ZF/uniface
1
0
Fork 0
mirror of https://github.com/yakhyo/uniface.git synced 2025-12-30 09:02:25 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: Add FairFace model and AttributeResults return type (#46)

Browse Source 
* feat: Add FairFace model and unified AttributeResult return type
- Update FaceAnalyzer to support FairFace
- Update documentation (README.md, QUICKSTART.md, MODELS.md)

* docs: Change python3.10 to python3.11 in python badge

* chore: Remove unused import

* fix: Fix test for age gender to reflect AttributeResult type
This commit is contained in:
Yakhyokhuja Valikhujaev
2025-12-28 21:07:36 +09:00
committed by GitHub
parent 7c98a60d26
commit 64ad0d2f53
18 changed files with 639 additions and 393 deletions
Download Patch File Download Diff File Expand all files Collapse all files

38
MODELS.md
View File

@@ -259,9 +259,40 @@ landmarks = landmarker.get_landmarks(image, bbox)
from uniface import AgeGender
predictor = AgeGender()
gender, age = predictor.predict(image, bbox)
# Returns: (gender, age_in_years)
# gender: 0 for Female, 1 for Male
result = predictor.predict(image, bbox)
# Returns: AttributeResult with gender, age, sex property
# result.gender: 0 for Female, 1 for Male
# result.sex: "Female" or "Male"
# result.age: age in years
```
---
### FairFace Attributes
| Model Name | Attributes | Params | Size | Use Case |
| ----------- | --------------------- | ------ | ----- | --------------------------- |
| `DEFAULT` | Race, Gender, Age Group | - | 44MB | Balanced demographic prediction |
**Dataset**: Trained on FairFace dataset with balanced demographics
**Note**: FairFace provides more equitable predictions across different racial and gender groups
**Race Categories (7):** White, Black, Latino Hispanic, East Asian, Southeast Asian, Indian, Middle Eastern
**Age Groups (9):** 0-2, 3-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+
#### Usage
```python
from uniface import FairFace
predictor = FairFace()
result = predictor.predict(image, bbox)
# Returns: AttributeResult with gender, age_group, race, sex property
# result.gender: 0 for Female, 1 for Male
# result.sex: "Female" or "Male"
# result.age_group: "20-29", "30-39", etc.
# result.race: "East Asian", "White", etc.
```
---
@@ -487,6 +518,7 @@ python scripts/download_model.py --model MNET_V2
- **Gaze Estimation Training**: [yakhyo/gaze-estimation](https://github.com/yakhyo/gaze-estimation) - MobileGaze training code and pretrained weights
- **Face Parsing Training**: [yakhyo/face-parsing](https://github.com/yakhyo/face-parsing) - BiSeNet training code and pretrained weights
- **Face Anti-Spoofing**: [yakhyo/face-anti-spoofing](https://github.com/yakhyo/face-anti-spoofing) - MiniFASNet ONNX inference (weights from [minivision-ai/Silent-Face-Anti-Spoofing](https://github.com/minivision-ai/Silent-Face-Anti-Spoofing))
- **FairFace**: [yakhyo/fairface-onnx](https://github.com/yakhyo/fairface-onnx) - FairFace ONNX inference for race, gender, age prediction
- **InsightFace**: [deepinsight/insightface](https://github.com/deepinsight/insightface) - Model architectures and pretrained weights
### Papers