---
license: cc-by-4.0
task_categories:
  - keypoint-detection
tags:
  - libreyolo
  - pose
  - animal-pose
  - ap10k
pretty_name: AP-10K Animal Pose (LibreYOLO)
size_categories:
  - 1K<n<10K
---

# AP-10K Animal Pose (LibreYOLO)

Multi-class animal pose in LibreYOLO YOLO-pose layout: detect and classify each
animal into one of **54 species** while predicting a single shared **17-keypoint
quadruped skeleton**. Hosted so `model.train(...)` / `model.val(...)` can consume
it directly for multi-class pose (one `kpt_shape` for every class).

## Provenance

- **Source**: AP-10K, *A Benchmark for Animal Pose Estimation in the Wild*
  (Yu et al., NeurIPS 2021 Datasets and Benchmarks Track).
  Upstream: https://github.com/AlexTheBad/AP-10K (canonical release,
  [Google Drive labeled set](https://drive.google.com/file/d/1-FNNGcdtAQRehYYkGY1y4wzFNg4iWNad/view)).
- **Pinned source `sha256`**: `420980abb135d6f66bcc8e29f289a46081214016192ae197ad24bc1525c8e62c`
  (the upstream `ap-10k` archive).
- **Transform applied**: extracted the archive; converted the COCO-style
  keypoint split (`split1`) to YOLO-pose TXT, writing **one class per species**
  over the shared 17-keypoint skeleton; copied images into
  `images/{train,val}`; generated `ap10k-pose.yaml`
  (`kpt_shape`, `flip_idx`, `skeleton`, `oks_sigmas`). No third-party
  repackaging used.

## Contents

```
ap10k-pose/
├── images/train/*.jpg   (7023)
├── images/val/*.jpg     (995)
├── labels/train/*.txt   (7023)   # <cls> <cx> <cy> <w> <h> (<x> <y> <v>)*17, normalized
├── labels/val/*.txt     (995)
└── ap10k-pose.yaml
```

- **Splits** (AP-10K `split1`): 7,023 train images / 9,122 instances,
  995 val images / 1,272 instances.
- **Classes**: 54 species across 23 families (Bovidae, Canidae, Castoridae,
  Cercopithecidae, Cervidae, Cricetidae, Elephantidae, Equidae, Felidae,
  Giraffidae, Hippopotamidae, Hominidae, Leporidae, Mephitidae, Muridae,
  Mustelidae, Procyonidae, Rhinocerotidae, Sciuridae, Suidae, Talpidae,
  Ursidae, Vespertilionidae). Class index = species, ordered by upstream
  category id (see `names` in the yaml).
- **Keypoints (17, shared)**: left_eye, right_eye, nose, neck, root_of_tail,
  left/right shoulder, elbow, front_paw, left/right hip, knee, back_paw.

## Use with LibreYOLO

```python
from libreyolo import LibreYOLONAS
model = LibreYOLONAS("yolo_nas_pose_s_coco_pose.pth", size="s", task="pose")
model.train(data="ap10k-pose.yaml", epochs=100, imgsz=640)
model.val(data="ap10k-pose.yaml")
```

## License

Source license: **CC BY 4.0** (Creative Commons Attribution 4.0 International),
inherited. Please attribute AP-10K:

> Hang Yu, Yufei Xu, Jing Zhang, Wei Zhao, Ziyu Guan, Dacheng Tao.
> "AP-10K: A Benchmark for Animal Pose Estimation in the Wild." NeurIPS 2021
> Datasets and Benchmarks Track. https://github.com/AlexTheBad/AP-10K