'right_forefinger3'),
id=6,
color=[255, 153, 255]),
7:
dict(
link=('right_forefinger3', 'right_forefinger4'),
id=7,
color=[255, 153, 255]),
8:
dict(
link=('right_wrist', 'right_middle_finger1'),
id=8,
color=[102, 178, 255]),
9:
dict(
link=('right_middle_finger1', 'right_middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('right_middle_finger2', 'right_middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('right_middle_finger3', 'right_middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(
link=('right_wrist', 'right_ring_finger1'),
id=12,
color=[255, 51, 51]),
13:
dict(
link=('right_ring_finger1', 'right_ring_finger2'),
id=13,
color=[255, 51, 51]),
14:
dict(
link=('right_ring_finger2', 'right_ring_finger3'),
id=14,
color=[255, 51, 51]),
15:
dict(
link=('right_ring_finger3', 'right_ring_finger4'),
id=15,
color=[255, 51, 51]),
16:
dict(
link=('right_wrist', 'right_pinky_finger1'),
id=16,
color=[0, 255, 0]),
17:
dict(
link=('right_pinky_finger1', 'right_pinky_finger2'),
id=17,
color=[0, 255, 0]),
18:
dict(
link=('right_pinky_finger2', 'right_pinky_finger3'),
id=18,
color=[0, 255, 0]),
19:
dict(
link=('right_pinky_finger3', 'right_pinky_finger4'),
id=19,
color=[0, 255, 0]),
20:
dict(link=('left_wrist', 'left_thumb1'), id=20, color=[255, 128, 0]),
21:
dict(link=('left_thumb1', 'left_thumb2'), id=21, color=[255, 128, 0]),
22:
dict(link=('left_thumb2', 'left_thumb3'), id=22, color=[255, 128, 0]),
23:
dict(link=('left_thumb3', 'left_thumb4'), id=23, color=[255, 128, 0]),
24:
dict(
link=('left_wrist', 'left_forefinger1'),
id=24,
color=[255, 153, 255]),
25:
dict(
link=('left_forefinger1', 'left_forefinger2'),
id=25,
color=[255, 153, 255]),
26:
dict(
link=('left_forefinger2', 'left_forefinger3'),
id=26,
color=[255, 153, 255]),
27:
dict(
link=('left_forefinger3', 'left_forefinger4'),
id=27,
color=[255, 153, 255]),
28:
dict(
link=('left_wrist', 'left_middle_finger1'),
id=28,
color=[102, 178, 255]),
29:
dict(
link=('left_middle_finger1', 'left_middle_finger2'),
id=29,
color=[102, 178, 255]),
30:
dict(
link=('left_middle_finger2', 'left_middle_finger3'),
id=30,
color=[102, 178, 255]),
31:
dict(
link=('left_middle_finger3', 'left_middle_finger4'),
id=31,
color=[102, 178, 255]),
32:
dict(
link=('left_wrist', 'left_ring_finger1'),
id=32,
color=[255, 51, 51]),
33:
dict(
link=('left_ring_finger1', 'left_ring_finger2'),
id=33,
color=[255, 51, 51]),
34:
dict(
link=('left_ring_finger2', 'left_ring_finger3'),
id=34,
color=[255, 51, 51]),
35:
dict(
link=('left_ring_finger3', 'left_ring_finger4'),
id=35,
color=[255, 51, 51]),
36:
dict(
link=('left_wrist', 'left_pinky_finger1'),
id=36,
color=[0, 255, 0]),
37:
dict(
link=('left_pinky_finger1', 'left_pinky_finger2'),
id=37,
color=[0, 255, 0]),
38:
dict(
link=('left_pinky_finger2', 'left_pinky_finger3'),
id=38,
color=[0, 255, 0]),
39:
dict(
link=('left_pinky_finger3', 'left_pinky_finger4'),
id=39,
color=[0, 255, 0]),
},
joint_weights=[1.] * 42,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/jhmdb.py
Vendored
+129
View File
@@ -0,0 +1,129 @@
dataset_info = dict(
dataset_name='jhmdb',
paper_info=dict(
author='H. Jhuang and J. Gall and S. Zuffi and '
'C. Schmid and M. J. Black',
title='Towards understanding action recognition',
container='International Conf. on Computer Vision (ICCV)',
year='2013',
homepage='http://jhmdb.is.tue.mpg.de/dataset',
),
keypoint_info={
0:
dict(name='neck', id=0, color=[255, 128, 0], type='upper', swap=''),
1:
dict(name='belly', id=1, color=[255, 128, 0], type='upper', swap=''),
2:
dict(name='head', id=2, color=[255, 128, 0], type='upper', swap=''),
3:
dict(
name='right_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='left_shoulder'),
4:
dict(
name='left_shoulder',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
5:
dict(
name='right_hip',
id=5,
color=[0, 255, 0],
type='lower',
swap='left_hip'),
6:
dict(
name='left_hip',
id=6,
color=[51, 153, 255],
type='lower',
swap='right_hip'),
7:
dict(
name='right_elbow',
id=7,
color=[51, 153, 255],
type='upper',
swap='left_elbow'),
8:
dict(
name='left_elbow',
id=8,
color=[51, 153, 255],
type='upper',
swap='right_elbow'),
9:
dict(
name='right_knee',
id=9,
color=[51, 153, 255],
type='lower',
swap='left_knee'),
10:
dict(
name='left_knee',
id=10,
color=[255, 128, 0],
type='lower',
swap='right_knee'),
11:
dict(
name='right_wrist',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
12:
dict(
name='left_wrist',
id=12,
color=[255, 128, 0],
type='upper',
swap='right_wrist'),
13:
dict(
name='right_ankle',
id=13,
color=[0, 255, 0],
type='lower',
swap='left_ankle'),
14:
dict(
name='left_ankle',
id=14,
color=[0, 255, 0],
type='lower',
swap='right_ankle')
},
skeleton_info={
0: dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1: dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2: dict(link=('right_hip', 'belly'), id=2, color=[255, 128, 0]),
3: dict(link=('belly', 'left_hip'), id=3, color=[0, 255, 0]),
4: dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
5: dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
6: dict(link=('belly', 'neck'), id=6, color=[51, 153, 255]),
7: dict(link=('neck', 'head'), id=7, color=[51, 153, 255]),
8: dict(link=('neck', 'right_shoulder'), id=8, color=[255, 128, 0]),
9: dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('right_elbow', 'right_wrist'), id=10, color=[255, 128, 0]),
11: dict(link=('neck', 'left_shoulder'), id=11, color=[0, 255, 0]),
12:
dict(link=('left_shoulder', 'left_elbow'), id=12, color=[0, 255, 0]),
13: dict(link=('left_elbow', 'left_wrist'), id=13, color=[0, 255, 0])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.2, 1.2, 1.5, 1.5, 1.5, 1.5
],
# Adapted from COCO dataset.
sigmas=[
0.025, 0.107, 0.025, 0.079, 0.079, 0.107, 0.107, 0.072, 0.072, 0.087,
0.087, 0.062, 0.062, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/locust.py
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dataset_info = dict(
dataset_name='locust',
paper_info=dict(
author='Graving, Jacob M and Chae, Daniel and Naik, Hemal and '
'Li, Liang and Koger, Benjamin and Costelloe, Blair R and '
'Couzin, Iain D',
title='DeepPoseKit, a software toolkit for fast and robust '
'animal pose estimation using deep learning',
container='Elife',
year='2019',
homepage='https://github.com/jgraving/DeepPoseKit-Data',
),
keypoint_info={
0:
dict(name='head', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='neck', id=1, color=[255, 255, 255], type='', swap=''),
2:
dict(name='thorax', id=2, color=[255, 255, 255], type='', swap=''),
3:
dict(name='abdomen1', id=3, color=[255, 255, 255], type='', swap=''),
4:
dict(name='abdomen2', id=4, color=[255, 255, 255], type='', swap=''),
5:
dict(
name='anttipL',
id=5,
color=[255, 255, 255],
type='',
swap='anttipR'),
6:
dict(
name='antbaseL',
id=6,
color=[255, 255, 255],
type='',
swap='antbaseR'),
7:
dict(name='eyeL', id=7, color=[255, 255, 255], type='', swap='eyeR'),
8:
dict(
name='forelegL1',
id=8,
color=[255, 255, 255],
type='',
swap='forelegR1'),
9:
dict(
name='forelegL2',
id=9,
color=[255, 255, 255],
type='',
swap='forelegR2'),
10:
dict(
name='forelegL3',
id=10,
color=[255, 255, 255],
type='',
swap='forelegR3'),
11:
dict(
name='forelegL4',
id=11,
color=[255, 255, 255],
type='',
swap='forelegR4'),
12:
dict(
name='midlegL1',
id=12,
color=[255, 255, 255],
type='',
swap='midlegR1'),
13:
dict(
name='midlegL2',
id=13,
color=[255, 255, 255],
type='',
swap='midlegR2'),
14:
dict(
name='midlegL3',
id=14,
color=[255, 255, 255],
type='',
swap='midlegR3'),
15:
dict(
name='midlegL4',
id=15,
color=[255, 255, 255],
type='',
swap='midlegR4'),
16:
dict(
name='hindlegL1',
id=16,
color=[255, 255, 255],
type='',
swap='hindlegR1'),
17:
dict(
name='hindlegL2',
id=17,
color=[255, 255, 255],
type='',
swap='hindlegR2'),
18:
dict(
name='hindlegL3',
id=18,
color=[255, 255, 255],
type='',
swap='hindlegR3'),
19:
dict(
name='hindlegL4',
id=19,
color=[255, 255, 255],
type='',
swap='hindlegR4'),
20:
dict(
name='anttipR',
id=20,
color=[255, 255, 255],
type='',
swap='anttipL'),
21:
dict(
name='antbaseR',
id=21,
color=[255, 255, 255],
type='',
swap='antbaseL'),
22:
dict(name='eyeR', id=22, color=[255, 255, 255], type='', swap='eyeL'),
23:
dict(
name='forelegR1',
id=23,
color=[255, 255, 255],
type='',
swap='forelegL1'),
24:
dict(
name='forelegR2',
id=24,
color=[255, 255, 255],
type='',
swap='forelegL2'),
25:
dict(
name='forelegR3',
id=25,
color=[255, 255, 255],
type='',
swap='forelegL3'),
26:
dict(
name='forelegR4',
id=26,
color=[255, 255, 255],
type='',
swap='forelegL4'),
27:
dict(
name='midlegR1',
id=27,
color=[255, 255, 255],
type='',
swap='midlegL1'),
28:
dict(
name='midlegR2',
id=28,
color=[255, 255, 255],
type='',
swap='midlegL2'),
29:
dict(
name='midlegR3',
id=29,
color=[255, 255, 255],
type='',
swap='midlegL3'),
30:
dict(
name='midlegR4',
id=30,
color=[255, 255, 255],
type='',
swap='midlegL4'),
31:
dict(
name='hindlegR1',
id=31,
color=[255, 255, 255],
type='',
swap='hindlegL1'),
32:
dict(
name='hindlegR2',
id=32,
color=[255, 255, 255],
type='',
swap='hindlegL2'),
33:
dict(
name='hindlegR3',
id=33,
color=[255, 255, 255],
type='',
swap='hindlegL3'),
34:
dict(
name='hindlegR4',
id=34,
color=[255, 255, 255],
type='',
swap='hindlegL4')
},
skeleton_info={
0: dict(link=('neck', 'head'), id=0, color=[255, 255, 255]),
1: dict(link=('thorax', 'neck'), id=1, color=[255, 255, 255]),
2: dict(link=('abdomen1', 'thorax'), id=2, color=[255, 255, 255]),
3: dict(link=('abdomen2', 'abdomen1'), id=3, color=[255, 255, 255]),
4: dict(link=('antbaseL', 'anttipL'), id=4, color=[255, 255, 255]),
5: dict(link=('eyeL', 'antbaseL'), id=5, color=[255, 255, 255]),
6: dict(link=('forelegL2', 'forelegL1'), id=6, color=[255, 255, 255]),
7: dict(link=('forelegL3', 'forelegL2'), id=7, color=[255, 255, 255]),
8: dict(link=('forelegL4', 'forelegL3'), id=8, color=[255, 255, 255]),
9: dict(link=('midlegL2', 'midlegL1'), id=9, color=[255, 255, 255]),
10: dict(link=('midlegL3', 'midlegL2'), id=10, color=[255, 255, 255]),
11: dict(link=('midlegL4', 'midlegL3'), id=11, color=[255, 255, 255]),
12:
dict(link=('hindlegL2', 'hindlegL1'), id=12, color=[255, 255, 255]),
13:
dict(link=('hindlegL3', 'hindlegL2'), id=13, color=[255, 255, 255]),
14:
dict(link=('hindlegL4', 'hindlegL3'), id=14, color=[255, 255, 255]),
15: dict(link=('antbaseR', 'anttipR'), id=15, color=[255, 255, 255]),
16: dict(link=('eyeR', 'antbaseR'), id=16, color=[255, 255, 255]),
17:
dict(link=('forelegR2', 'forelegR1'), id=17, color=[255, 255, 255]),
18:
dict(link=('forelegR3', 'forelegR2'), id=18, color=[255, 255, 255]),
19:
dict(link=('forelegR4', 'forelegR3'), id=19, color=[255, 255, 255]),
20: dict(link=('midlegR2', 'midlegR1'), id=20, color=[255, 255, 255]),
21: dict(link=('midlegR3', 'midlegR2'), id=21, color=[255, 255, 255]),
22: dict(link=('midlegR4', 'midlegR3'), id=22, color=[255, 255, 255]),
23:
dict(link=('hindlegR2', 'hindlegR1'), id=23, color=[255, 255, 255]),
24:
dict(link=('hindlegR3', 'hindlegR2'), id=24, color=[255, 255, 255]),
25:
dict(link=('hindlegR4', 'hindlegR3'), id=25, color=[255, 255, 255])
},
joint_weights=[1.] * 35,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/macaque.py
Vendored
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dataset_info = dict(
dataset_name='macaque',
paper_info=dict(
author='Labuguen, Rollyn and Matsumoto, Jumpei and '
'Negrete, Salvador and Nishimaru, Hiroshi and '
'Nishijo, Hisao and Takada, Masahiko and '
'Go, Yasuhiro and Inoue, Ken-ichi and Shibata, Tomohiro',
title='MacaquePose: A novel "in the wild" macaque monkey pose dataset '
'for markerless motion capture',
container='bioRxiv',
year='2020',
homepage='http://www.pri.kyoto-u.ac.jp/datasets/'
'macaquepose/index.html',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/mhp.py
Vendored
+156
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dataset_info = dict(
dataset_name='mhp',
paper_info=dict(
author='Zhao, Jian and Li, Jianshu and Cheng, Yu and '
'Sim, Terence and Yan, Shuicheng and Feng, Jiashi',
title='Understanding humans in crowded scenes: '
'Deep nested adversarial learning and a '
'new benchmark for multi-human parsing',
container='Proceedings of the 26th ACM '
'international conference on Multimedia',
year='2018',
homepage='https://lv-mhp.github.io/dataset',
),
keypoint_info={
0:
dict(
name='right_ankle',
id=0,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
1:
dict(
name='right_knee',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
2:
dict(
name='right_hip',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
3:
dict(
name='left_hip',
id=3,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
4:
dict(
name='left_knee',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
5:
dict(
name='left_ankle',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
6:
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''),
7:
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='upper_neck',
id=8,
color=[51, 153, 255],
type='upper',
swap=''),
9:
dict(
name='head_top', id=9, color=[51, 153, 255], type='upper',
swap=''),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='right_elbow',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
12:
dict(
name='right_shoulder',
id=12,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
13:
dict(
name='left_shoulder',
id=13,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
14:
dict(
name='left_elbow',
id=14,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
15:
dict(
name='left_wrist',
id=15,
color=[0, 255, 0],
type='upper',
swap='right_wrist')
},
skeleton_info={
0:
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]),
3:
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
5:
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
6:
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]),
7:
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]),
8:
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]),
9:
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]),
10:
dict(
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128,
0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]),
14:
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0])
},
joint_weights=[
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5
],
# Adapted from COCO dataset.
sigmas=[
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026,
0.062, 0.072, 0.179, 0.179, 0.072, 0.062
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/mpi_inf_3dhp.py
Vendored
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dataset_info = dict(
dataset_name='mpi_inf_3dhp',
paper_info=dict(
author='ehta, Dushyant and Rhodin, Helge and Casas, Dan and '
'Fua, Pascal and Sotnychenko, Oleksandr and Xu, Weipeng and '
'Theobalt, Christian',
title='Monocular 3D Human Pose Estimation In The Wild Using Improved '
'CNN Supervision',
container='2017 international conference on 3D vision (3DV)',
year='2017',
homepage='http://gvv.mpi-inf.mpg.de/3dhp-dataset',
),
keypoint_info={
0:
dict(
name='head_top', id=0, color=[51, 153, 255], type='upper',
swap=''),
1:
dict(name='neck', id=1, color=[51, 153, 255], type='upper', swap=''),
2:
dict(
name='right_shoulder',
id=2,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
3:
dict(
name='right_elbow',
id=3,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
4:
dict(
name='right_wrist',
id=4,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='left_elbow',
id=6,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
7:
dict(
name='left_wrist',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
8:
dict(
name='right_hip',
id=8,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
9:
dict(
name='right_knee',
id=9,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
10:
dict(
name='right_ankle',
id=10,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='left_knee',
id=12,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
13:
dict(
name='left_ankle',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
14:
dict(name='root', id=14, color=[51, 153, 255], type='lower', swap=''),
15:
dict(name='spine', id=15, color=[51, 153, 255], type='upper', swap=''),
16:
dict(name='head', id=16, color=[51, 153, 255], type='upper', swap='')
},
skeleton_info={
0: dict(link=('neck', 'right_shoulder'), id=0, color=[255, 128, 0]),
1: dict(
link=('right_shoulder', 'right_elbow'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_elbow', 'right_wrist'), id=2, color=[255, 128, 0]),
3: dict(link=('neck', 'left_shoulder'), id=3, color=[0, 255, 0]),
4: dict(link=('left_shoulder', 'left_elbow'), id=4, color=[0, 255, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6: dict(link=('root', 'right_hip'), id=6, color=[255, 128, 0]),
7: dict(link=('right_hip', 'right_knee'), id=7, color=[255, 128, 0]),
8: dict(link=('right_knee', 'right_ankle'), id=8, color=[255, 128, 0]),
9: dict(link=('root', 'left_hip'), id=9, color=[0, 255, 0]),
10: dict(link=('left_hip', 'left_knee'), id=10, color=[0, 255, 0]),
11: dict(link=('left_knee', 'left_ankle'), id=11, color=[0, 255, 0]),
12: dict(link=('head_top', 'head'), id=12, color=[51, 153, 255]),
13: dict(link=('head', 'neck'), id=13, color=[51, 153, 255]),
14: dict(link=('neck', 'spine'), id=14, color=[51, 153, 255]),
15: dict(link=('spine', 'root'), id=15, color=[51, 153, 255])
},
joint_weights=[1.] * 17,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/mpii.py
Vendored
+155
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dataset_info = dict(
dataset_name='mpii',
paper_info=dict(
author='Mykhaylo Andriluka and Leonid Pishchulin and '
'Peter Gehler and Schiele, Bernt',
title='2D Human Pose Estimation: New Benchmark and '
'State of the Art Analysis',
container='IEEE Conference on Computer Vision and '
'Pattern Recognition (CVPR)',
year='2014',
homepage='http://human-pose.mpi-inf.mpg.de/',
),
keypoint_info={
0:
dict(
name='right_ankle',
id=0,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
1:
dict(
name='right_knee',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
2:
dict(
name='right_hip',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
3:
dict(
name='left_hip',
id=3,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
4:
dict(
name='left_knee',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
5:
dict(
name='left_ankle',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
6:
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''),
7:
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='upper_neck',
id=8,
color=[51, 153, 255],
type='upper',
swap=''),
9:
dict(
name='head_top', id=9, color=[51, 153, 255], type='upper',
swap=''),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='right_elbow',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
12:
dict(
name='right_shoulder',
id=12,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
13:
dict(
name='left_shoulder',
id=13,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
14:
dict(
name='left_elbow',
id=14,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
15:
dict(
name='left_wrist',
id=15,
color=[0, 255, 0],
type='upper',
swap='right_wrist')
},
skeleton_info={
0:
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]),
3:
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
5:
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
6:
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]),
7:
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]),
8:
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]),
9:
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]),
10:
dict(
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128,
0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]),
14:
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0])
},
joint_weights=[
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5
],
# Adapted from COCO dataset.
sigmas=[
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026,
0.062, 0.072, 0.179, 0.179, 0.072, 0.062
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/mpii_info.py
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+155
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mpii_info = dict(
dataset_name='mpii',
paper_info=dict(
author='Mykhaylo Andriluka and Leonid Pishchulin and '
'Peter Gehler and Schiele, Bernt',
title='2D Human Pose Estimation: New Benchmark and '
'State of the Art Analysis',
container='IEEE Conference on Computer Vision and '
'Pattern Recognition (CVPR)',
year='2014',
homepage='http://human-pose.mpi-inf.mpg.de/',
),
keypoint_info={
0:
dict(
name='right_ankle',
id=0,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
1:
dict(
name='right_knee',
id=1,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
2:
dict(
name='right_hip',
id=2,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
3:
dict(
name='left_hip',
id=3,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
4:
dict(
name='left_knee',
id=4,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
5:
dict(
name='left_ankle',
id=5,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
6:
dict(name='pelvis', id=6, color=[51, 153, 255], type='lower', swap=''),
7:
dict(name='thorax', id=7, color=[51, 153, 255], type='upper', swap=''),
8:
dict(
name='upper_neck',
id=8,
color=[51, 153, 255],
type='upper',
swap=''),
9:
dict(
name='head_top', id=9, color=[51, 153, 255], type='upper',
swap=''),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='right_elbow',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
12:
dict(
name='right_shoulder',
id=12,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
13:
dict(
name='left_shoulder',
id=13,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
14:
dict(
name='left_elbow',
id=14,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
15:
dict(
name='left_wrist',
id=15,
color=[0, 255, 0],
type='upper',
swap='right_wrist')
},
skeleton_info={
0:
dict(link=('right_ankle', 'right_knee'), id=0, color=[255, 128, 0]),
1:
dict(link=('right_knee', 'right_hip'), id=1, color=[255, 128, 0]),
2:
dict(link=('right_hip', 'pelvis'), id=2, color=[255, 128, 0]),
3:
dict(link=('pelvis', 'left_hip'), id=3, color=[0, 255, 0]),
4:
dict(link=('left_hip', 'left_knee'), id=4, color=[0, 255, 0]),
5:
dict(link=('left_knee', 'left_ankle'), id=5, color=[0, 255, 0]),
6:
dict(link=('pelvis', 'thorax'), id=6, color=[51, 153, 255]),
7:
dict(link=('thorax', 'upper_neck'), id=7, color=[51, 153, 255]),
8:
dict(link=('upper_neck', 'head_top'), id=8, color=[51, 153, 255]),
9:
dict(link=('upper_neck', 'right_shoulder'), id=9, color=[255, 128, 0]),
10:
dict(
link=('right_shoulder', 'right_elbow'), id=10, color=[255, 128,
0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('upper_neck', 'left_shoulder'), id=12, color=[0, 255, 0]),
13:
dict(link=('left_shoulder', 'left_elbow'), id=13, color=[0, 255, 0]),
14:
dict(link=('left_elbow', 'left_wrist'), id=14, color=[0, 255, 0])
},
joint_weights=[
1.5, 1.2, 1., 1., 1.2, 1.5, 1., 1., 1., 1., 1.5, 1.2, 1., 1., 1.2, 1.5
],
# Adapted from COCO dataset.
sigmas=[
0.089, 0.083, 0.107, 0.107, 0.083, 0.089, 0.026, 0.026, 0.026, 0.026,
0.062, 0.072, 0.179, 0.179, 0.072, 0.062
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/mpii_trb.py
Vendored
+380
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dataset_info = dict(
dataset_name='mpii_trb',
paper_info=dict(
author='Duan, Haodong and Lin, Kwan-Yee and Jin, Sheng and '
'Liu, Wentao and Qian, Chen and Ouyang, Wanli',
title='TRB: A Novel Triplet Representation for '
'Understanding 2D Human Body',
container='Proceedings of the IEEE International '
'Conference on Computer Vision',
year='2019',
homepage='https://github.com/kennymckormick/'
'Triplet-Representation-of-human-Body',
),
keypoint_info={
0:
dict(
name='left_shoulder',
id=0,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
1:
dict(
name='right_shoulder',
id=1,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
2:
dict(
name='left_elbow',
id=2,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
3:
dict(
name='right_elbow',
id=3,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
4:
dict(
name='left_wrist',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
5:
dict(
name='right_wrist',
id=5,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
6:
dict(
name='left_hip',
id=6,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
7:
dict(
name='right_hip',
id=7,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
8:
dict(
name='left_knee',
id=8,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
9:
dict(
name='right_knee',
id=9,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
10:
dict(
name='left_ankle',
id=10,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
11:
dict(
name='right_ankle',
id=11,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
12:
dict(name='head', id=12, color=[51, 153, 255], type='upper', swap=''),
13:
dict(name='neck', id=13, color=[51, 153, 255], type='upper', swap=''),
14:
dict(
name='right_neck',
id=14,
color=[255, 255, 255],
type='upper',
swap='left_neck'),
15:
dict(
name='left_neck',
id=15,
color=[255, 255, 255],
type='upper',
swap='right_neck'),
16:
dict(
name='medial_right_shoulder',
id=16,
color=[255, 255, 255],
type='upper',
swap='medial_left_shoulder'),
17:
dict(
name='lateral_right_shoulder',
id=17,
color=[255, 255, 255],
type='upper',
swap='lateral_left_shoulder'),
18:
dict(
name='medial_right_bow',
id=18,
color=[255, 255, 255],
type='upper',
swap='medial_left_bow'),
19:
dict(
name='lateral_right_bow',
id=19,
color=[255, 255, 255],
type='upper',
swap='lateral_left_bow'),
20:
dict(
name='medial_right_wrist',
id=20,
color=[255, 255, 255],
type='upper',
swap='medial_left_wrist'),
21:
dict(
name='lateral_right_wrist',
id=21,
color=[255, 255, 255],
type='upper',
swap='lateral_left_wrist'),
22:
dict(
name='medial_left_shoulder',
id=22,
color=[255, 255, 255],
type='upper',
swap='medial_right_shoulder'),
23:
dict(
name='lateral_left_shoulder',
id=23,
color=[255, 255, 255],
type='upper',
swap='lateral_right_shoulder'),
24:
dict(
name='medial_left_bow',
id=24,
color=[255, 255, 255],
type='upper',
swap='medial_right_bow'),
25:
dict(
name='lateral_left_bow',
id=25,
color=[255, 255, 255],
type='upper',
swap='lateral_right_bow'),
26:
dict(
name='medial_left_wrist',
id=26,
color=[255, 255, 255],
type='upper',
swap='medial_right_wrist'),
27:
dict(
name='lateral_left_wrist',
id=27,
color=[255, 255, 255],
type='upper',
swap='lateral_right_wrist'),
28:
dict(
name='medial_right_hip',
id=28,
color=[255, 255, 255],
type='lower',
swap='medial_left_hip'),
29:
dict(
name='lateral_right_hip',
id=29,
color=[255, 255, 255],
type='lower',
swap='lateral_left_hip'),
30:
dict(
name='medial_right_knee',
id=30,
color=[255, 255, 255],
type='lower',
swap='medial_left_knee'),
31:
dict(
name='lateral_right_knee',
id=31,
color=[255, 255, 255],
type='lower',
swap='lateral_left_knee'),
32:
dict(
name='medial_right_ankle',
id=32,
color=[255, 255, 255],
type='lower',
swap='medial_left_ankle'),
33:
dict(
name='lateral_right_ankle',
id=33,
color=[255, 255, 255],
type='lower',
swap='lateral_left_ankle'),
34:
dict(
name='medial_left_hip',
id=34,
color=[255, 255, 255],
type='lower',
swap='medial_right_hip'),
35:
dict(
name='lateral_left_hip',
id=35,
color=[255, 255, 255],
type='lower',
swap='lateral_right_hip'),
36:
dict(
name='medial_left_knee',
id=36,
color=[255, 255, 255],
type='lower',
swap='medial_right_knee'),
37:
dict(
name='lateral_left_knee',
id=37,
color=[255, 255, 255],
type='lower',
swap='lateral_right_knee'),
38:
dict(
name='medial_left_ankle',
id=38,
color=[255, 255, 255],
type='lower',
swap='medial_right_ankle'),
39:
dict(
name='lateral_left_ankle',
id=39,
color=[255, 255, 255],
type='lower',
swap='lateral_right_ankle'),
},
skeleton_info={
0:
dict(link=('head', 'neck'), id=0, color=[51, 153, 255]),
1:
dict(link=('neck', 'left_shoulder'), id=1, color=[51, 153, 255]),
2:
dict(link=('neck', 'right_shoulder'), id=2, color=[51, 153, 255]),
3:
dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]),
4:
dict(
link=('right_shoulder', 'right_elbow'), id=4, color=[255, 128, 0]),
5:
dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6:
dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]),
7:
dict(link=('left_shoulder', 'left_hip'), id=7, color=[51, 153, 255]),
8:
dict(link=('right_shoulder', 'right_hip'), id=8, color=[51, 153, 255]),
9:
dict(link=('left_hip', 'right_hip'), id=9, color=[51, 153, 255]),
10:
dict(link=('left_hip', 'left_knee'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_hip', 'right_knee'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_knee', 'left_ankle'), id=12, color=[0, 255, 0]),
13:
dict(link=('right_knee', 'right_ankle'), id=13, color=[255, 128, 0]),
14:
dict(link=('right_neck', 'left_neck'), id=14, color=[255, 255, 255]),
15:
dict(
link=('medial_right_shoulder', 'lateral_right_shoulder'),
id=15,
color=[255, 255, 255]),
16:
dict(
link=('medial_right_bow', 'lateral_right_bow'),
id=16,
color=[255, 255, 255]),
17:
dict(
link=('medial_right_wrist', 'lateral_right_wrist'),
id=17,
color=[255, 255, 255]),
18:
dict(
link=('medial_left_shoulder', 'lateral_left_shoulder'),
id=18,
color=[255, 255, 255]),
19:
dict(
link=('medial_left_bow', 'lateral_left_bow'),
id=19,
color=[255, 255, 255]),
20:
dict(
link=('medial_left_wrist', 'lateral_left_wrist'),
id=20,
color=[255, 255, 255]),
21:
dict(
link=('medial_right_hip', 'lateral_right_hip'),
id=21,
color=[255, 255, 255]),
22:
dict(
link=('medial_right_knee', 'lateral_right_knee'),
id=22,
color=[255, 255, 255]),
23:
dict(
link=('medial_right_ankle', 'lateral_right_ankle'),
id=23,
color=[255, 255, 255]),
24:
dict(
link=('medial_left_hip', 'lateral_left_hip'),
id=24,
color=[255, 255, 255]),
25:
dict(
link=('medial_left_knee', 'lateral_left_knee'),
id=25,
color=[255, 255, 255]),
26:
dict(
link=('medial_left_ankle', 'lateral_left_ankle'),
id=26,
color=[255, 255, 255])
},
joint_weights=[1.] * 40,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/ochuman.py
Vendored
+181
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dataset_info = dict(
dataset_name='ochuman',
paper_info=dict(
author='Zhang, Song-Hai and Li, Ruilong and Dong, Xin and '
'Rosin, Paul and Cai, Zixi and Han, Xi and '
'Yang, Dingcheng and Huang, Haozhi and Hu, Shi-Min',
title='Pose2seg: Detection free human instance segmentation',
container='Proceedings of the IEEE conference on computer '
'vision and pattern recognition',
year='2019',
homepage='https://github.com/liruilong940607/OCHumanApi',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='left_eye',
id=1,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
2:
dict(
name='right_eye',
id=2,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
14:
dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
15:
dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
16:
dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
17:
dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
18:
dict(
link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/onehand10k.py
Vendored
+142
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@@ -0,0 +1,142 @@
dataset_info = dict(
dataset_name='onehand10k',
paper_info=dict(
author='Wang, Yangang and Peng, Cong and Liu, Yebin',
title='Mask-pose cascaded cnn for 2d hand pose estimation '
'from single color image',
container='IEEE Transactions on Circuits and Systems '
'for Video Technology',
year='2018',
homepage='https://www.yangangwang.com/papers/WANG-MCC-2018-10.html',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/panoptic_body3d.py
Vendored
+160
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@@ -0,0 +1,160 @@
dataset_info = dict(
dataset_name='panoptic_pose_3d',
paper_info=dict(
author='Joo, Hanbyul and Simon, Tomas and Li, Xulong'
'and Liu, Hao and Tan, Lei and Gui, Lin and Banerjee, Sean'
'and Godisart, Timothy and Nabbe, Bart and Matthews, Iain'
'and Kanade, Takeo and Nobuhara, Shohei and Sheikh, Yaser',
title='Panoptic Studio: A Massively Multiview System '
'for Interaction Motion Capture',
container='IEEE Transactions on Pattern Analysis'
' and Machine Intelligence',
year='2017',
homepage='http://domedb.perception.cs.cmu.edu',
),
keypoint_info={
0:
dict(name='neck', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(name='nose', id=1, color=[51, 153, 255], type='upper', swap=''),
2:
dict(name='mid_hip', id=2, color=[0, 255, 0], type='lower', swap=''),
3:
dict(
name='left_shoulder',
id=3,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
4:
dict(
name='left_elbow',
id=4,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
5:
dict(
name='left_wrist',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
6:
dict(
name='left_hip',
id=6,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
7:
dict(
name='left_knee',
id=7,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
8:
dict(
name='left_ankle',
id=8,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
9:
dict(
name='right_shoulder',
id=9,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
10:
dict(
name='right_elbow',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
11:
dict(
name='right_wrist',
id=11,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='right_knee',
id=13,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
14:
dict(
name='right_ankle',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_ankle'),
15:
dict(
name='left_eye',
id=15,
color=[51, 153, 255],
type='upper',
swap='right_eye'),
16:
dict(
name='left_ear',
id=16,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
17:
dict(
name='right_eye',
id=17,
color=[51, 153, 255],
type='upper',
swap='left_eye'),
18:
dict(
name='right_ear',
id=18,
color=[51, 153, 255],
type='upper',
swap='left_ear')
},
skeleton_info={
0: dict(link=('nose', 'neck'), id=0, color=[51, 153, 255]),
1: dict(link=('neck', 'left_shoulder'), id=1, color=[0, 255, 0]),
2: dict(link=('neck', 'right_shoulder'), id=2, color=[255, 128, 0]),
3: dict(link=('left_shoulder', 'left_elbow'), id=3, color=[0, 255, 0]),
4: dict(
link=('right_shoulder', 'right_elbow'), id=4, color=[255, 128, 0]),
5: dict(link=('left_elbow', 'left_wrist'), id=5, color=[0, 255, 0]),
6:
dict(link=('right_elbow', 'right_wrist'), id=6, color=[255, 128, 0]),
7: dict(link=('left_ankle', 'left_knee'), id=7, color=[0, 255, 0]),
8: dict(link=('left_knee', 'left_hip'), id=8, color=[0, 255, 0]),
9: dict(link=('right_ankle', 'right_knee'), id=9, color=[255, 128, 0]),
10: dict(link=('right_knee', 'right_hip'), id=10, color=[255, 128, 0]),
11: dict(link=('mid_hip', 'left_hip'), id=11, color=[0, 255, 0]),
12: dict(link=('mid_hip', 'right_hip'), id=12, color=[255, 128, 0]),
13: dict(link=('mid_hip', 'neck'), id=13, color=[51, 153, 255]),
},
joint_weights=[
1.0, 1.0, 1.0, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2, 1.5, 1.0, 1.2,
1.5, 1.0, 1.0, 1.0, 1.0
],
sigmas=[
0.026, 0.026, 0.107, 0.079, 0.072, 0.062, 0.107, 0.087, 0.089, 0.079,
0.072, 0.062, 0.107, 0.087, 0.089, 0.025, 0.035, 0.025, 0.035
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/panoptic_hand2d.py
Vendored
+143
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@@ -0,0 +1,143 @@
dataset_info = dict(
dataset_name='panoptic_hand2d',
paper_info=dict(
author='Simon, Tomas and Joo, Hanbyul and '
'Matthews, Iain and Sheikh, Yaser',
title='Hand keypoint detection in single images using '
'multiview bootstrapping',
container='Proceedings of the IEEE conference on '
'Computer Vision and Pattern Recognition',
year='2017',
homepage='http://domedb.perception.cs.cmu.edu/handdb.html',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/posetrack18.py
Vendored
+176
View File
@@ -0,0 +1,176 @@
dataset_info = dict(
dataset_name='posetrack18',
paper_info=dict(
author='Andriluka, Mykhaylo and Iqbal, Umar and '
'Insafutdinov, Eldar and Pishchulin, Leonid and '
'Milan, Anton and Gall, Juergen and Schiele, Bernt',
title='Posetrack: A benchmark for human pose estimation and tracking',
container='Proceedings of the IEEE Conference on '
'Computer Vision and Pattern Recognition',
year='2018',
homepage='https://posetrack.net/users/download.php',
),
keypoint_info={
0:
dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
1:
dict(
name='head_bottom',
id=1,
color=[51, 153, 255],
type='upper',
swap=''),
2:
dict(
name='head_top', id=2, color=[51, 153, 255], type='upper',
swap=''),
3:
dict(
name='left_ear',
id=3,
color=[51, 153, 255],
type='upper',
swap='right_ear'),
4:
dict(
name='right_ear',
id=4,
color=[51, 153, 255],
type='upper',
swap='left_ear'),
5:
dict(
name='left_shoulder',
id=5,
color=[0, 255, 0],
type='upper',
swap='right_shoulder'),
6:
dict(
name='right_shoulder',
id=6,
color=[255, 128, 0],
type='upper',
swap='left_shoulder'),
7:
dict(
name='left_elbow',
id=7,
color=[0, 255, 0],
type='upper',
swap='right_elbow'),
8:
dict(
name='right_elbow',
id=8,
color=[255, 128, 0],
type='upper',
swap='left_elbow'),
9:
dict(
name='left_wrist',
id=9,
color=[0, 255, 0],
type='upper',
swap='right_wrist'),
10:
dict(
name='right_wrist',
id=10,
color=[255, 128, 0],
type='upper',
swap='left_wrist'),
11:
dict(
name='left_hip',
id=11,
color=[0, 255, 0],
type='lower',
swap='right_hip'),
12:
dict(
name='right_hip',
id=12,
color=[255, 128, 0],
type='lower',
swap='left_hip'),
13:
dict(
name='left_knee',
id=13,
color=[0, 255, 0],
type='lower',
swap='right_knee'),
14:
dict(
name='right_knee',
id=14,
color=[255, 128, 0],
type='lower',
swap='left_knee'),
15:
dict(
name='left_ankle',
id=15,
color=[0, 255, 0],
type='lower',
swap='right_ankle'),
16:
dict(
name='right_ankle',
id=16,
color=[255, 128, 0],
type='lower',
swap='left_ankle')
},
skeleton_info={
0:
dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
1:
dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
2:
dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
3:
dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
4:
dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
5:
dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
6:
dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
7:
dict(
link=('left_shoulder', 'right_shoulder'),
id=7,
color=[51, 153, 255]),
8:
dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
9:
dict(
link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
10:
dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
11:
dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
12:
dict(link=('nose', 'head_bottom'), id=12, color=[51, 153, 255]),
13:
dict(link=('nose', 'head_top'), id=13, color=[51, 153, 255]),
14:
dict(
link=('head_bottom', 'left_shoulder'), id=14, color=[51, 153,
255]),
15:
dict(
link=('head_bottom', 'right_shoulder'),
id=15,
color=[51, 153, 255])
},
joint_weights=[
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
sigmas=[
0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/rhd2d.py
Vendored
+141
View File
@@ -0,0 +1,141 @@
dataset_info = dict(
dataset_name='rhd2d',
paper_info=dict(
author='Christian Zimmermann and Thomas Brox',
title='Learning to Estimate 3D Hand Pose from Single RGB Images',
container='arXiv',
year='2017',
homepage='https://lmb.informatik.uni-freiburg.de/resources/'
'datasets/RenderedHandposeDataset.en.html',
),
keypoint_info={
0:
dict(name='wrist', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='thumb1', id=1, color=[255, 128, 0], type='', swap=''),
2:
dict(name='thumb2', id=2, color=[255, 128, 0], type='', swap=''),
3:
dict(name='thumb3', id=3, color=[255, 128, 0], type='', swap=''),
4:
dict(name='thumb4', id=4, color=[255, 128, 0], type='', swap=''),
5:
dict(
name='forefinger1', id=5, color=[255, 153, 255], type='', swap=''),
6:
dict(
name='forefinger2', id=6, color=[255, 153, 255], type='', swap=''),
7:
dict(
name='forefinger3', id=7, color=[255, 153, 255], type='', swap=''),
8:
dict(
name='forefinger4', id=8, color=[255, 153, 255], type='', swap=''),
9:
dict(
name='middle_finger1',
id=9,
color=[102, 178, 255],
type='',
swap=''),
10:
dict(
name='middle_finger2',
id=10,
color=[102, 178, 255],
type='',
swap=''),
11:
dict(
name='middle_finger3',
id=11,
color=[102, 178, 255],
type='',
swap=''),
12:
dict(
name='middle_finger4',
id=12,
color=[102, 178, 255],
type='',
swap=''),
13:
dict(
name='ring_finger1', id=13, color=[255, 51, 51], type='', swap=''),
14:
dict(
name='ring_finger2', id=14, color=[255, 51, 51], type='', swap=''),
15:
dict(
name='ring_finger3', id=15, color=[255, 51, 51], type='', swap=''),
16:
dict(
name='ring_finger4', id=16, color=[255, 51, 51], type='', swap=''),
17:
dict(name='pinky_finger1', id=17, color=[0, 255, 0], type='', swap=''),
18:
dict(name='pinky_finger2', id=18, color=[0, 255, 0], type='', swap=''),
19:
dict(name='pinky_finger3', id=19, color=[0, 255, 0], type='', swap=''),
20:
dict(name='pinky_finger4', id=20, color=[0, 255, 0], type='', swap='')
},
skeleton_info={
0:
dict(link=('wrist', 'thumb1'), id=0, color=[255, 128, 0]),
1:
dict(link=('thumb1', 'thumb2'), id=1, color=[255, 128, 0]),
2:
dict(link=('thumb2', 'thumb3'), id=2, color=[255, 128, 0]),
3:
dict(link=('thumb3', 'thumb4'), id=3, color=[255, 128, 0]),
4:
dict(link=('wrist', 'forefinger1'), id=4, color=[255, 153, 255]),
5:
dict(link=('forefinger1', 'forefinger2'), id=5, color=[255, 153, 255]),
6:
dict(link=('forefinger2', 'forefinger3'), id=6, color=[255, 153, 255]),
7:
dict(link=('forefinger3', 'forefinger4'), id=7, color=[255, 153, 255]),
8:
dict(link=('wrist', 'middle_finger1'), id=8, color=[102, 178, 255]),
9:
dict(
link=('middle_finger1', 'middle_finger2'),
id=9,
color=[102, 178, 255]),
10:
dict(
link=('middle_finger2', 'middle_finger3'),
id=10,
color=[102, 178, 255]),
11:
dict(
link=('middle_finger3', 'middle_finger4'),
id=11,
color=[102, 178, 255]),
12:
dict(link=('wrist', 'ring_finger1'), id=12, color=[255, 51, 51]),
13:
dict(
link=('ring_finger1', 'ring_finger2'), id=13, color=[255, 51, 51]),
14:
dict(
link=('ring_finger2', 'ring_finger3'), id=14, color=[255, 51, 51]),
15:
dict(
link=('ring_finger3', 'ring_finger4'), id=15, color=[255, 51, 51]),
16:
dict(link=('wrist', 'pinky_finger1'), id=16, color=[0, 255, 0]),
17:
dict(
link=('pinky_finger1', 'pinky_finger2'), id=17, color=[0, 255, 0]),
18:
dict(
link=('pinky_finger2', 'pinky_finger3'), id=18, color=[0, 255, 0]),
19:
dict(
link=('pinky_finger3', 'pinky_finger4'), id=19, color=[0, 255, 0])
},
joint_weights=[1.] * 21,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/wflw.py
Vendored
+582
View File
@@ -0,0 +1,582 @@
dataset_info = dict(
dataset_name='wflw',
paper_info=dict(
author='Wu, Wayne and Qian, Chen and Yang, Shuo and Wang, '
'Quan and Cai, Yici and Zhou, Qiang',
title='Look at boundary: A boundary-aware face alignment algorithm',
container='Proceedings of the IEEE conference on computer '
'vision and pattern recognition',
year='2018',
homepage='https://wywu.github.io/projects/LAB/WFLW.html',
),
keypoint_info={
0:
dict(
name='kpt-0', id=0, color=[255, 255, 255], type='', swap='kpt-32'),
1:
dict(
name='kpt-1', id=1, color=[255, 255, 255], type='', swap='kpt-31'),
2:
dict(
name='kpt-2', id=2, color=[255, 255, 255], type='', swap='kpt-30'),
3:
dict(
name='kpt-3', id=3, color=[255, 255, 255], type='', swap='kpt-29'),
4:
dict(
name='kpt-4', id=4, color=[255, 255, 255], type='', swap='kpt-28'),
5:
dict(
name='kpt-5', id=5, color=[255, 255, 255], type='', swap='kpt-27'),
6:
dict(
name='kpt-6', id=6, color=[255, 255, 255], type='', swap='kpt-26'),
7:
dict(
name='kpt-7', id=7, color=[255, 255, 255], type='', swap='kpt-25'),
8:
dict(
name='kpt-8', id=8, color=[255, 255, 255], type='', swap='kpt-24'),
9:
dict(
name='kpt-9', id=9, color=[255, 255, 255], type='', swap='kpt-23'),
10:
dict(
name='kpt-10',
id=10,
color=[255, 255, 255],
type='',
swap='kpt-22'),
11:
dict(
name='kpt-11',
id=11,
color=[255, 255, 255],
type='',
swap='kpt-21'),
12:
dict(
name='kpt-12',
id=12,
color=[255, 255, 255],
type='',
swap='kpt-20'),
13:
dict(
name='kpt-13',
id=13,
color=[255, 255, 255],
type='',
swap='kpt-19'),
14:
dict(
name='kpt-14',
id=14,
color=[255, 255, 255],
type='',
swap='kpt-18'),
15:
dict(
name='kpt-15',
id=15,
color=[255, 255, 255],
type='',
swap='kpt-17'),
16:
dict(name='kpt-16', id=16, color=[255, 255, 255], type='', swap=''),
17:
dict(
name='kpt-17',
id=17,
color=[255, 255, 255],
type='',
swap='kpt-15'),
18:
dict(
name='kpt-18',
id=18,
color=[255, 255, 255],
type='',
swap='kpt-14'),
19:
dict(
name='kpt-19',
id=19,
color=[255, 255, 255],
type='',
swap='kpt-13'),
20:
dict(
name='kpt-20',
id=20,
color=[255, 255, 255],
type='',
swap='kpt-12'),
21:
dict(
name='kpt-21',
id=21,
color=[255, 255, 255],
type='',
swap='kpt-11'),
22:
dict(
name='kpt-22',
id=22,
color=[255, 255, 255],
type='',
swap='kpt-10'),
23:
dict(
name='kpt-23', id=23, color=[255, 255, 255], type='',
swap='kpt-9'),
24:
dict(
name='kpt-24', id=24, color=[255, 255, 255], type='',
swap='kpt-8'),
25:
dict(
name='kpt-25', id=25, color=[255, 255, 255], type='',
swap='kpt-7'),
26:
dict(
name='kpt-26', id=26, color=[255, 255, 255], type='',
swap='kpt-6'),
27:
dict(
name='kpt-27', id=27, color=[255, 255, 255], type='',
swap='kpt-5'),
28:
dict(
name='kpt-28', id=28, color=[255, 255, 255], type='',
swap='kpt-4'),
29:
dict(
name='kpt-29', id=29, color=[255, 255, 255], type='',
swap='kpt-3'),
30:
dict(
name='kpt-30', id=30, color=[255, 255, 255], type='',
swap='kpt-2'),
31:
dict(
name='kpt-31', id=31, color=[255, 255, 255], type='',
swap='kpt-1'),
32:
dict(
name='kpt-32', id=32, color=[255, 255, 255], type='',
swap='kpt-0'),
33:
dict(
name='kpt-33',
id=33,
color=[255, 255, 255],
type='',
swap='kpt-46'),
34:
dict(
name='kpt-34',
id=34,
color=[255, 255, 255],
type='',
swap='kpt-45'),
35:
dict(
name='kpt-35',
id=35,
color=[255, 255, 255],
type='',
swap='kpt-44'),
36:
dict(
name='kpt-36',
id=36,
color=[255, 255, 255],
type='',
swap='kpt-43'),
37:
dict(
name='kpt-37',
id=37,
color=[255, 255, 255],
type='',
swap='kpt-42'),
38:
dict(
name='kpt-38',
id=38,
color=[255, 255, 255],
type='',
swap='kpt-50'),
39:
dict(
name='kpt-39',
id=39,
color=[255, 255, 255],
type='',
swap='kpt-49'),
40:
dict(
name='kpt-40',
id=40,
color=[255, 255, 255],
type='',
swap='kpt-48'),
41:
dict(
name='kpt-41',
id=41,
color=[255, 255, 255],
type='',
swap='kpt-47'),
42:
dict(
name='kpt-42',
id=42,
color=[255, 255, 255],
type='',
swap='kpt-37'),
43:
dict(
name='kpt-43',
id=43,
color=[255, 255, 255],
type='',
swap='kpt-36'),
44:
dict(
name='kpt-44',
id=44,
color=[255, 255, 255],
type='',
swap='kpt-35'),
45:
dict(
name='kpt-45',
id=45,
color=[255, 255, 255],
type='',
swap='kpt-34'),
46:
dict(
name='kpt-46',
id=46,
color=[255, 255, 255],
type='',
swap='kpt-33'),
47:
dict(
name='kpt-47',
id=47,
color=[255, 255, 255],
type='',
swap='kpt-41'),
48:
dict(
name='kpt-48',
id=48,
color=[255, 255, 255],
type='',
swap='kpt-40'),
49:
dict(
name='kpt-49',
id=49,
color=[255, 255, 255],
type='',
swap='kpt-39'),
50:
dict(
name='kpt-50',
id=50,
color=[255, 255, 255],
type='',
swap='kpt-38'),
51:
dict(name='kpt-51', id=51, color=[255, 255, 255], type='', swap=''),
52:
dict(name='kpt-52', id=52, color=[255, 255, 255], type='', swap=''),
53:
dict(name='kpt-53', id=53, color=[255, 255, 255], type='', swap=''),
54:
dict(name='kpt-54', id=54, color=[255, 255, 255], type='', swap=''),
55:
dict(
name='kpt-55',
id=55,
color=[255, 255, 255],
type='',
swap='kpt-59'),
56:
dict(
name='kpt-56',
id=56,
color=[255, 255, 255],
type='',
swap='kpt-58'),
57:
dict(name='kpt-57', id=57, color=[255, 255, 255], type='', swap=''),
58:
dict(
name='kpt-58',
id=58,
color=[255, 255, 255],
type='',
swap='kpt-56'),
59:
dict(
name='kpt-59',
id=59,
color=[255, 255, 255],
type='',
swap='kpt-55'),
60:
dict(
name='kpt-60',
id=60,
color=[255, 255, 255],
type='',
swap='kpt-72'),
61:
dict(
name='kpt-61',
id=61,
color=[255, 255, 255],
type='',
swap='kpt-71'),
62:
dict(
name='kpt-62',
id=62,
color=[255, 255, 255],
type='',
swap='kpt-70'),
63:
dict(
name='kpt-63',
id=63,
color=[255, 255, 255],
type='',
swap='kpt-69'),
64:
dict(
name='kpt-64',
id=64,
color=[255, 255, 255],
type='',
swap='kpt-68'),
65:
dict(
name='kpt-65',
id=65,
color=[255, 255, 255],
type='',
swap='kpt-75'),
66:
dict(
name='kpt-66',
id=66,
color=[255, 255, 255],
type='',
swap='kpt-74'),
67:
dict(
name='kpt-67',
id=67,
color=[255, 255, 255],
type='',
swap='kpt-73'),
68:
dict(
name='kpt-68',
id=68,
color=[255, 255, 255],
type='',
swap='kpt-64'),
69:
dict(
name='kpt-69',
id=69,
color=[255, 255, 255],
type='',
swap='kpt-63'),
70:
dict(
name='kpt-70',
id=70,
color=[255, 255, 255],
type='',
swap='kpt-62'),
71:
dict(
name='kpt-71',
id=71,
color=[255, 255, 255],
type='',
swap='kpt-61'),
72:
dict(
name='kpt-72',
id=72,
color=[255, 255, 255],
type='',
swap='kpt-60'),
73:
dict(
name='kpt-73',
id=73,
color=[255, 255, 255],
type='',
swap='kpt-67'),
74:
dict(
name='kpt-74',
id=74,
color=[255, 255, 255],
type='',
swap='kpt-66'),
75:
dict(
name='kpt-75',
id=75,
color=[255, 255, 255],
type='',
swap='kpt-65'),
76:
dict(
name='kpt-76',
id=76,
color=[255, 255, 255],
type='',
swap='kpt-82'),
77:
dict(
name='kpt-77',
id=77,
color=[255, 255, 255],
type='',
swap='kpt-81'),
78:
dict(
name='kpt-78',
id=78,
color=[255, 255, 255],
type='',
swap='kpt-80'),
79:
dict(name='kpt-79', id=79, color=[255, 255, 255], type='', swap=''),
80:
dict(
name='kpt-80',
id=80,
color=[255, 255, 255],
type='',
swap='kpt-78'),
81:
dict(
name='kpt-81',
id=81,
color=[255, 255, 255],
type='',
swap='kpt-77'),
82:
dict(
name='kpt-82',
id=82,
color=[255, 255, 255],
type='',
swap='kpt-76'),
83:
dict(
name='kpt-83',
id=83,
color=[255, 255, 255],
type='',
swap='kpt-87'),
84:
dict(
name='kpt-84',
id=84,
color=[255, 255, 255],
type='',
swap='kpt-86'),
85:
dict(name='kpt-85', id=85, color=[255, 255, 255], type='', swap=''),
86:
dict(
name='kpt-86',
id=86,
color=[255, 255, 255],
type='',
swap='kpt-84'),
87:
dict(
name='kpt-87',
id=87,
color=[255, 255, 255],
type='',
swap='kpt-83'),
88:
dict(
name='kpt-88',
id=88,
color=[255, 255, 255],
type='',
swap='kpt-92'),
89:
dict(
name='kpt-89',
id=89,
color=[255, 255, 255],
type='',
swap='kpt-91'),
90:
dict(name='kpt-90', id=90, color=[255, 255, 255], type='', swap=''),
91:
dict(
name='kpt-91',
id=91,
color=[255, 255, 255],
type='',
swap='kpt-89'),
92:
dict(
name='kpt-92',
id=92,
color=[255, 255, 255],
type='',
swap='kpt-88'),
93:
dict(
name='kpt-93',
id=93,
color=[255, 255, 255],
type='',
swap='kpt-95'),
94:
dict(name='kpt-94', id=94, color=[255, 255, 255], type='', swap=''),
95:
dict(
name='kpt-95',
id=95,
color=[255, 255, 255],
type='',
swap='kpt-93'),
96:
dict(
name='kpt-96',
id=96,
color=[255, 255, 255],
type='',
swap='kpt-97'),
97:
dict(
name='kpt-97',
id=97,
color=[255, 255, 255],
type='',
swap='kpt-96')
},
skeleton_info={},
joint_weights=[1.] * 98,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/datasets/zebra.py
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dataset_info = dict(
dataset_name='zebra',
paper_info=dict(
author='Graving, Jacob M and Chae, Daniel and Naik, Hemal and '
'Li, Liang and Koger, Benjamin and Costelloe, Blair R and '
'Couzin, Iain D',
title='DeepPoseKit, a software toolkit for fast and robust '
'animal pose estimation using deep learning',
container='Elife',
year='2019',
homepage='https://github.com/jgraving/DeepPoseKit-Data',
),
keypoint_info={
0:
dict(name='snout', id=0, color=[255, 255, 255], type='', swap=''),
1:
dict(name='head', id=1, color=[255, 255, 255], type='', swap=''),
2:
dict(name='neck', id=2, color=[255, 255, 255], type='', swap=''),
3:
dict(
name='forelegL1',
id=3,
color=[255, 255, 255],
type='',
swap='forelegR1'),
4:
dict(
name='forelegR1',
id=4,
color=[255, 255, 255],
type='',
swap='forelegL1'),
5:
dict(
name='hindlegL1',
id=5,
color=[255, 255, 255],
type='',
swap='hindlegR1'),
6:
dict(
name='hindlegR1',
id=6,
color=[255, 255, 255],
type='',
swap='hindlegL1'),
7:
dict(name='tailbase', id=7, color=[255, 255, 255], type='', swap=''),
8:
dict(name='tailtip', id=8, color=[255, 255, 255], type='', swap='')
},
skeleton_info={
0: dict(link=('head', 'snout'), id=0, color=[255, 255, 255]),
1: dict(link=('neck', 'head'), id=1, color=[255, 255, 255]),
2: dict(link=('forelegL1', 'neck'), id=2, color=[255, 255, 255]),
3: dict(link=('forelegR1', 'neck'), id=3, color=[255, 255, 255]),
4: dict(link=('hindlegL1', 'tailbase'), id=4, color=[255, 255, 255]),
5: dict(link=('hindlegR1', 'tailbase'), id=5, color=[255, 255, 255]),
6: dict(link=('tailbase', 'neck'), id=6, color=[255, 255, 255]),
7: dict(link=('tailtip', 'tailbase'), id=7, color=[255, 255, 255])
},
joint_weights=[1.] * 9,
sigmas=[])
third_party/hamer/third-party/ViTPose/configs/_base_/default_runtime.py
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checkpoint_config = dict(interval=10)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
log_level = 'INFO'
load_from = None
resume_from = None
dist_params = dict(backend='nccl')
workflow = [('train', 1)]
# disable opencv multithreading to avoid system being overloaded
opencv_num_threads = 0
# set multi-process start method as `fork` to speed up the training
mp_start_method = 'fork'
third_party/hamer/third-party/ViTPose/configs/_base_/filters/gausian_filter.py
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third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/README.md
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# 2D Animal Keypoint Detection
2D animal keypoint detection (animal pose estimation) aims to detect the key-point of different species, including rats,
dogs, macaques, and cheetah. It provides detailed behavioral analysis for neuroscience, medical and ecology applications.
## Data preparation
Please follow [DATA Preparation](/docs/en/tasks/2d_animal_keypoint.md) to prepare data.
## Demo
Please follow [DEMO](/demo/docs/2d_animal_demo.md) to generate fancy demos.
<img src="https://user-images.githubusercontent.com/11788150/114201893-4446ec00-9989-11eb-808b-5718c47c7b23.gif" height="140px" alt><br>
<img src="https://user-images.githubusercontent.com/11788150/114205282-b5d46980-998c-11eb-9d6b-85ba47f81252.gif" height="140px" alt><br>
<img src="https://user-images.githubusercontent.com/11788150/114023530-944c8280-98a5-11eb-86b0-5f6d3e232af0.gif" height="140px" alt><br>
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/README.md
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# Top-down heatmap-based pose estimation
Top-down methods divide the task into two stages: object detection and pose estimation.
They perform object detection first, 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.
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_animalpose.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary>
```bibtex
@inproceedings{sun2019deep,
title={Deep high-resolution representation learning for human pose estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5693--5703},
year={2019}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.html">Animal-Pose (ICCV'2019)</a></summary>
```bibtex
@InProceedings{Cao_2019_ICCV,
author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing},
title = {Cross-Domain Adaptation for Animal Pose Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```
</details>
Results on AnimalPose validation set (1117 instances)
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py) | 256x256 | 0.736 | 0.959 | 0.832 | 0.775 | 0.966 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256_20210426.log.json) |
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w48_animalpose_256x256.py) | 256x256 | 0.737 | 0.959 | 0.823 | 0.778 | 0.962 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256-34644726_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256_20210426.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_animalpose.yml
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Collections:
- Name: HRNet
Paper:
Title: Deep high-resolution representation learning for human pose estimation
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py
In Collection: HRNet
Metadata:
Architecture: &id001
- HRNet
Training Data: Animal-Pose
Name: topdown_heatmap_hrnet_w32_animalpose_256x256
Results:
- Dataset: Animal-Pose
Metrics:
AP: 0.736
AP@0.5: 0.959
AP@0.75: 0.832
AR: 0.775
AR@0.5: 0.966
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_animalpose_256x256-1aa7f075_20210426.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w48_animalpose_256x256.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Animal-Pose
Name: topdown_heatmap_hrnet_w48_animalpose_256x256
Results:
- Dataset: Animal-Pose
Metrics:
AP: 0.737
AP@0.5: 0.959
AP@0.75: 0.823
AR: 0.778
AR@0.5: 0.962
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_animalpose_256x256-34644726_20210426.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w32_animalpose_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=20,
dataset_joints=20,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/animalpose'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_w48_animalpose_256x256.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=20,
dataset_joints=20,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/animalpose'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res101_animalpose_256x256.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=20,
dataset_joints=20,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/animalpose'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res152_animalpose_256x256.py
Vendored
+141
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=20,
dataset_joints=20,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet152',
backbone=dict(type='ResNet', depth=152),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/animalpose'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res50_animalpose_256x256.py
Vendored
+141
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/animalpose.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=20,
dataset_joints=20,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', depth=50),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/animalpose'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_train.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalPoseDataset',
ann_file=f'{data_root}/annotations/animalpose_val.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/resnet_animalpose.md
Vendored
+41
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@@ -0,0 +1,41 @@
<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary>
```bibtex
@inproceedings{xiao2018simple,
title={Simple baselines for human pose estimation and tracking},
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={466--481},
year={2018}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ICCV_2019/html/Cao_Cross-Domain_Adaptation_for_Animal_Pose_Estimation_ICCV_2019_paper.html">Animal-Pose (ICCV'2019)</a></summary>
```bibtex
@InProceedings{Cao_2019_ICCV,
author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing},
title = {Cross-Domain Adaptation for Animal Pose Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
```
</details>
Results on AnimalPose validation set (1117 instances)
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res50_animalpose_256x256.py) | 256x256 | 0.688 | 0.945 | 0.772 | 0.733 | 0.952 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256-e1f30bff_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256_20210426.log.json) |
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res101_animalpose_256x256.py) | 256x256 | 0.696 | 0.948 | 0.785 | 0.737 | 0.954 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256-85563f4a_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256_20210426.log.json) |
| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res152_animalpose_256x256.py) | 256x256 | 0.709 | 0.948 | 0.797 | 0.749 | 0.951 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256-a0a7506c_20210426.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256_20210426.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/resnet_animalpose.yml
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Collections:
- Name: SimpleBaseline2D
Paper:
Title: Simple baselines for human pose estimation and tracking
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res50_animalpose_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: &id001
- SimpleBaseline2D
Training Data: Animal-Pose
Name: topdown_heatmap_res50_animalpose_256x256
Results:
- Dataset: Animal-Pose
Metrics:
AP: 0.688
AP@0.5: 0.945
AP@0.75: 0.772
AR: 0.733
AR@0.5: 0.952
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_animalpose_256x256-e1f30bff_20210426.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res101_animalpose_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: Animal-Pose
Name: topdown_heatmap_res101_animalpose_256x256
Results:
- Dataset: Animal-Pose
Metrics:
AP: 0.696
AP@0.5: 0.948
AP@0.75: 0.785
AR: 0.737
AR@0.5: 0.954
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_animalpose_256x256-85563f4a_20210426.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/res152_animalpose_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: Animal-Pose
Name: topdown_heatmap_res152_animalpose_256x256
Results:
- Dataset: Animal-Pose
Metrics:
AP: 0.709
AP@0.5: 0.948
AP@0.75: 0.797
AR: 0.749
AR@0.5: 0.951
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_animalpose_256x256-a0a7506c_20210426.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_base_ap10k_256x192.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=768,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/apt36k'
data = dict(
samples_per_gpu=32,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/train_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_huge_ap10k_256x192.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1280,
depth=32,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1280,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_large_ap10k_256x192.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1024,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/ViTPose_small_ap10k_256x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=384,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=384,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_ap10k.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary>
```bibtex
@inproceedings{sun2019deep,
title={Deep high-resolution representation learning for human pose estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5693--5703},
year={2019}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://arxiv.org/abs/2108.12617">AP-10K (NeurIPS'2021)</a></summary>
```bibtex
@misc{yu2021ap10k,
title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild},
author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao},
year={2021},
eprint={2108.12617},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
</details>
Results on AP-10K validation set
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AP<sup>M</sup> | AP<sup>L</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w32_ap10k_256x256.py) | 256x256 | 0.738 | 0.958 | 0.808 | 0.592 | 0.743 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.log.json) |
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w48_ap10k_256x256.py) | 256x256 | 0.744 | 0.959 | 0.807 | 0.589 | 0.748 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_ap10k.yml
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Collections:
- Name: HRNet
Paper:
Title: Deep high-resolution representation learning for human pose estimation
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w32_ap10k_256x256.py
In Collection: HRNet
Metadata:
Architecture: &id001
- HRNet
Training Data: AP-10K
Name: topdown_heatmap_hrnet_w32_ap10k_256x256
Results:
- Dataset: AP-10K
Metrics:
AP: 0.738
AP@0.5: 0.958
AP@0.75: 0.808
APL: 0.743
APM: 0.592
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_ap10k_256x256-18aac840_20211029.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w48_ap10k_256x256.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: AP-10K
Name: topdown_heatmap_hrnet_w48_ap10k_256x256
Results:
- Dataset: AP-10K
Metrics:
AP: 0.744
AP@0.5: 0.959
AP@0.75: 0.807
APL: 0.748
APM: 0.589
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_ap10k_256x256-d95ab412_20211029.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w32_ap10k_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_w48_ap10k_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res101_ap10k_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', depth=50),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/resnet_ap10k.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary>
```bibtex
@inproceedings{xiao2018simple,
title={Simple baselines for human pose estimation and tracking},
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={466--481},
year={2018}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://arxiv.org/abs/2108.12617">AP-10K (NeurIPS'2021)</a></summary>
```bibtex
@misc{yu2021ap10k,
title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild},
author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao},
year={2021},
eprint={2108.12617},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
</details>
Results on AP-10K validation set
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AP<sup>M</sup> | AP<sup>L</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py) | 256x256 | 0.699 | 0.940 | 0.760 | 0.570 | 0.703 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.log.json) |
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res101_ap10k_256x256.py) | 256x256 | 0.698 | 0.943 | 0.754 | 0.543 | 0.702 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/resnet_ap10k.yml
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Collections:
- Name: SimpleBaseline2D
Paper:
Title: Simple baselines for human pose estimation and tracking
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res50_ap10k_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: &id001
- SimpleBaseline2D
Training Data: AP-10K
Name: topdown_heatmap_res50_ap10k_256x256
Results:
- Dataset: AP-10K
Metrics:
AP: 0.699
AP@0.5: 0.94
AP@0.75: 0.76
APL: 0.703
APM: 0.57
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_ap10k_256x256-35760eb8_20211029.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/res101_ap10k_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: AP-10K
Name: topdown_heatmap_res101_ap10k_256x256
Results:
- Dataset: AP-10K
Metrics:
AP: 0.698
AP@0.5: 0.943
AP@0.75: 0.754
APL: 0.702
APM: 0.543
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_ap10k_256x256-9edfafb9_20211029.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_base_apt36k_256x192.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=768,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=768,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/ap10k'
data = dict(
samples_per_gpu=64,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-train-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-val-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/ap10k-test-split1.json',
img_prefix=f'{data_root}/data/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_huge_apt36k_256x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1280,
depth=32,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1280,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/apt36k'
data = dict(
samples_per_gpu=32,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/train_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_large_apt36k_256x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=1024,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/apt36k'
data = dict(
samples_per_gpu=32,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/train_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/apt36k/ViTPose_small_apt36k_256x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/ap10k.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=17,
dataset_joints=17,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
])
# model settings
model = dict(
type='TopDown',
pretrained=None,
backbone=dict(
type='ViT',
img_size=(256, 192),
patch_size=16,
embed_dim=384,
depth=12,
num_heads=12,
ratio=1,
use_checkpoint=False,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=0.3,
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=384,
num_deconv_layers=2,
num_deconv_filters=(256, 256),
num_deconv_kernels=(4, 4),
extra=dict(final_conv_kernel=1, ),
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/apt36k'
data = dict(
samples_per_gpu=32,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/train_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalAP10KDataset',
ann_file=f'{data_root}/annotations/val_annotations_1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_atrw.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary>
```bibtex
@inproceedings{sun2019deep,
title={Deep high-resolution representation learning for human pose estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5693--5703},
year={2019}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://arxiv.org/abs/1906.05586">ATRW (ACM MM'2020)</a></summary>
```bibtex
@inproceedings{li2020atrw,
title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild},
author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao},
booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
pages={2590--2598},
year={2020}
}
```
</details>
Results on ATRW validation set
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w32_atrw_256x256.py) | 256x256 | 0.912 | 0.973 | 0.959 | 0.938 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256-f027f09a_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256_20210414.log.json) |
| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w48_atrw_256x256.py) | 256x256 | 0.911 | 0.972 | 0.946 | 0.937 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256-ac088892_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256_20210414.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_atrw.yml
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Collections:
- Name: HRNet
Paper:
Title: Deep high-resolution representation learning for human pose estimation
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w32_atrw_256x256.py
In Collection: HRNet
Metadata:
Architecture: &id001
- HRNet
Training Data: ATRW
Name: topdown_heatmap_hrnet_w32_atrw_256x256
Results:
- Dataset: ATRW
Metrics:
AP: 0.912
AP@0.5: 0.973
AP@0.75: 0.959
AR: 0.938
AR@0.5: 0.985
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_atrw_256x256-f027f09a_20210414.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w48_atrw_256x256.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: ATRW
Name: topdown_heatmap_hrnet_w48_atrw_256x256
Results:
- Dataset: ATRW
Metrics:
AP: 0.911
AP@0.5: 0.972
AP@0.75: 0.946
AR: 0.937
AR@0.5: 0.985
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_atrw_256x256-ac088892_20210414.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w32_atrw_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/atrw'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_train.json',
img_prefix=f'{data_root}/images/train/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_w48_atrw_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/atrw'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_train.json',
img_prefix=f'{data_root}/images/train/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res101_atrw_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/atrw'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_train.json',
img_prefix=f'{data_root}/images/train/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet152',
backbone=dict(type='ResNet', depth=152),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/atrw'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_train.json',
img_prefix=f'{data_root}/images/train/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/atrw.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=15,
dataset_joints=15,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', depth=50),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
bbox_file='',
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownHalfBodyTransform',
num_joints_half_body=8,
prob_half_body=0.3),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/atrw'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_train.json',
img_prefix=f'{data_root}/images/train/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalATRWDataset',
ann_file=f'{data_root}/annotations/keypoint_val.json',
img_prefix=f'{data_root}/images/val/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/resnet_atrw.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary>
```bibtex
@inproceedings{xiao2018simple,
title={Simple baselines for human pose estimation and tracking},
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={466--481},
year={2018}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://arxiv.org/abs/1906.05586">ATRW (ACM MM'2020)</a></summary>
```bibtex
@inproceedings{li2020atrw,
title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild},
author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao},
booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
pages={2590--2598},
year={2020}
}
```
</details>
Results on ATRW validation set
| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
| :-------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
| [pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py) | 256x256 | 0.900 | 0.973 | 0.932 | 0.929 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256-546c4594_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256_20210414.log.json) |
| [pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res101_atrw_256x256.py) | 256x256 | 0.898 | 0.973 | 0.936 | 0.927 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256-da93f371_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256_20210414.log.json) |
| [pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py) | 256x256 | 0.896 | 0.973 | 0.931 | 0.927 | 0.985 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256-2bb8e162_20210414.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256_20210414.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/resnet_atrw.yml
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Collections:
- Name: SimpleBaseline2D
Paper:
Title: Simple baselines for human pose estimation and tracking
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res50_atrw_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: &id001
- SimpleBaseline2D
Training Data: ATRW
Name: topdown_heatmap_res50_atrw_256x256
Results:
- Dataset: ATRW
Metrics:
AP: 0.9
AP@0.5: 0.973
AP@0.75: 0.932
AR: 0.929
AR@0.5: 0.985
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_atrw_256x256-546c4594_20210414.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res101_atrw_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: ATRW
Name: topdown_heatmap_res101_atrw_256x256
Results:
- Dataset: ATRW
Metrics:
AP: 0.898
AP@0.5: 0.973
AP@0.75: 0.936
AR: 0.927
AR@0.5: 0.985
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_atrw_256x256-da93f371_20210414.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/res152_atrw_256x256.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: ATRW
Name: topdown_heatmap_res152_atrw_256x256
Results:
- Dataset: ATRW
Metrics:
AP: 0.896
AP@0.5: 0.973
AP@0.75: 0.931
AR: 0.927
AR@0.5: 0.985
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_atrw_256x256-2bb8e162_20210414.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res101_fly_192x192.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=32,
dataset_joints=32,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 192],
heatmap_size=[48, 48],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
]
test_pipeline = val_pipeline
data_root = 'data/fly'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_train.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=32,
dataset_joints=32,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet152',
backbone=dict(type='ResNet', depth=152),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 192],
heatmap_size=[48, 48],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
]
test_pipeline = val_pipeline
data_root = 'data/fly'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_train.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res50_fly_192x192.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/fly.py'
]
evaluation = dict(interval=10, metric=['PCK', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=32,
dataset_joints=32,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet50',
backbone=dict(type='ResNet', depth=50),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[192, 192],
heatmap_size=[48, 48],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']),
]
test_pipeline = val_pipeline
data_root = 'data/fly'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_train.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalFlyDataset',
ann_file=f'{data_root}/annotations/fly_test.json',
img_prefix=f'{data_root}/images/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/resnet_fly.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html">SimpleBaseline2D (ECCV'2018)</a></summary>
```bibtex
@inproceedings{xiao2018simple,
title={Simple baselines for human pose estimation and tracking},
author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={466--481},
year={2018}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://www.nature.com/articles/s41592-018-0234-5">Vinegar Fly (Nature Methods'2019)</a></summary>
```bibtex
@article{pereira2019fast,
title={Fast animal pose estimation using deep neural networks},
author={Pereira, Talmo D and Aldarondo, Diego E and Willmore, Lindsay and Kislin, Mikhail and Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W},
journal={Nature methods},
volume={16},
number={1},
pages={117--125},
year={2019},
publisher={Nature Publishing Group}
}
```
</details>
Results on Vinegar Fly test set
| Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log |
| :-------- | :--------: | :------: | :------: | :------: |:------: |:------: |
|[pose_resnet_50](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res50_fly_192x192.py) | 192x192 | 0.996 | 0.910 | 2.00 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192-5d0ee2d9_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192_20210407.log.json) |
|[pose_resnet_101](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res101_fly_192x192.py) | 192x192 | 0.996 | 0.912 | 1.95 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192-41a7a6cc_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192_20210407.log.json) |
|[pose_resnet_152](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py) | 192x192 | 0.997 | 0.917 | 1.78 | [ckpt](https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth) | [log](https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192_20210407.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/resnet_fly.yml
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Collections:
- Name: SimpleBaseline2D
Paper:
Title: Simple baselines for human pose estimation and tracking
URL: http://openaccess.thecvf.com/content_ECCV_2018/html/Bin_Xiao_Simple_Baselines_for_ECCV_2018_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/algorithms/simplebaseline2d.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res50_fly_192x192.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: &id001
- SimpleBaseline2D
Training Data: Vinegar Fly
Name: topdown_heatmap_res50_fly_192x192
Results:
- Dataset: Vinegar Fly
Metrics:
AUC: 0.91
EPE: 2.0
PCK@0.2: 0.996
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res50_fly_192x192-5d0ee2d9_20210407.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res101_fly_192x192.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: Vinegar Fly
Name: topdown_heatmap_res101_fly_192x192
Results:
- Dataset: Vinegar Fly
Metrics:
AUC: 0.912
EPE: 1.95
PCK@0.2: 0.996
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res101_fly_192x192-41a7a6cc_20210407.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/res152_fly_192x192.py
In Collection: SimpleBaseline2D
Metadata:
Architecture: *id001
Training Data: Vinegar Fly
Name: topdown_heatmap_res152_fly_192x192
Results:
- Dataset: Vinegar Fly
Metrics:
AUC: 0.917
EPE: 1.78
PCK@0.2: 0.997
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/resnet/res152_fly_192x192-fcafbd5a_20210407.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_horse10.md
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<!-- [ALGORITHM] -->
<details>
<summary align="right"><a href="http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html">HRNet (CVPR'2019)</a></summary>
```bibtex
@inproceedings{sun2019deep,
title={Deep high-resolution representation learning for human pose estimation},
author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={5693--5703},
year={2019}
}
```
</details>
<!-- [DATASET] -->
<details>
<summary align="right"><a href="https://openaccess.thecvf.com/content/WACV2021/html/Mathis_Pretraining_Boosts_Out-of-Domain_Robustness_for_Pose_Estimation_WACV_2021_paper.html">Horse-10 (WACV'2021)</a></summary>
```bibtex
@inproceedings{mathis2021pretraining,
title={Pretraining boosts out-of-domain robustness for pose estimation},
author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={1859--1868},
year={2021}
}
```
</details>
Results on Horse-10 test set
|Set | Arch | Input Size | PCK@0.3 | NME | ckpt | log |
| :--- | :---: | :--------: | :------: | :------: |:------: |:------: |
|split1| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split1.py) | 256x256 | 0.951 | 0.122 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1-401d901a_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1_20210405.log.json) |
|split2| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split2.py) | 256x256 | 0.949 | 0.116 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2-04840523_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2_20210405.log.json) |
|split3| [pose_hrnet_w32](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split3.py) | 256x256 | 0.939 | 0.153 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3-4db47400_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3_20210405.log.json) |
|split1| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split1.py) | 256x256 | 0.973 | 0.095 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1-3c950d3b_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1_20210405.log.json) |
|split2| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split2.py) | 256x256 | 0.969 | 0.101 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2-8ef72b5d_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2_20210405.log.json) |
|split3| [pose_hrnet_w48](/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split3.py) | 256x256 | 0.961 | 0.128 | [ckpt](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3-0232ec47_20210405.pth) | [log](https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3_20210405.log.json) |
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_horse10.yml
Vendored
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Collections:
- Name: HRNet
Paper:
Title: Deep high-resolution representation learning for human pose estimation
URL: http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html
README: https://github.com/open-mmlab/mmpose/blob/master/docs/en/papers/backbones/hrnet.md
Models:
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split1.py
In Collection: HRNet
Metadata:
Architecture: &id001
- HRNet
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split1
Results:
- Dataset: Horse-10
Metrics:
NME: 0.122
PCK@0.3: 0.951
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split1-401d901a_20210405.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split2.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split2
Results:
- Dataset: Horse-10
Metrics:
NME: 0.116
PCK@0.3: 0.949
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split2-04840523_20210405.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split3.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w32_horse10_256x256-split3
Results:
- Dataset: Horse-10
Metrics:
NME: 0.153
PCK@0.3: 0.939
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w32_horse10_256x256_split3-4db47400_20210405.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split1.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split1
Results:
- Dataset: Horse-10
Metrics:
NME: 0.095
PCK@0.3: 0.973
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split1-3c950d3b_20210405.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split2.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split2
Results:
- Dataset: Horse-10
Metrics:
NME: 0.101
PCK@0.3: 0.969
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split2-8ef72b5d_20210405.pth
- Config: configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split3.py
In Collection: HRNet
Metadata:
Architecture: *id001
Training Data: Horse-10
Name: topdown_heatmap_hrnet_w48_horse10_256x256-split3
Results:
- Dataset: Horse-10
Metrics:
NME: 0.128
PCK@0.3: 0.961
Task: Animal 2D Keypoint
Weights: https://download.openmmlab.com/mmpose/animal/hrnet/hrnet_w48_horse10_256x256_split3-0232ec47_20210405.pth
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split1.py
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split2.py
Vendored
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w32_horse10_256x256-split3.py
Vendored
+164
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w32-36af842e.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=32,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split1.py
Vendored
+164
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split2.py
Vendored
+164
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_w48_horse10_256x256-split3.py
Vendored
+164
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='https://download.openmmlab.com/mmpose/'
'pretrain_models/hrnet_w48-8ef0771d.pth',
backbone=dict(
type='HRNet',
in_channels=3,
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192)),
stage4=dict(
num_modules=3,
num_branches=4,
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384))),
),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=48,
out_channels=channel_cfg['num_output_channels'],
num_deconv_layers=0,
extra=dict(final_conv_kernel=1, ),
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split1.py
Vendored
+133
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@@ -0,0 +1,133 @@
_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split2.py
Vendored
+133
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_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res101_horse10_256x256-split3.py
Vendored
+133
View File
@@ -0,0 +1,133 @@
_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=64,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split3.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split1.py
Vendored
+133
View File
@@ -0,0 +1,133 @@
_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet152',
backbone=dict(type='ResNet', depth=152),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split1.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)
third_party/hamer/third-party/ViTPose/configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/res152_horse10_256x256-split2.py
Vendored
+133
View File
@@ -0,0 +1,133 @@
_base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/horse10.py'
]
evaluation = dict(interval=10, metric='PCK', save_best='PCK')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[170, 200])
total_epochs = 210
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
channel_cfg = dict(
num_output_channels=22,
dataset_joints=22,
dataset_channel=[
[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 21
],
],
inference_channel=[
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
21
])
# model settings
model = dict(
type='TopDown',
pretrained='torchvision://resnet152',
backbone=dict(type='ResNet', depth=152),
keypoint_head=dict(
type='TopdownHeatmapSimpleHead',
in_channels=2048,
out_channels=channel_cfg['num_output_channels'],
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
train_cfg=dict(),
test_cfg=dict(
flip_test=True,
post_process='default',
shift_heatmap=True,
modulate_kernel=11))
data_cfg = dict(
image_size=[256, 256],
heatmap_size=[64, 64],
num_output_channels=channel_cfg['num_output_channels'],
num_joints=channel_cfg['dataset_joints'],
dataset_channel=channel_cfg['dataset_channel'],
inference_channel=channel_cfg['inference_channel'])
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownRandomFlip', flip_prob=0.5),
dict(
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(type='TopDownGenerateTarget', sigma=2),
dict(
type='Collect',
keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
'rotation', 'bbox_score', 'flip_pairs'
]),
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='TopDownAffine'),
dict(type='ToTensor'),
dict(
type='NormalizeTensor',
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
dict(
type='Collect',
keys=['img'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation', 'bbox_score',
'flip_pairs'
]),
]
test_pipeline = val_pipeline
data_root = 'data/horse10'
data = dict(
samples_per_gpu=32,
workers_per_gpu=2,
val_dataloader=dict(samples_per_gpu=32),
test_dataloader=dict(samples_per_gpu=32),
train=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-train-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=train_pipeline,
dataset_info={{_base_.dataset_info}}),
val=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=val_pipeline,
dataset_info={{_base_.dataset_info}}),
test=dict(
type='AnimalHorse10Dataset',
ann_file=f'{data_root}/annotations/horse10-test-split2.json',
img_prefix=f'{data_root}/',
data_cfg=data_cfg,
pipeline=test_pipeline,
dataset_info={{_base_.dataset_info}}),
)

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