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license: cc-by-nc-sa-4.0 |
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# Dataset Card for Horse-30 |
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## Dataset Description |
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- **Homepage:** horse10.deeplabcut.org |
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- **Repository:** https://github.com/DeepLabCut/DeepLabCut |
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- **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} |
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- **Leaderboard:** https://paperswithcode.com/sota/animal-pose-estimation-on-horse-10?p=pretraining-boosts-out-of-domain-robustness |
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- **Point of Contact:** Mackenzie Mathis |
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### Dataset Summary |
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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). |
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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. |
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- **Homepage:** horse10.deeplabcut.org |
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- **Repository:** https://github.com/DeepLabCut/DeepLabCut |
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- **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} ` |
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- **Leaderboard:** https://paperswithcode.com/sota/animal-pose-estimation-on-horse-10?p=pretraining-boosts-out-of-domain-robustness |
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- **Point of Contact:** Mackenzie Mathis |
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### Supported Tasks and Leaderboards |
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Horse-10 task: Train on a subset of individuals (10) and evaluate on held-out “out-of-domain” horses (20). |
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### Languages |
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Python, deeplabcut, tensorflow, pytorch |
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## Dataset Structure |
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### Data Instances |
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Over 8,000 expertly labeled frames across 30 individual thoroughbred horses |
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### Data Splits |
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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). |
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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! |
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The download also includes all of Horse-30 images and annotations (thus is ~850MB). |
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