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Use TensorFlow and Python to retain Yolo3

1. Introduction

  • According to this project, you can get your own yolo3 model for your dataset.
  • This project is from qqwweee/keras-yolo3, but I recorded the implementation process in more detail.
  • If you are more familiar with Chinese ,this blog has more details.

2. How to run this project

  • a. git clone https://github.com/Cw-zero/Retrain-yolo3.git
  • b. Download yolo3.weight from this, and put it in the Retrain-yolo3 folder.
  • c. python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
  • d. Prepare your Dataset
1.New several folders in VOC2007 folder, the final structure like that:

VOC2007
├── Annotations
├── ImageSets
|   ├── Layout
|   ├── Main
|   ├── Segmentation
├── JPEGImages
├── SegmentationClass
├── SegmentationObject
└── test.py

2.Copy your all images to JPEGImages
  • e.Use LabelImg to annotate and label your images,and the outputs saved in Annotations folder.
  • d.cd to VOC2007, python test.py
  • e.cd to Retrain-yolo3, python voc_annotation.py
  • f.cd to Retrain-yolo3, python train.py

3. Others:

The above steps can only train VOC Dataset, if you want to change the number of classes, you also need to modify voc_annotation.py, yolo3.cfg and voc_classes.txt. I will update this part on blog and here as soon as possible.