mirror of https://github.com/open-mmlab/mmpose
536 lines
19 KiB
Markdown
536 lines
19 KiB
Markdown
# 2D Animal Keypoint Dataset
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It is recommended to symlink the dataset root to `$MMPOSE/data`.
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If your folder structure is different, you may need to change the corresponding paths in config files.
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MMPose supported datasets:
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- [Animal-Pose](#animal-pose) \[ [Homepage](https://sites.google.com/view/animal-pose/) \]
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- [AP-10K](#ap-10k) \[ [Homepage](https://github.com/AlexTheBad/AP-10K/) \]
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- [Horse-10](#horse-10) \[ [Homepage](http://www.mackenziemathislab.org/horse10) \]
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- [MacaquePose](#macaquepose) \[ [Homepage](http://pri.ehub.kyoto-u.ac.jp/datasets/macaquepose/index.html) \]
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- [Vinegar Fly](#vinegar-fly) \[ [Homepage](https://github.com/jgraving/DeepPoseKit-Data) \]
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- [Desert Locust](#desert-locust) \[ [Homepage](https://github.com/jgraving/DeepPoseKit-Data) \]
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- [Grévy’s Zebra](#grvys-zebra) \[ [Homepage](https://github.com/jgraving/DeepPoseKit-Data) \]
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- [ATRW](#atrw) \[ [Homepage](https://cvwc2019.github.io/challenge.html) \]
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- [Animal Kingdom](#Animal-Kindom) \[ [Homepage](https://openaccess.thecvf.com/content/CVPR2022/html/Ng_Animal_Kingdom_A_Large_and_Diverse_Dataset_for_Animal_Behavior_CVPR_2022_paper.html) \]
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## Animal-Pose
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<!-- [DATASET] -->
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<details>
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<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>
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```bibtex
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@InProceedings{Cao_2019_ICCV,
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author = {Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing},
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title = {Cross-Domain Adaptation for Animal Pose Estimation},
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booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
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month = {October},
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year = {2019}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227796953-95ae1e30-5323-43f8-9a19-c4c2326e9835.png" height="200px">
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</div>
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For [Animal-Pose](https://sites.google.com/view/animal-pose/) dataset, we prepare the dataset as follows:
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1. Download the images of [PASCAL VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/#data), especially the five categories (dog, cat, sheep, cow, horse), which we use as trainval dataset.
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2. Download the [test-set](https://drive.google.com/drive/folders/1DwhQobZlGntOXxdm7vQsE4bqbFmN3b9y?usp=sharing) images with raw annotations (1000 images, 5 categories).
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3. We have pre-processed the annotations to make it compatible with MMPose. Please download the annotation files from [annotations](https://download.openmmlab.com/mmpose/datasets/animalpose_annotations.tar). If you would like to generate the annotations by yourself, please check our dataset parsing [codes](/tools/dataset_converters/parse_animalpose_dataset.py).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── animalpose
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│
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│-- VOC2012
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│ │-- Annotations
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│ │-- ImageSets
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│ │-- JPEGImages
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│ │-- SegmentationClass
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│ │-- SegmentationObject
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│
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│-- animalpose_image_part2
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│ │-- cat
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│ │-- cow
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│ │-- dog
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│ │-- horse
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│ │-- sheep
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│
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│-- annotations
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│ │-- animalpose_train.json
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│ |-- animalpose_val.json
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│ |-- animalpose_trainval.json
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│ │-- animalpose_test.json
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│
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│-- PASCAL2011_animal_annotation
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│ │-- cat
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│ │ |-- 2007_000528_1.xml
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│ │ |-- 2007_000549_1.xml
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│ │ │-- ...
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│ │-- cow
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│ │-- dog
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│ │-- horse
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│ │-- sheep
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│
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│-- annimalpose_anno2
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│ │-- cat
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│ │ |-- ca1.xml
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│ │ |-- ca2.xml
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│ │ │-- ...
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│ │-- cow
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│ │-- dog
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│ │-- horse
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│ │-- sheep
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```
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The official dataset does not provide the official train/val/test set split.
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We choose the images from PascalVOC for train & val. In total, we have 3608 images and 5117 annotations for train+val, where
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2798 images with 4000 annotations are used for training, and 810 images with 1117 annotations are used for validation.
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Those images from other sources (1000 images with 1000 annotations) are used for testing.
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## AP-10K
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://arxiv.org/abs/2108.12617">AP-10K (NeurIPS'2021)</a></summary>
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```bibtex
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@misc{yu2021ap10k,
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title={AP-10K: A Benchmark for Animal Pose Estimation in the Wild},
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author={Hang Yu and Yufei Xu and Jing Zhang and Wei Zhao and Ziyu Guan and Dacheng Tao},
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year={2021},
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eprint={2108.12617},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227797151-091dc21a-d944-49c9-8b62-cc47fa89e69f.png" height="200px">
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</div>
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For [AP-10K](https://github.com/AlexTheBad/AP-10K/) dataset, images and annotations can be downloaded from [download](https://drive.google.com/file/d/1-FNNGcdtAQRehYYkGY1y4wzFNg4iWNad/view?usp=sharing).
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Note, this data and annotation data is for non-commercial use only.
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── ap10k
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│-- annotations
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│ │-- ap10k-train-split1.json
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│ |-- ap10k-train-split2.json
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│ |-- ap10k-train-split3.json
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│ │-- ap10k-val-split1.json
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│ |-- ap10k-val-split2.json
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│ |-- ap10k-val-split3.json
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│ |-- ap10k-test-split1.json
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│ |-- ap10k-test-split2.json
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│ |-- ap10k-test-split3.json
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│-- data
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│ │-- 000000000001.jpg
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│ │-- 000000000002.jpg
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│ │-- ...
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```
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The annotation files in 'annotation' folder contains 50 labeled animal species. There are total 10,015 labeled images with 13,028 instances in the AP-10K dataset. We randonly split them into train, val, and test set following the ratio of 7:1:2.
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## Horse-10
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<!-- [DATASET] -->
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<details>
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<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>
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```bibtex
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@inproceedings{mathis2021pretraining,
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title={Pretraining boosts out-of-domain robustness for pose estimation},
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author={Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W},
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booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
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pages={1859--1868},
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year={2021}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227797934-32bc1b2c-7957-4a29-94df-8e431842ab3b.png" height="200px">
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</div>
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For [Horse-10](http://www.mackenziemathislab.org/horse10) dataset, images can be downloaded from [download](http://www.mackenziemathislab.org/horse10).
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Please download the annotation files from [horse10_annotations](https://download.openmmlab.com/mmpose/datasets/horse10_annotations.tar). Note, this data and annotation data is for non-commercial use only, per the authors (see http://horse10.deeplabcut.org for more information).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── horse10
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│-- annotations
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│ │-- horse10-train-split1.json
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│ |-- horse10-train-split2.json
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│ |-- horse10-train-split3.json
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│ │-- horse10-test-split1.json
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│ |-- horse10-test-split2.json
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│ |-- horse10-test-split3.json
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│-- labeled-data
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│ │-- BrownHorseinShadow
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│ │-- BrownHorseintoshadow
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│ │-- ...
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```
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## MacaquePose
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/pmc7874091/">MacaquePose (bioRxiv'2020)</a></summary>
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```bibtex
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@article{labuguen2020macaquepose,
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title={MacaquePose: A novel ‘in the wild’macaque monkey pose dataset for markerless motion capture},
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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},
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journal={bioRxiv},
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year={2020},
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publisher={Cold Spring Harbor Laboratory}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227799576-f10f8469-9432-4139-beb4-195037dee72c.png" height="200px">
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</div>
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For [MacaquePose](http://pri.ehub.kyoto-u.ac.jp/datasets/macaquepose/index.html) dataset, images can be downloaded from [download](http://pri.ehub.kyoto-u.ac.jp/datasets/macaquepose/download.php).
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Please download the annotation files from [macaque_annotations](https://download.openmmlab.com/mmpose/datasets/macaque_annotations.tar).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── macaque
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│-- annotations
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│ │-- macaque_train.json
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│ |-- macaque_test.json
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│-- images
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│ │-- 01418849d54b3005.jpg
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│ │-- 0142d1d1a6904a70.jpg
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│ │-- 01ef2c4c260321b7.jpg
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│ │-- 020a1c75c8c85238.jpg
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│ │-- 020b1506eef2557d.jpg
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│ │-- ...
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```
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Since the official dataset does not provide the test set, we randomly select 12500 images for training, and the rest for evaluation (see [code](/tools/dataset/parse_macaquepose_dataset.py)).
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## Vinegar Fly
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://www.nature.com/articles/s41592-018-0234-5">Vinegar Fly (Nature Methods'2019)</a></summary>
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```bibtex
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@article{pereira2019fast,
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title={Fast animal pose estimation using deep neural networks},
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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},
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journal={Nature methods},
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volume={16},
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number={1},
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pages={117--125},
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year={2019},
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publisher={Nature Publishing Group}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227802774-bb4e4ef2-2ade-42ad-80f1-97f2a7faa9e2.png" height="200px">
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</div>
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For [Vinegar Fly](https://github.com/jgraving/DeepPoseKit-Data) dataset, images can be downloaded from [vinegar_fly_images](https://download.openmmlab.com/mmpose/datasets/vinegar_fly_images.tar).
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Please download the annotation files from [vinegar_fly_annotations](https://download.openmmlab.com/mmpose/datasets/vinegar_fly_annotations.tar).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── fly
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│-- annotations
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│ │-- fly_train.json
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│ |-- fly_test.json
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│-- images
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│ │-- 0.jpg
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│ │-- 1.jpg
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│ │-- 2.jpg
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│ │-- 3.jpg
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│ │-- ...
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```
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Since the official dataset does not provide the test set, we randomly select 90% images for training, and the rest (10%) for evaluation (see [code](/tools/dataset_converters/parse_deepposekit_dataset.py)).
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## Desert Locust
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://elifesciences.org/articles/47994">Desert Locust (Elife'2019)</a></summary>
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```bibtex
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@article{graving2019deepposekit,
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title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
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author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
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journal={Elife},
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volume={8},
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pages={e47994},
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year={2019},
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publisher={eLife Sciences Publications Limited}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227802779-09d0ec8c-8971-4c67-a315-e2d1355f7f72.png" height="200px">
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</div>
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For [Desert Locust](https://github.com/jgraving/DeepPoseKit-Data) dataset, images can be downloaded from [locust_images](https://download.openmmlab.com/mmpose/datasets/locust_images.tar).
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Please download the annotation files from [locust_annotations](https://download.openmmlab.com/mmpose/datasets/locust_annotations.tar).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── locust
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│-- annotations
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│ │-- locust_train.json
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│ |-- locust_test.json
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│-- images
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│ │-- 0.jpg
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│ │-- 1.jpg
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│ │-- 2.jpg
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│ │-- 3.jpg
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│ │-- ...
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```
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Since the official dataset does not provide the test set, we randomly select 90% images for training, and the rest (10%) for evaluation (see [code](/tools/dataset_converters/parse_deepposekit_dataset.py)).
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## Grévy’s Zebra
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://elifesciences.org/articles/47994">Grévy’s Zebra (Elife'2019)</a></summary>
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```bibtex
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@article{graving2019deepposekit,
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title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
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author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
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journal={Elife},
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volume={8},
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pages={e47994},
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year={2019},
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publisher={eLife Sciences Publications Limited}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227802783-ace952bb-1ff9-4720-80a8-c63cc9e714b6.png" height="200px">
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</div>
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For [Grévy’s Zebra](https://github.com/jgraving/DeepPoseKit-Data) dataset, images can be downloaded from [zebra_images](https://download.openmmlab.com/mmpose/datasets/zebra_images.tar).
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Please download the annotation files from [zebra_annotations](https://download.openmmlab.com/mmpose/datasets/zebra_annotations.tar).
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Extract them under {MMPose}/data, and make them look like this:
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```text
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mmpose
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├── mmpose
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├── docs
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├── tests
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├── tools
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├── configs
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`── data
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│── zebra
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│-- annotations
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│ │-- zebra_train.json
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│ |-- zebra_test.json
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│-- images
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│ │-- 0.jpg
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│ │-- 1.jpg
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│ │-- 2.jpg
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│ │-- 3.jpg
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│ │-- ...
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```
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Since the official dataset does not provide the test set, we randomly select 90% images for training, and the rest (10%) for evaluation (see [code](/tools/dataset_converters/parse_deepposekit_dataset.py)).
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## ATRW
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<!-- [DATASET] -->
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<details>
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<summary align="right"><a href="https://arxiv.org/abs/1906.05586">ATRW (ACM MM'2020)</a></summary>
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```bibtex
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@inproceedings{li2020atrw,
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title={ATRW: A Benchmark for Amur Tiger Re-identification in the Wild},
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author={Li, Shuyuan and Li, Jianguo and Tang, Hanlin and Qian, Rui and Lin, Weiyao},
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booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
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pages={2590--2598},
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year={2020}
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}
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```
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</details>
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<div align="center">
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<img src="https://user-images.githubusercontent.com/100993824/227797386-fce99241-8a0e-4a40-a179-dad013e6c5a4.png" height="200px">
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</div>
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ATRW captures images of the Amur tiger (also known as Siberian tiger, Northeast-China tiger) in the wild.
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For [ATRW](https://cvwc2019.github.io/challenge.html) dataset, please download images from
|
||
[Pose_train](https://lilablobssc.blob.core.windows.net/cvwc2019/train/atrw_pose_train.tar.gz),
|
||
[Pose_val](https://lilablobssc.blob.core.windows.net/cvwc2019/train/atrw_pose_val.tar.gz), and
|
||
[Pose_test](https://lilablobssc.blob.core.windows.net/cvwc2019/test/atrw_pose_test.tar.gz).
|
||
Note that in the ATRW official annotation files, the key "file_name" is written as "filename". To make it compatible with
|
||
other coco-type json files, we have modified this key.
|
||
Please download the modified annotation files from [atrw_annotations](https://download.openmmlab.com/mmpose/datasets/atrw_annotations.tar).
|
||
Extract them under {MMPose}/data, and make them look like this:
|
||
|
||
```text
|
||
mmpose
|
||
├── mmpose
|
||
├── docs
|
||
├── tests
|
||
├── tools
|
||
├── configs
|
||
`── data
|
||
│── atrw
|
||
│-- annotations
|
||
│ │-- keypoint_train.json
|
||
│ │-- keypoint_val.json
|
||
│ │-- keypoint_trainval.json
|
||
│-- images
|
||
│ │-- train
|
||
│ │ │-- 000002.jpg
|
||
│ │ │-- 000003.jpg
|
||
│ │ │-- ...
|
||
│ │-- val
|
||
│ │ │-- 000001.jpg
|
||
│ │ │-- 000013.jpg
|
||
│ │ │-- ...
|
||
│ │-- test
|
||
│ │ │-- 000000.jpg
|
||
│ │ │-- 000004.jpg
|
||
│ │ │-- ...
|
||
```
|
||
|
||
## Animal Kingdom
|
||
|
||
<details>
|
||
<summary align="right"><a href="https://arxiv.org/abs/2204.08129">Animal Kingdom (CVPR'2022)</a></summary>
|
||
</details>
|
||
<div align="center">
|
||
<img src="https://github.com/open-mmlab/mmpose/assets/53283758/8591989e-91fa-4f6e-99c8-48b614de862e" height="200px">
|
||
</div>
|
||
|
||
```bibtex
|
||
@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}
|
||
}
|
||
```
|
||
|
||
For [Animal Kingdom](https://github.com/sutdcv/Animal-Kingdom) dataset, images can be downloaded from [here](https://forms.office.com/pages/responsepage.aspx?id=drd2NJDpck-5UGJImDFiPVRYpnTEMixKqPJ1FxwK6VZUQkNTSkRISTNORUI2TDBWMUpZTlQ5WUlaSyQlQCN0PWcu).
|
||
Please Extract dataset under {MMPose}/data, and make them look like this:
|
||
|
||
```text
|
||
mmpose
|
||
├── mmpose
|
||
├── docs
|
||
├── tests
|
||
├── tools
|
||
├── configs
|
||
`── data
|
||
│── ak
|
||
|--annotations
|
||
│ │-- ak_P1
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P2
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P3_amphibian
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P3_bird
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P3_fish
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P3_mammal
|
||
│ │ │-- train.json
|
||
│ │ │-- test.json
|
||
│ │-- ak_P3_reptile
|
||
│ │-- train.json
|
||
│ │-- test.json
|
||
│-- images
|
||
│ │-- AAACXZTV
|
||
│ │ │--AAACXZTV_f000059.jpg
|
||
│ │ │--...
|
||
│ │-- AAAUILHH
|
||
│ │ │--AAAUILHH_f000098.jpg
|
||
│ │ │--...
|
||
│ │-- ...
|
||
```
|