This project is an online annotation tool designed to facilitate the annotation process for YOLO format datasets used in object detection tasks. The tool provides a user-friendly interface for labeling images with bounding boxes around objects of interest and assigning corresponding class labels. It aims to streamline the annotation workflow, making it easier and more efficient for researchers and developers to create annotated datasets for training YOLO-based object detection models.
- Intuitive drawing tools for creating bounding boxes around objects of interest.
- Easy selection of object classes for each annotation.
- Editing capabilities to adjust and refine annotations as needed.
- Web-based interface for seamless annotation directly within the browser.
- Export annotated data in the YOLO format for integration into machine learning pipelines.
- User-friendly interface.
To use the annotation tool, follow these steps:
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Clone the repository:
git clone https://github.com/your-username/yolo-annotation-tool.git
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Go to the main directory:
cd yolo-annotation-tool -
Install flask and other dependencies:
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Start the server:
python app.py # Access the application at http://localhost:5000 in your web browser -
Upload images that you want to annotate.
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Draw bounding boxes around objects in the images and assign class labels.
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Save annotation.
Click on the image above to watch the demo video.
Contributions to the project are welcome! If you find any bugs or have suggestions for new features, please open an issue or submit a pull request.
This project is licensed under the MIT License.