Amur Tiger Re-identification

This dataset contains more than 8,000 video clips of 92 individual Amur tigers from 10 zoos in China. Around 9500 bounding boxes are provided along with pose keypoints, and around 3600 of those bounding boxes are associated with an individual tiger ID. This data set was originally published as part of the Re-identification challenge at the ICCV 2019 Workshop on Computer Vision for Wildlife Conservation; suggested train/val/test splits correspond to those used for the competition.

Data access

Data typeSplitImagesAnnotations
Detection train Detection train images (2GB) Detection train annotations
test Detection test images (1.4GB)
Pose train Pose train images (255MB)
Pose val images (38MB)
Pose train/val annotations
test Pose test images (70MB)
Re-identification train Re-ID train images (132MB) Re-ID train annotations
test Re-ID test images (90MB) Re-ID test annotations

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Data format

All annotation tar files include files with detailed format information; this section provides a high-level summary only.


Bounding boxes are provided in Pascal VOC format.


Pose annotations are provided in COCO format. Annotations use the COCO “keypoint” annotation type, with categories like “left_ear”, “right_ear”, “nose”, etc.


Identifications in the “train” set are provided as a .csv-formatted list of [ID,filename] pairs; the “test” set contains only a list of images requiring identification. Pose annotations are provided for both sets.

The competition for which this dataset was prepared divided re-identification into two tasks, one (“plain re-ID”) where pose and bounding box annotations were available, and one (“wild re-ID”) where annotations were not available.


This data set is released under the CC BY-NC-SA 4.0 License. Images are owned by MakerCollider and the World Wildlife Fund.


If you use this dataset, please cite the associated arXiv publication:

Li, S., Li, J., Lin, W., & Tang, H. (2019). Amur Tiger Re-identification in the Wild. arXiv preprint arXiv:1906.05586.


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Posted by Dan Morris.