Extracts object from raw images to train machine learning or deep learning models.

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• About the Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact

Extracted Person	Extracted Car	Extracted Bus

This project labels raw images for your machine learning, deep learning, or computer vision tasks. By far this is the easiest and the most effective data labeling took out there.

Here is why:

• Completely open-source and free for everyone
• Most east data labeling software out there.
• Always in development so new features are coming in daily

This project is built using Python, OpenCV, and Yolo for more information I put the links down below.

• Python
• OpenCV
• Yolo

This section explains and goes thought how to install and setup everything

• pip

pip install -r requirements.txt

1. Clone the repo

git clone https://github.com/YigitGunduc/data-labeler.git

2. Install Yolo Yolo website link

• Go to the Yolo's website
• Install yoloV3.weights
• Place it in the 'yolo' folder

• Put the images you want to extract the object from in the raw data folder.

• To label, data run the 'data labeler.py' file, it will go through all of the images and extract objects from each photo and put extracted images to the corresponding folder.

See the open issues for a list of proposed features (and known issues).

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.

Project Link: https://github.com/YigitGunduc/data-labeler