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Deep learning-based Face detection using the YOLOv3 algorithm (https://github.com/sthanhng/yoloface)
| Date | Stars |
|---|---|
| 2026-07-24 | 463 |
| 2026-07-25 | 463 |
| 2026-07-28 | 463 |
| 2026-07-30 | 463 |
| 2026-08-06 | 462 |
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# YOLOFace # Deep learning based Face detection using the YOLOv3 algorithm ## Getting started The YOLOv3 (You Only Look Once) is a state-of-the-art, real-time object detection algorithm. The published model recognizes 80 different objects in images and videos. For more details, you can refer to this [paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf). ## YOLOv3's architecture  Credit: [Ayoosh Kathuria](https://towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b) ## OpenCV Deep Neural Networks (dnn module) OpenCV `dnn` module supports running inference on pre-trained deep learning models from popular frameworks such as TensorFlow, Torch, Darknet and Caffe. ## Prerequisites * Tensorflow * opencv-python * opencv-contrib-python * Numpy * Keras * Matplotlib * Pillow Development for this project will be isolated in Python virtual environment. This allows us to experiment with different versions of dependencies. There are many ways to install `virtual environment (virtualenv)`, see the [Python Virtual Environments: A Primer](https://realpython.com/python-virtual-environments-a-primer/) guide for different platforms, but here are a couple: - For Ubuntu ```bash $ pip install virtualenv ``` - For Mac ```bash $ pip install --upgrade virtualenv ``` Create a Python 3.6 virtual environment for this project and activate the virtualenv: ```bash $ virtualenv -p python3.6 yoloface $ source ./yoloface/bin/activate ``` Next, install the dependencies for the this project: ```bash $ pip install -r requirements.txt ``` ## Usage * Clone this repository ```bash $ git clone https://github.com/sthanhng/yoloface ``` * For face detection, you should download the pre-trained YOLOv3 weights file which trained on the [WIDER FACE: A Face Detection Benchmark](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html) dataset from this [link](https://drive.google.com/file/d/1xYasjU52whXMLT5MtF7RCPQkV66993oR/view?usp=sharing) and place it in the `model-weights/` directory. * Run the following command: >**image input** ```bash $ python yoloface.py --image samples/outside_000001.jpg --output-dir outputs/ ``` >**video input** ```bash $ python yoloface.py --video samples/subway.mp4 --output-dir outputs/ ``` >**webcam** ```bash $ python yoloface.py --src 1 --output-dir outputs/ ``` ## Sample outputs  ## License This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for more details. ## References
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d86fa3a304126b77, topic:yolo, topic:opencv, readme:object detection
matched fp:d86fa3a304126b77, topic:deep-learning