Animal-Pose
2D Box
2D Keypoints
Pose Estimation
|Animal
|...
License: Unknown

Overview

This dataset provides animal pose annotations on five categories are provided: dog, cat, cow, horse, sheep, with in total 6,000+ instances in 4,000+ images. Besides, the dataset also contains bounding box annotations for other 7 animal categories. Find details in the paper.

We annotate in total 20 keypoints: Two eyes, Throat, Nose, Withers, Two Earbases, Tailbase, Four Elbows, Four Knees, Four Paws. We select some samples from this dataset. The first figure represents keypoint-labeled animal instances from five animal categories. The second figure contains some animal images with only bounding box labeled from seven different categories: otter, bobcat, rhino, hippo, chimpanzee, bear and antelope.

Citation

Please use the following citation when referencing the dataset:

@misc{cao2019crossdomain,
      title={Cross-Domain Adaptation for Animal Pose Estimation},
      author={Jinkun Cao and Hongyang Tang and Hao-Shu Fang and Xiaoyong Shen and Cewu Lu and
Yu-Wing Tai},
      year={2019},
      eprint={1908.05806},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Data Summary
Type
Image,
Amount
--
Size
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Provided by
MoE Key Lab of Artificial Intelligence, AIInstitute, Shanghai Jiao Tong University
实验室以新一代机器学习、智能感知认知、人工智能芯片、大数据智能分析为研究方向,这四个方面形成有机整体,新一代机器学习是开拓人工智能理论前沿的基础,智能感知认知是创新人工智能算法的关键,人工智能芯片是提升人工智能算力的支撑,大数据智能分析是提升异质非结构化数据价值、拓展人工智能应用场景的重要工具。
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