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Animal 2D Keypoint



Zebra Dataset


Topdown Heatmap + Resnet on Zebra

SimpleBaseline2D (ECCV'2018) 
@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}
}
Grévy’s Zebra (Elife'2019) 
@article{graving2019deepposekit,
  title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
  author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
  journal={Elife},
  volume={8},
  pages={e47994},
  year={2019},
  publisher={eLife Sciences Publications Limited}
}

Results on Grévy’s Zebra test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 160x160 1.000 0.914 1.87 ckpt log
pose_resnet_101 160x160 1.000 0.915 1.83 ckpt log
pose_resnet_152 160x160 1.000 0.921 1.67 ckpt log


Ap10k Dataset


Topdown Heatmap + Resnet on Ap10k

SimpleBaseline2D (ECCV'2018) 
@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}
}
AP-10K (NeurIPS'2021) 
@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}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
pose_resnet_50 256x256 0.680 0.926 0.738 0.552 0.687 ckpt log
pose_resnet_101 256x256 0.681 0.921 0.751 0.545 0.690 ckpt log

Topdown Heatmap + Cspnext + Udp on Ap10k

RTMDet (ArXiv 2022) 
@misc{lyu2022rtmdet,
      title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
      author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen},
      year={2022},
      eprint={2212.07784},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
UDP (CVPR'2020) 
@InProceedings{Huang_2020_CVPR,
  author = {Huang, Junjie and Zhu, Zheng and Guo, Feng and Huang, Guan},
  title = {The Devil Is in the Details: Delving Into Unbiased Data Processing for Human Pose Estimation},
  booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2020}
}
AP-10K (NeurIPS'2021) 
@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}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
pose_cspnext_m 256x256 0.703 0.944 0.776 0.513 0.710 ckpt log

Topdown Heatmap + Hrnet on Ap10k

HRNet (CVPR'2019) 
@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}
}
AP-10K (NeurIPS'2021) 
@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}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
pose_hrnet_w32 256x256 0.722 0.935 0.789 0.557 0.729 ckpt log
pose_hrnet_w48 256x256 0.728 0.936 0.802 0.577 0.735 ckpt log

Rtmpose + Rtmpose on Ap10k

AP-10K (NeurIPS'2021) 
@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}
}

Results on AP-10K validation set

Arch Input Size AP AP50 AP75 APM APL ckpt log
rtmpose-m 256x256 0.722 0.939 0.788 0.569 0.728 ckpt log


Animalpose Dataset


Topdown Heatmap + Resnet on Animalpose

SimpleBaseline2D (ECCV'2018) 
@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}
}
Animal-Pose (ICCV'2019) 
@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}
}

Results on AnimalPose validation set (1117 instances)

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_resnet_50 256x256 0.691 0.947 0.770 0.736 0.955 ckpt log
pose_resnet_101 256x256 0.696 0.948 0.774 0.736 0.951 ckpt log
pose_resnet_152 256x256 0.704 0.938 0.786 0.748 0.946 ckpt log

Topdown Heatmap + Hrnet on Animalpose

HRNet (CVPR'2019) 
@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}
}
Animal-Pose (ICCV'2019) 
@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}
}

Results on AnimalPose validation set (1117 instances)

Arch Input Size AP AP50 AP75 AR AR50 ckpt log
pose_hrnet_w32 256x256 0.740 0.959 0.833 0.780 0.965 ckpt log
pose_hrnet_w48 256x256 0.738 0.958 0.831 0.778 0.962 ckpt log


Locust Dataset


Topdown Heatmap + Resnet on Locust

SimpleBaseline2D (ECCV'2018) 
@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}
}
Desert Locust (Elife'2019) 
@article{graving2019deepposekit,
  title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
  author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
  journal={Elife},
  volume={8},
  pages={e47994},
  year={2019},
  publisher={eLife Sciences Publications Limited}
}

Results on Desert Locust test set

Arch Input Size PCK@0.2 AUC EPE ckpt log
pose_resnet_50 160x160 1.000 0.900 2.27 ckpt log
pose_resnet_101 160x160 1.000 0.907 2.03 ckpt log
pose_resnet_152 160x160 1.000 0.925 1.49 ckpt log


Ak Dataset


Topdown Heatmap + Hrnet + Animalkingdom on Ak

HRNet (CVPR'2019) 
@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}
}
AnimalKingdom (CVPR'2022) 
@InProceedings{
    Ng_2022_CVPR,
    author    = {Ng, Xun Long and Ong, Kian Eng and Zheng, Qichen and Ni, Yun and Yeo, Si Yong and Liu, Jun},
    title     = {Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {19023-19034}
 }

Results on AnimalKingdom validation set

Arch Input Size PCK(0.05) Official Repo Paper ckpt log
P1_hrnet_w32 256x256 0.6323 0.6342 0.6606 ckpt log
P2_hrnet_w32 256x256 0.3741 0.3726 0.393 ckpt log
P3_mammals_hrnet_w32 256x256 0.571 0.5719 0.6159 ckpt log
P3_amphibians_hrnet_w32 256x256 0.5358 0.5432 0.5674 ckpt log
P3_reptiles_hrnet_w32 256x256 0.51 0.5 0.5606 ckpt log
P3_birds_hrnet_w32 256x256 0.7671 0.7636 0.7735 ckpt log
P3_fishes_hrnet_w32 256x256 0.6406 0.636 0.6825 ckpt log
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