Animal Models¶
Deep high-resolution representation learning for human pose estimation¶
Introduction¶
@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}
}
Results and models¶
2d Animal Landmark Detection¶
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 |
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 |
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 |
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 | 0.911 | 0.972 | 0.946 | 0.937 | 0.985 | ckpt | log |
Simple baselines for human pose estimation and tracking¶
Introduction¶
@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}
}
Results and models¶
2d Animal Landmark Detection¶
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 |
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 |
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 |
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 |
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 |
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 |
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 |