RetinaFace: Single-stage Dense Face Localisation in the Wild
Face detection in the wild presents unique challenges, but RetinaFace 🌐, a single-stage dense face localization model, tackles them effectively. My latest repository on GitHub explores this model, which enables high-precision facial detection and localization of key landmarks. This implementation integrates several backbone models (MobileNet and ResNet variants), allowing for flexibility between model size and accuracy. 🚀
💡 Easily get started with UniFace, a lightweight library built on models from this repository.
UniFace supports face detection, alignment, and more!
👉 https://github.com/yakhyo/retinaface-pytorch
RetinaFace is well-suited for real-time applications 🎥 and cross-platform deployment 📲, with support for ONNX exports. The codebase has been refactored for ease of use, and lightweight MobileNet backbones ensure that the model performs efficiently even on low-power devices.
✨ Features
- 🔄 New trained model weights: Filtering out smaller faces (<16 pixels) to decrease false positives.
- 📱 Lightweight Models: MobileNetV1 (width mult=0.25) for fast inference.
- 📈 Improved Accuracy: ResNet backbones deliver higher detection accuracy.
- 🎥 Webcam Support & ONNX Export: Real-time inference and flexible deployment.
- ✅ Refactored Codebase: Cleaner and more reproducible.
📈 Performance on WiderFace Dataset
Multi-scale Image Size
| RetinaFace Backbones | Pretrained on ImageNet | Easy | Medium | Hard |
|---|---|---|---|---|
| MobileNetV1 (width mult=0.25) | True | 88.48% | 87.02% | 80.61% |
| MobileNetV1 (width mult=0.50) | False | 89.42% | 87.97% | 82.40% |
| MobileNetV1 | False | 90.59% | 89.14% | 84.13% |
| MobileNetV2 | True | 91.70% | 91.03% | 86.60% |
| ResNet18 | True | 92.50% | 91.02% | 86.63% |
| ResNet34 | True | 94.16% | 93.12% | 88.90% |
Original Image Size
| RetinaFace Backbones | Pretrained on ImageNet | Easy | Medium | Hard |
|---|---|---|---|---|
| MobileNetV1 (width mult=0.25) | True | 90.70% | 88.12% | 73.82% |
| MobileNetV1 (width mult=0.50) | False | 91.56% | 89.46% | 76.56% |
| MobileNetV1 | False | 92.19% | 90.41% | 79.56% |
| MobileNetV2 | True | 94.04% | 92.26% | 83.59% |
| ResNet18 | True | 94.28% | 92.69% | 82.95% |
| ResNet34 | True | 95.07% | 93.48% | 84.40% |
📂 Get Started
-
Clone the repository:
git clone https://github.com/yakhyo/retinaface-pytorch.git cd retinaface-pytorch -
Install dependencies:
pip install -r requirements.txt -
Run Webcam Inference:
python detect.py --network mobilenetv1 --weights retinaface_mv1.pth
For a detailed setup and to explore various training configurations, check out the https://github.com/yakhyo/retinaface-pytorch and contribute to this project! 👩💻👨💻