Extracts object from raw images to train machine learning or deep learning models.
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| Extracted Person | Extracted Car | Extracted Bus |
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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.
This section explains and goes thought how to install and setup everything
- pip
pip install -r requirements.txt- Clone the repo
git clone https://github.com/YigitGunduc/data-labeler.git- Install Yolo Yolo website link
- Go to the Yolo's website
- Install yoloV3.weights
- Place it in the 'yolo' folder
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Put the images you want to extract the object from in the raw data folder.
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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.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.
Project Link: https://github.com/YigitGunduc/data-labeler