https://github.com/2vin/PyYAT

Semi-Automatic Yolo Annotation Tool In Python
https://github.com/2vin/PyYAT

annotation-tool computer-vision deeplearning yolo

Last synced: about 1 month ago
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Semi-Automatic Yolo Annotation Tool In Python

• Host: GitHub
• URL: https://github.com/2vin/PyYAT
• Owner: 2vin
• Created: 2022-06-24T21:18:20.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2023-08-23T22:43:19.000Z (over 1 year ago)
• Last Synced: 2024-08-02T01:25:11.837Z (4 months ago)
• Topics: annotation-tool, computer-vision, deeplearning, yolo
• Language: Python
• Homepage:
• Size: 1.25 MB
• Stars: 18
• Watchers: 1
• Forks: 8
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

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README

        




# PyYAT

Semi-Automatic Yolo Annotation Tool In Python

  

    PyYAT

  

  

    

  

 

 

Using this tool, we can annotate bounding boxes for image annotation in YOLO format. 

It uses YOLOv3-608 weights to pre-annotate the bounding boxes in images. It reduces time and efforts in annotating large datasets by upto 90%.

## About the repository

* 'yolo_annotation_tool.py' : Main file to load images one-by-one from the dataset, and then annotate them.

* 'recognize_objects.py' : Object recognition class for pre-annotating images before manual annotation process.

* 'config.ini' : Edit data folder, output folder and label file path according to your preference. 'annotation_index' is automatically updated based on index of the last saved annotated image.

* 'labels.csv' : List of all the classes to be annotated.

* '/models' : It contains YOLOv3-608 weights (to be downloaded), cfg and coco.names files.

* '/data' : Sample of input images to be annotated

* '/output' : Sample of output files after annotation.

## Usage:

```

python yolo_annotation_tool.py

```

## Key buttons:

* 'S' : Save annotations

* 'L' : Change current class of labeling

* 'Esc' :  Exit the code

## Input images

  

    Image 1

     Image 2

     Image 3

  

  

    

    

    

  

 

## Output images (Pre-annotation)

  

    Image 1

     Image 2

     Image 3