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https://github.com/atticuszz/boostface
😎real time fullstack face recognition system
https://github.com/atticuszz/boostface
arcface-face-recognition deep-learning face-detection face-recognition fastapi insightface milvus onnxruntime scrfd streamlit tracking
Last synced: 3 months ago
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😎real time fullstack face recognition system
- Host: GitHub
- URL: https://github.com/atticuszz/boostface
- Owner: Atticuszz
- Created: 2024-01-27T13:36:25.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-01T09:23:40.000Z (3 months ago)
- Last Synced: 2024-11-01T10:22:55.735Z (3 months ago)
- Topics: arcface-face-recognition, deep-learning, face-detection, face-recognition, fastapi, insightface, milvus, onnxruntime, scrfd, streamlit, tracking
- Language: Python
- Homepage:
- Size: 152 MB
- Stars: 6
- Watchers: 1
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Codeowners: .github/CODEOWNERS
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README
# 🚀 BoostFace 🚀
Welcome to **BoostFace**! The cutting-edge, high-performance face recognition system designed to revolutionize the way we think about real-time identification and tracking. Built with a powerful stack of technologies, BoostFace is your go-to solution for handling high-load, high-concurrency scenarios with ease and efficiency. 🌟
## Demo
## 🛠 Tech Stack
- **Frontend**: Leveraging [Streamlit](https://streamlit.io/) for an intuitive, user-friendly interface, combined with SCRFD for efficient and accurate face detection, and SORT for multi-object tracking. Real-time image transmission is facilitated through Websockets, ensuring a seamless and dynamic user experience. 🖥️
- **Backend**: Powered by [FastAPI](https://fastapi.tiangolo.com/) for lightning-fast backend operations, [ONNX Runtime](https://onnx.ai/onnx-runtime) for optimized machine learning model inference, and [CUDA](https://developer.nvidia.com/cuda-zone)/[cuDNN](https://developer.nvidia.com/cudnn) for leveraging NVIDIA's GPU acceleration. To round it all off, [Milvus](https://milvus.io/) is used to handle the heavy lifting of vector similarity search, making our system not only fast but also incredibly scalable. 🚀
## 🌈 Features
- **Real-Time Face Recognition**: Identify and track faces in real-time with unparalleled accuracy. 🤖
- **High Scalability**: Ready to handle an expansive number of concurrent requests without breaking a sweat. 📈
- **Low Latency**: Designed from the ground up to minimize response times, making it ideal for real-time applications. ⚡
- **Easy Integration**: With a well-documented API, integrating BoostFace into your existing infrastructure is a breeze. 🌐
## 📚 Getting Started
Dive into the world of high-performance face recognition by cloning this repo and following our detailed setup instructions. Whether you're a developer looking to integrate face recognition into your project, or you're simply curious about the technology, BoostFace has something for everyone.
```bash
git clone https://github.com/Atticuszz/boostface.git
cd boostface
```
### prepare
- download arcface_onnx model
```bash
cd src/Demo/backend/services/inference/model_zoo
wget https://github.com/Atticuszz/BoostFace/releases/download/dataset/models.zip
unzip models.zip
rm -rf models.zip
```
- init env
```bash
cd src/Demo
conda env create -f environment.yml
conda activate BoostFace-demo
```
- register demo face after backend start
```bash
# register demo
python src/Demo/web/inference/utils/register.py
```
### run
- start vector database
```bash
docker-compose up src/Demo/backend/services/db/milvus-standalone-docker-compose.yml
```
- start backend
```bash
# run backend
python src/Demo/backend/main.py
```
- start web
```bash
streamlit run src/Demo/web/main.py
```