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ref: Add comprehensive test suite and enhance model functionality

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- Add new test files for age_gender, factory, landmark, recognition, scrfd, and utils
- Add new scripts for age_gender, landmarks, and video detection
- Update documentation in README.md, MODELS.md, QUICKSTART.md
- Improve model constants and face utilities
- Update detection models (retinaface, scrfd) with enhanced functionality
- Update project configuration in pyproject.toml
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yakhyo
2025-11-15 21:09:37 +09:00
parent df673c4a3f
commit 2c78f39e5d
28 changed files with 2014 additions and 591 deletions
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30
MODELS.md
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@@ -113,13 +113,15 @@ embedding = recognizer.get_normalized_embedding(image, landmarks)
Lightweight face recognition optimized for mobile devices.
| Model Name | Backbone | Params | Size | Use Case |
|-----------------|-----------------|--------|------|--------------------|
| `MNET_025` | MobileNetV1 0.25| 0.2M | 1MB | Ultra-lightweight |
| `MNET_V2` ⭐ | MobileNetV2 | 1.0M | 4MB | **Mobile/Edge** |
| `MNET_V3_SMALL` | MobileNetV3-S | 0.8M | 3MB | Mobile optimized |
| `MNET_V3_LARGE` | MobileNetV3-L | 2.5M | 10MB | Balanced mobile |
| Model Name | Backbone | Params | Size | LFW | CALFW | CPLFW | AgeDB-30 | Use Case |
|-----------------|-----------------|--------|------|-------|-------|-------|----------|--------------------|
| `MNET_025` | MobileNetV1 0.25| 0.36M | 1MB | 98.76%| 92.02%| 82.37%| 90.02% | Ultra-lightweight |
| `MNET_V2` ⭐ | MobileNetV2 | 2.29M | 4MB | 99.55%| 94.87%| 86.89%| 95.16% | **Mobile/Edge** |
| `MNET_V3_SMALL` | MobileNetV3-S | 1.25M | 3MB | 99.30%| 93.77%| 85.29%| 92.79% | Mobile optimized |
| `MNET_V3_LARGE` | MobileNetV3-L | 3.52M | 10MB | 99.53%| 94.56%| 86.79%| 95.13% | Balanced mobile |
**Dataset**: Trained on MS1M-V2 (5.8M images, 85K identities)
**Accuracy**: Evaluated on LFW, CALFW, CPLFW, and AgeDB-30 benchmarks
**Note**: These models are lightweight alternatives to ArcFace for resource-constrained environments
#### Usage
@@ -138,12 +140,14 @@ recognizer = MobileFace(model_name=MobileFaceWeights.MNET_V2)
Face recognition using angular softmax loss.
| Model Name | Backbone | Params | Size | Use Case |
|-------------|----------|--------|------|----------------------|
| `SPHERE20` | Sphere20 | 13.0M | 50MB | Research/Comparison |
| `SPHERE36` | Sphere36 | 24.2M | 92MB | Research/Comparison |
| Model Name | Backbone | Params | Size | LFW | CALFW | CPLFW | AgeDB-30 | Use Case |
|-------------|----------|--------|------|-------|-------|-------|----------|----------------------|
| `SPHERE20` | Sphere20 | 24.5M | 50MB | 99.67%| 95.61%| 88.75%| 96.58% | Research/Comparison |
| `SPHERE36` | Sphere36 | 34.6M | 92MB | 99.72%| 95.64%| 89.92%| 96.83% | Research/Comparison |
**Note**: SphereFace uses angular softmax loss, an earlier approach before ArcFace
**Dataset**: Trained on MS1M-V2 (5.8M images, 85K identities)
**Accuracy**: Evaluated on LFW, CALFW, CPLFW, and AgeDB-30 benchmarks
**Note**: SphereFace uses angular softmax loss, an earlier approach before ArcFace. These models provide good accuracy with moderate resource requirements.
#### Usage
@@ -264,10 +268,10 @@ emotion, confidence = predictor.predict(image, landmarks)
### By Hardware
#### Apple Silicon (M1/M2/M3/M4)
**Recommended**: All models work well with CoreML acceleration
**Recommended**: All models work well with ARM64 optimizations (automatically included)
```bash
pip install uniface[silicon]
pip install uniface
```
**Recommended models**: