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license: cc-by-nc-sa-4.0
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# Dataset Card for Horse-30

## Dataset Description

- **Homepage:** horse10.deeplabcut.org
- **Repository:** https://github.com/DeepLabCut/DeepLabCut
- **Paper:** Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.},     title     = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation},     booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},     month     = {January},     year      = {2021},     pages     = {1859-1868} 
- **Leaderboard:** https://paperswithcode.com/sota/animal-pose-estimation-on-horse-10?p=pretraining-boosts-out-of-domain-robustness
- **Point of Contact:** Mackenzie Mathis

### Dataset Summary

Pose estimation is an important tool for measuring behavior, and thus widely used in technology, medicine and biology. Due to innovations in both deep learning algorithms and large-scale datasets pose estimation on humans has gotten very powerful. However, typical human pose estimation benchmarks, such as MPII pose and COCO, contain many different individuals (>10K) in different contexts, but only very few example postures per individual. In real world application of pose estimation, users want to estimate the location of user-defined bodyparts by only labeling a few hundred frames on a small subset of individuals, yet want this to generalize to new individuals. Thus, one naturally asks the following question: Assume you have trained an algorithm that performs with high accuracy on a given (individual) animal for the whole repertoire of movement  - how well will it generalize to different individuals that have slightly or a dramatically different appearance? Unlike in common human pose estimation benchmarks here the setting is that datasets have many (annotated) poses per individual (>200) but only few individuals (1-25).   
To allow the field to tackle this challenge, we developed a novel benchmark, called Horse-10, comprising 30 diverse Thoroughbred horses, for which 22 body parts were labeled by an expert in 8,114 frames. Horses have various coat colors and the “in-the-wild” aspect of the collected data at various Thoroughbred yearling sales and farms added additional complexity. 

- **Homepage:** horse10.deeplabcut.org
- **Repository:** https://github.com/DeepLabCut/DeepLabCut
- **Paper:** `{Mathis, Alexander and Biasi, Thomas and Schneider, Steffen and Yuksekgonul, Mert and Rogers, Byron and Bethge, Matthias and Mathis, Mackenzie W.},     title     = {Pretraining Boosts Out-of-Domain Robustness for Pose Estimation},     booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},     month     = {January},     year      = {2021},     pages     = {1859-1868} `
- **Leaderboard:** https://paperswithcode.com/sota/animal-pose-estimation-on-horse-10?p=pretraining-boosts-out-of-domain-robustness
- **Point of Contact:** Mackenzie Mathis

### Supported Tasks and Leaderboards

Horse-10 task: Train on a subset of individuals (10) and evaluate on held-out “out-of-domain” horses (20).

### Languages

Python, deeplabcut, tensorflow, pytorch

## Dataset Structure

### Data Instances

Over 8,000 expertly labeled frames across 30 individual thoroughbred horses

### Data Splits

The ground truth training data is provided as 3 splits of 10 Horses each. The download provides you a project compatible with loading into the deeplabcut framework, but ground truth labels/training data can be easily loaded in pandas to accommodate your framework (example loader here). 

Please do NOT train on all three splits simultaneously. You must train independently (as some horses can be considered out-of-domain in other splits for evaluation!). Integrity matters!

The download also includes all of Horse-30 images and annotations (thus is ~850MB).