face-recognition
Face-Recognition Training Framework
🔥 Updates
2025/03/13: Face Detection added inonnx_inference.py.2025/03/13: ONNX Export and Inference has been added.2025/01/03: We released the Face-Recognition training framework and pretrained model weights.
📊 Results
| Dataset | Backbone | LFW (%) | CALFW (%) | CPLFW (%) | AgeDB_30 (%) | Num Params |
|---|---|---|---|---|---|---|
| MS1MV2 | Sphere20 | 99.67 | 95.61 | 88.75 | 96.58 | 24.5M |
| MS1MV2 | Sphere36 | 99.72 | 95.64 | 89.92 | 96.83 | 34.6M |
| MS1MV2 | MobileNetV1_0.25 | 98.76 | 92.02 | 82.37 | 90.02 | 0.36M |
| MS1MV2 | MobileNetV2 | 99.55 | 94.87 | 86.89 | 95.16 | 2.29M |
| MS1MV2 | MobileNetV3_Small | 99.30 | 93.77 | 85.29 | 92.79 | 1.25M |
| MS1MV2 | MobileNetV3_Large | 99.53 | 94.56 | 86.79 | 95.13 | 3.52M |
✨ Features
| Date | Feature Description |
|---|---|
| 2024-12-15 | 🔄 Training Pipeline: Introduced a simple and effective pipeline for face-recognition training with support for DDP and single GPU configurations. |
| 2024-12-15 | 📚 Pretrained Models: Added support for MobileNetV1/V2/V3, Sphere20, and Sphere36 models for versatile use-cases and performance tiers. |
| 2024-12-15 | 📂 Dataset Downloads: Easy access to aligned and cropped training and validation datasets via Kaggle links. |
| 2024-12-15 | 🔧 Modular Codebase: Fully modular and reproducible codebase for easier customization and extension. |
| 2024-12-15 | 🌐 Dataset Compatibility: Supports CASIA-WebFace, VGGFace2, and MS1MV2 datasets, pre-aligned and cropped for streamlined training. |
🚀 Getting Started
💻 Installation
git clone https://github.com/yakhyo/face-recognition.git
cd face-recognition
pip install -r requirements.txt
🏋️♂️ Training
Codebase supports DDP, to run using DDP please use below example command:
torchrun --nproc_per_node=2 train.py --root data/train/ms1m_112x112 --database MS1M --network mobilenetv1 --classifier MCP
If you have a single GPU then use the below example command:
python train.py --root data/train/ms1m_112x112 --database MS1M --network mobilenetv1 --classifier MCP
🧪 Evaluate
To evaluate, please modify model, weights, and validation data filenames in evaluate.py
python evaluate.py
📥 Pretrained Model Weights (v0.0.1)
The following pretrained model weights are available for download under the release v0.0.1:
| Model | Download Link |
|---|---|
| MobileNetV1_0.25 | Download |
| MobileNetV2 | Download |
| MobileNetV3_Small | Download |
| MobileNetV3_Large | Download |
| Sphere20 | Download |
| Sphere36 | Download |
🔧 Usage
- Download the model weights from the links above.
- Place the weights in the desired directory (e.g.,
weights/). - Update your training or inference script to load the appropriate model weights.
ONNX Export
Run following command to export to ONNX:
python -m scripts.onnx_export -w [path/to/weight/file] -n [network/architecture/name] --dynamic[Optional]
ONNX Inference
Run onnx_inference.py to use ONNXRuntime. This inference calculates the similarity between two face images.
python onnx_inference.py
PyTorch Inference
Run inference.py for PyTorch model inference. This inference calculates the similarity between two face images.
📂 Dataset
You can download aligned and cropped (112x112) training and validation datasets from Kaggle.
📦 Training Data
- CASIA-WebFace 112x112 from
opensphere- Identities: 10.6k
- #Images: 491k
- VGGFace2 112x112 from
opensphere- Identities: 8.6k
- #Images: 3.1M
- MS1MV2 112x112 from
insightface- Identities: 85.7k
- #Images: 5.8M
🔍 Validation Data
Validation data contains AgeDB_30, CALFW, CPLFW, and LFW datasets.
🗂️ Folder Structure
data/
|-- train/
| |-- ms1m_112x112/
| |-- vggface2_112x112/
| |-- webface_112x112/
|-- val/
| |-- agedb_30_112x112/
| |-- calfw_112x112/
| |-- cplfw_112x112/
| |-- lfw_112x112/
| |-- agedb_30_ann.txt
| |-- calfw_ann.txt
| |-- cplfw_ann.txt
| |-- lfw_ann.txt
📝 License
This project is licensed under the MIT License. See the LICENSE file for details.