yolov3-master

# YOLOv3 Keras(TF backend) implementation of yolo v3 objects detection. According to the paper [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf). ## Requirement - OpenCV 3.4 - Python 3.6 - Tensorflow-gpu 1.5.0 - Keras 2.1.3 ## Quick start - Download official [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights) and put it on top floder of project. - Run the follow command to convert darknet weight file to keras h5 file. The `yad2k.py` was modified from [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K). ``` python yad2k.py cfg\yolo.cfg yolov3.weights data\yolo.h5 ``` - run follow command to show the demo. The result can be found in `images\res\` floder. ``` python demo.py ``` ## Demo result It can be seen that yolo v3 has a better classification ability than yolo v2. <img width="400" height="350" src="/images/res/dog.jpg"/><img width="400" height="350" src="/images/res/person.jpg"/> ## TODO - Train the model. ## Reference @article{YOLOv3, title={YOLOv3: An Incremental Improvement}, author={J Redmon, A Farhadi }, year={2018} ## Copyright See [LICENSE](LICENSE) for details.