YOLOv3

Keras(TF backend) implementation of yolo v3 objects detection.

According to the paper YOLOv3: An Incremental Improvement.

Requirement

• OpenCV 3.4
• Python 3.6
• Tensorflow-gpu 1.5.0
• Keras 2.1.3

Quick start

• Download official 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.

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

The first image is the early prediction at the 82nd layer, while the second image is the prediction at the 106th layer (final layer).

Reference

@article{YOLOv3,  
  title={YOLOv3: An Incremental Improvement},  
  author={J Redmon, A Farhadi },
  year={2018}

This code is a wrapper over YOLOv3

Copyright

See LICENSE for details.