Introduction

A Keras implementation of YOLOv3 (Tensorflow backend) on bdd100k dataset.

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Guide

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

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
python yolo.py   OR   python yolo_video.py [video_path] [output_path(optional)]

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Choice of Anchor Boxes

YOLO v3, in total uses 9 anchor boxes. Three for each scale. If training YOLO on a custom dataset, generation of anchors must be done using K-Means clustering. Then, arrange the anchors is descending order of dimensions. Assign the three biggest anchors for the first scale , the next three for the second scale, and the last three for the third.

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Training

1. Generate the annotation file using python bdd100k_annotation.py 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 example:

   path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
   path/to/img2.jpg 120,300,250,600,2
   ...
   

2. Make sure you have run python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
   The file model_data/yolo_weights.h5 is used to load pretrained weights.

3. Modify train.py and start training.
   python train.py
   Use your trained weights or checkpoint weights in yolo.py.
   Remember to modify class path or anchor path.

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