Animal¶
Animalpose Dataset¶
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 | 0.736 | 0.959 | 0.832 | 0.775 | 0.966 | ckpt | log |
pose_hrnet_w48 | 256x256 | 0.737 | 0.959 | 0.823 | 0.778 | 0.962 | ckpt | log |
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 | 0.688 | 0.945 | 0.772 | 0.733 | 0.952 | ckpt | log |
pose_resnet_101 | 256x256 | 0.696 | 0.948 | 0.785 | 0.737 | 0.954 | ckpt | log |
pose_resnet_152 | 256x256 | 0.709 | 0.948 | 0.797 | 0.749 | 0.951 | ckpt | log |
Atrw Dataset¶
Topdown Heatmap + Resnet on Atrw¶
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}
}
ATRW (ACM MM'2020)
@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}
}
Results on ATRW validation set
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_resnet_50 | 256x256 | 0.900 | 0.973 | 0.932 | 0.929 | 0.985 | ckpt | log |
pose_resnet_101 | 256x256 | 0.898 | 0.973 | 0.936 | 0.927 | 0.985 | ckpt | log |
pose_resnet_152 | 256x256 | 0.896 | 0.973 | 0.931 | 0.927 | 0.985 | ckpt | log |
Topdown Heatmap + Hrnet on Atrw¶
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}
}
ATRW (ACM MM'2020)
@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}
}
Results on ATRW validation set
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_hrnet_w32 | 256x256 | 0.912 | 0.973 | 0.959 | 0.938 | 0.985 | ckpt | log |
pose_hrnet_w48 | 256x256 | 0.911 | 0.972 | 0.946 | 0.937 | 0.985 | ckpt | log |
Fly Dataset¶
Topdown Heatmap + Resnet on Fly¶
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}
}
Vinegar Fly (Nature Methods'2019)
@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}
}
Results on Vinegar Fly test set
Arch | Input Size | PCK@0.2 | AUC | EPE | ckpt | log |
---|---|---|---|---|---|---|
pose_resnet_50 | 192x192 | 0.996 | 0.910 | 2.00 | ckpt | log |
pose_resnet_101 | 192x192 | 0.996 | 0.912 | 1.95 | ckpt | log |
pose_resnet_152 | 192x192 | 0.997 | 0.917 | 1.78 | ckpt | log |
Horse10 Dataset¶
Topdown Heatmap + Hrnet on Horse10¶
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}
}
Horse-10 (WACV'2021)
@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}
}
Results on Horse-10 test set
Set | Arch | Input Size | PCK@0.3 | NME | ckpt | log |
---|---|---|---|---|---|---|
split1 | pose_hrnet_w32 | 256x256 | 0.951 | 0.122 | ckpt | log |
split2 | pose_hrnet_w32 | 256x256 | 0.949 | 0.116 | ckpt | log |
split3 | pose_hrnet_w32 | 256x256 | 0.939 | 0.153 | ckpt | log |
split1 | pose_hrnet_w48 | 256x256 | 0.973 | 0.095 | ckpt | log |
split2 | pose_hrnet_w48 | 256x256 | 0.969 | 0.101 | ckpt | log |
split3 | pose_hrnet_w48 | 256x256 | 0.961 | 0.128 | ckpt | log |
Topdown Heatmap + Resnet on Horse10¶
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}
}
HRNet (CVPR'2019)
@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}
}
Results on Horse-10 test set
Set | Arch | Input Size | PCK@0.3 | NME | ckpt | log |
---|---|---|---|---|---|---|
split1 | pose_resnet_50 | 256x256 | 0.956 | 0.113 | ckpt | log |
split2 | pose_resnet_50 | 256x256 | 0.954 | 0.111 | ckpt | log |
split3 | pose_resnet_50 | 256x256 | 0.946 | 0.129 | ckpt | log |
split1 | pose_resnet_101 | 256x256 | 0.958 | 0.115 | ckpt | log |
split2 | pose_resnet_101 | 256x256 | 0.955 | 0.115 | ckpt | log |
split3 | pose_resnet_101 | 256x256 | 0.946 | 0.126 | ckpt | log |
split1 | pose_resnet_152 | 256x256 | 0.969 | 0.105 | ckpt | log |
split2 | pose_resnet_152 | 256x256 | 0.970 | 0.103 | ckpt | log |
split3 | pose_resnet_152 | 256x256 | 0.957 | 0.131 | 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 | 0.999 | 0.899 | 2.27 | ckpt | log |
pose_resnet_101 | 160x160 | 0.999 | 0.907 | 2.03 | ckpt | log |
pose_resnet_152 | 160x160 | 1.000 | 0.926 | 1.48 | ckpt | log |
Macaque Dataset¶
Topdown Heatmap + Hrnet on Macaque¶
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}
}
MacaquePose (bioRxiv'2020)
@article{labuguen2020macaquepose,
title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
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},
journal={bioRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory}
}
Results on MacaquePose with ground-truth detection bounding boxes
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_hrnet_w32 | 256x192 | 0.814 | 0.953 | 0.918 | 0.851 | 0.969 | ckpt | log |
pose_hrnet_w48 | 256x192 | 0.818 | 0.963 | 0.917 | 0.855 | 0.971 | ckpt | log |
Topdown Heatmap + Resnet on Macaque¶
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}
}
MacaquePose (bioRxiv'2020)
@article{labuguen2020macaquepose,
title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
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},
journal={bioRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory}
}
Results on MacaquePose with ground-truth detection bounding boxes
Arch | Input Size | AP | AP50 | AP75 | AR | AR50 | ckpt | log |
---|---|---|---|---|---|---|---|---|
pose_resnet_50 | 256x192 | 0.799 | 0.952 | 0.919 | 0.837 | 0.964 | ckpt | log |
pose_resnet_101 | 256x192 | 0.790 | 0.953 | 0.908 | 0.828 | 0.967 | ckpt | log |
pose_resnet_152 | 256x192 | 0.794 | 0.951 | 0.915 | 0.834 | 0.968 | ckpt | log |
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 | 1.86 | ckpt | log |
pose_resnet_101 | 160x160 | 1.000 | 0.916 | 1.82 | ckpt | log |
pose_resnet_152 | 160x160 | 1.000 | 0.921 | 1.66 | ckpt | log |