Skip to content

Retrain the yolo3 model with TensorFlow and your own Dataset

Notifications You must be signed in to change notification settings

Cw-zero/Retrain-yolo3

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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.

About

Retrain the yolo3 model with TensorFlow and your own Dataset

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages