A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.

1. Download YOLOv3 weights from YOLO website.
2. Convert the Darknet YOLO model to a Keras model.
3. Run YOLO detection.

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo.py   OR   python yolo_video.py

1. Generate your own annotation file and class names file.
   One row for one image;
   Row format: image_file_path box1 box2 ... boxN;
   Box format: x_min,y_min,x_max,y_max,class_id (no space).
   For VOC dataset, try python voc_annotation.py

2. Make sure you have run python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
   A file model_data/yolo_weights.h5 will be generated when you run train.py for the first time.
   The file is used to load pretrained weights.

3. Modify train.py and start training.
   python train.py
   You will get the trained model model_data/my_yolo.h5.