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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).
+
+