---
license: mit
pretty_name: mice_hc (SLEAP → YOLO Pose via Annolid)
language:
  - en
tags:
  - animal-pose-estimation
  - pose
  - keypoints
  - mice
  - sleap
  - yolo
  - annolid 
---

# mice_hc — Home-cage mice pose (SLEAP) converted to YOLO pose format with Annolid (https://github.com/healthonrails/annolid)

## Dataset Summary

**mice_hc** is a two-animal pose dataset consisting of **pairs of male and female white Swiss Webster mice** recorded from an **overhead home-cage** view with **light bedding**. The animals are **low contrast** relative to background, which makes it a useful benchmark for robust pose estimation in challenging conditions.

This Hugging Face dataset provides the original split labels (Train/Val/Test) from SLEAP `.pkg.slp` files **converted to YOLO pose format** using **Annolid**.

**Key facts:**

- **Videos:** 40
- **Frame rate:** 40 FPS
- **Image size:** 1280 × 1024 × 1 (grayscale)
- **Resolution:** 1.9 px/mm
- **Skeleton:** 5 keypoints (“5 nodes”)
- **# Animals per frame:** 2
- **Identity tracking:** ❌ (no consistent identity labels across frames)
- **Labels:** 1474 frames, 2948 instances

**Original artifacts (SLEAP format):**

- Train: `train.pkg.slp`
- Validation: `val.pkg.slp`
- Test: `test.pkg.slp`  
  (See “Source data” below.)

## Supported Tasks

- **Keypoint detection / pose estimation (multi-animal)** in a single view (top-down).
- Training YOLO-style pose models (e.g., Ultralytics YOLO pose).

## Data Format (YOLO Pose)

This dataset is exported in **YOLO pose label format** (compatible with common YOLO pose toolchains).

### Directory layout

```
sleap_mice_hc_yolo_pose/
  images/
    train/
    val/
    test/
  labels/
    train/
    val/
    test/
  data.yaml
  pose_schema.json
```

### Label file format

Each image has a corresponding text file in `labels/<split>/`. Each line corresponds to one mouse instance:

```
<class_id> <x_center> <y_center> <width> <height> <x1> <y1> <v1> ... <xK> <yK> <vK>
```

Where:

- coordinates are **normalized** to `[0,1]` by image width/height
- `K = 5` keypoints
- `v` is keypoint visibility (commonly: `0=not labeled`, `1=labeled but not visible/occluded`, `2=visible`; exact convention depends on your training code—see “Notes on visibility”)

### `data.yaml` example

```
path: mice_hc_yolo
train: images/train
val: images/val
test: images/test

nc: 1
names: ["mouse"]

kpt_shape: [5, 3]
# optional (some trainers support explicit edges):
# skeleton:
#   - [0, 1]
#   - [0, 2]
#   ...
```

## Dataset Splits

The dataset preserves the original SLEAP random split:

- `train`
- `val`
- `test`

> Note: **Identity is not provided**, so although there are typically 2 mice per frame, instances are treated independently.

## Pose Schema

The original dataset uses a **5-node skeleton**. The exact keypoint names depend on the provided schema used during conversion. This repo includes a `pose_schema.json` used by Annolid to define:

- keypoint order
- optional symmetry pairs
- optional edges

Example skeleton schema structure:


## How the Conversion Was Done (SLEAP → YOLO Pose) with Annolid

### Source data

This dataset is derived from the SLEAP sample/benchmark dataset “mice_hc”:

- Train: `https://storage.googleapis.com/sleap-data/datasets/eleni_mice/random_split1/train.pkg.slp`
- Validation: `https://storage.googleapis.com/sleap-data/datasets/eleni_mice/random_split1/val.pkg.slp`
- Test: `https://storage.googleapis.com/sleap-data/datasets/eleni_mice/random_split1/test.pkg.slp`
- Example clip: `https://storage.googleapis.com/sleap-data/datasets/eleni_mice/clips/20200111_USVpairs_court1_M1_F1_top-01112020145828-0000%400-2560.mp4`
- Example tracking: `https://storage.googleapis.com/sleap-data/datasets/eleni_mice/clips/20200111_USVpairs_court1_M1_F1_top-01112020145828-0000%400-2560.slp`

### Conversion steps (recommended workflow)

1. **Download SLEAP `.pkg.slp` files** for train/val/test.
2. **Extract frames + annotations** from `.pkg.slp`.
3. **Compute per-instance bounding boxes** from keypoints (or SLEAP instance extents).
4. **Write YOLO pose labels** (normalized bbox + normalized keypoints).
5. **Save `data.yaml`** and the `pose_schema.json`.



### Notes on visibility (v)

SLEAP annotations may not always map 1:1 to YOLO’s visibility conventions. In this conversion:

- labeled keypoints are typically exported with `v=2`
- missing/unlabeled points may be `v=0`

If you need strict COCO-style handling (e.g., occluded vs visible), you may need to add a rule based on SLEAP point confidence/visibility metadata (if present).

## Intended Uses

- Training and benchmarking multi-animal pose estimation models in **challenging low-contrast** home-cage settings.
- Testing robustness of detectors + keypoint heads under occlusion and interaction.

## Limitations

- **No consistent identity labels** across frames (two animals per frame but not tracked by ID).
- Overhead grayscale imagery with low contrast may require careful augmentation and/or background normalization.

## Citation

Please cite the original SLEAP paper and dataset contributors:

- Pereira et al. (2022), Nature Methods
- Eleni Papadoyannis, Mala Murthy, Annegret Falkner


@article{yang2023automated,
  title={Automated Behavioral Analysis Using Instance Segmentation},
  author={Yang, Chen and Forest, Jeremy and Einhorn, Matthew and Cleland, Thomas A},
  journal={arXiv preprint arXiv:2312.07723},
  year={2023}
}

@misc{yang2020annolid,
  author = {Chen Yang and Jeremy Forest and Matthew Einhorn and Thomas Cleland},
  title = {Annolid: an instance segmentation-based multiple animal tracking and behavior analysis package},
  howpublished = {\url{https://github.com/healthonrails/annolid}},
  year = {2020}
}

## License / Terms

This Hugging Face dataset repo contains **converted annotations** and (optionally) extracted frames derived from the original SLEAP dataset files hosted at the URLs above.  
Please ensure your redistribution and usage comply with the **original dataset’s license/terms** and any institutional requirements.