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README.md

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 # YOLOFace
-# Face detection using YOLOv3
 
-Face detection using YOLOv3
+# 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
+
+![Imgur](assets/yolo-architecture.png)
+
+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
+
+Install the required packages by running the following command:
+
+```bash
+$ pip install -r requirements.txt
+```
+
+**Note:** This repositoty works on Python 3.x. Using Python virtual environment is highly recommended.
+
+## 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 --images 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
+
+![Imgur](outputs/outside_000001_yoloface.jpg)
+
+![Imgur](outputs/meeting_11_304_yoloface.jpg)
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for more details.
+
+## References
+