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feat: Enhace emotion inference speed on ARM and add FaceAnalyzer, Face classes for ease of use. (#25)

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* feat: Update linting and type annotations, return types in detect

* feat: add face analyzer and face classes

* chore: Update the format and clean up some docstrings

* docs: Update usage documentation

* feat: Change AgeGender model output to 0, 1 instead of string (Female, Male)

* test: Update testing code

* feat: Add Apple silicon backend for torchscript inference

* feat: Add face analyzer example and add run emotion for testing
This commit is contained in:
Yakhyokhuja Valikhujaev
2025-11-30 20:32:07 +09:00
committed by GitHub
parent 779952e3f8
commit 0c93598007
51 changed files with 1605 additions and 966 deletions
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README.md
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@@ -1,10 +1,11 @@
# UniFace: All-in-One Face Analysis Library
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
![Python](https://img.shields.io/badge/Python-3.10%2B-blue)
[![PyPI Version](https://img.shields.io/pypi/v/uniface.svg)](https://pypi.org/project/uniface/)
[![Python](https://img.shields.io/badge/Python-3.10%2B-blue)](https://www.python.org/)
[![PyPI](https://img.shields.io/pypi/v/uniface.svg)](https://pypi.org/project/uniface/)
[![CI](https://github.com/yakhyo/uniface/actions/workflows/ci.yml/badge.svg)](https://github.com/yakhyo/uniface/actions)
[![Downloads](https://pepy.tech/badge/uniface)](https://pepy.tech/project/uniface)
[![Ruff](https://img.shields.io/badge/Ruff-Checked-red)](https://github.com/astral-sh/ruff)
<div align="center">
<img src=".github/logos/logo_web.webp" width=75%>
@@ -56,6 +57,7 @@ pip install uniface[gpu]
```
**Requirements:**
- CUDA 11.x or 12.x
- cuDNN 8.x
- See [ONNX Runtime GPU requirements](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
@@ -145,7 +147,8 @@ detector = RetinaFace()
age_gender = AgeGender()
faces = detector.detect(image)
gender, age = age_gender.predict(image, faces[0]['bbox'])
gender_id, age = age_gender.predict(image, faces[0]['bbox'])
gender = 'Female' if gender_id == 0 else 'Male'
print(f"{gender}, {age} years old")
```
@@ -217,17 +220,18 @@ faces = detect_faces(image, method='retinaface', conf_thresh=0.8)
### Face Detection (WIDER FACE Dataset)
| Model | Easy | Medium | Hard | Use Case |
|--------------------|--------|--------|--------|-------------------------|
| retinaface_mnet025 | 88.48% | 87.02% | 80.61% | Mobile/Edge devices |
| retinaface_mnet_v2 | 91.70% | 91.03% | 86.60% | Balanced (recommended) |
| retinaface_r34 | 94.16% | 93.12% | 88.90% | High accuracy |
| scrfd_500m | 90.57% | 88.12% | 68.51% | Real-time applications |
| scrfd_10g | 95.16% | 93.87% | 83.05% | Best accuracy/speed |
| Model | Easy | Medium | Hard | Use Case |
| ------------------ | ------ | ------ | ------ | ---------------------- |
| retinaface_mnet025 | 88.48% | 87.02% | 80.61% | Mobile/Edge devices |
| retinaface_mnet_v2 | 91.70% | 91.03% | 86.60% | Balanced (recommended) |
| retinaface_r34 | 94.16% | 93.12% | 88.90% | High accuracy |
| scrfd_500m | 90.57% | 88.12% | 68.51% | Real-time applications |
| scrfd_10g | 95.16% | 93.87% | 83.05% | Best accuracy/speed |
*Accuracy values from original papers: [RetinaFace](https://arxiv.org/abs/1905.00641), [SCRFD](https://arxiv.org/abs/2105.04714)*
_Accuracy values from original papers: [RetinaFace](https://arxiv.org/abs/1905.00641), [SCRFD](https://arxiv.org/abs/2105.04714)_
**Benchmark on your hardware:**
```bash
python scripts/run_detection.py --image assets/test.jpg --iterations 100
```
@@ -412,6 +416,7 @@ ruff check . --fix
```
Ruff configuration is in `pyproject.toml`. Key settings:
- Line length: 120
- Python target: 3.10+
- Import sorting: `uniface` as first-party
@@ -454,4 +459,3 @@ uniface/
## Contributing
Contributions are welcome! Please open an issue or submit a pull request on [GitHub](https://github.com/yakhyo/uniface).