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license: mit
tags:
- primateface
- face-detection
- pose-estimation
- primates
- animal-behavior
- computer-vision
library_name: primateface
---
# PrimateFace Models
Pre-trained models for [PrimateFace](https://github.com/KordingLab/PrimateFace) --- an open-source toolkit for primate face detection, landmark estimation, and facial behavior analysis across 60+ primate genera.
## Models
| Model | Task | Architecture | File | Size |
|---|---|---|---|---|
| **Face Detection** | Bounding box detection | Cascade R-CNN (ResNet-101 + FPN) | `detection/cascade_rcnn_r101_fpn.pth` | 340 MB |
| **Face Pose** | 68-keypoint landmark estimation | HRNetV2-W18-DARK | `pose/hrnetv2_w18_dark_68kpt.pth` | 38 MB |
Both models were trained on PrimateFace data spanning 60+ primate genera using the [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMPose](https://github.com/open-mmlab/mmpose) frameworks.
## Keypoint Format
68-point facial landmarks following the dlib standard (COCO format: `[x, y, visibility] x 68`):
- Jaw contour (17 points)
- Eyebrows (10 points)
- Nose bridge + base (9 points)
- Eyes (12 points)
- Mouth outer + inner (20 points)
## Quick Start
```python
# Download models automatically
python demos/download_models.py
# Or use the Python API
from huggingface_hub import hf_hub_download
det_path = hf_hub_download(
repo_id="fparodi/primateface-models",
filename="cascade_rcnn_r101_fpn.pth",
subfolder="detection",
)
pose_path = hf_hub_download(
repo_id="fparodi/primateface-models",
filename="hrnetv2_w18_dark_68kpt.pth",
subfolder="pose",
)
```
## Citation
```bibtex
@article{parodi2025primateface,
title={PrimateFace: A Machine Learning Resource for Automated Face Analysis in Human and Non-human Primates},
author={Parodi, Felipe and Matelsky, Jordan and others},
year={2025},
url={https://www.biorxiv.org/content/10.1101/2025.08.12.669927}
}
```
## License
MIT --- same as the PrimateFace repository.
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