https://github.com/ultralytics/JSON2YOLO
Convert JSON annotations into YOLO format.
https://github.com/ultralytics/JSON2YOLO
coco darknet dataset json label labelbox ultralytics yolo yolov3 yolov5
Last synced: 4 months ago
JSON representation
Convert JSON annotations into YOLO format.
- Host: GitHub
- URL: https://github.com/ultralytics/JSON2YOLO
- Owner: ultralytics
- License: agpl-3.0
- Created: 2019-05-11T11:08:54.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2025-07-11T08:38:38.000Z (11 months ago)
- Last Synced: 2025-07-11T11:46:34.681Z (11 months ago)
- Topics: coco, darknet, dataset, json, label, labelbox, ultralytics, yolo, yolov3, yolov5
- Language: Python
- Homepage: https://docs.ultralytics.com
- Size: 121 KB
- Stars: 1,051
- Watchers: 7
- Forks: 254
- Open Issues: 63
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-mobile-ai - ultralytics/JSON2YOLO
README
# đ Introduction
Welcome to the [JSON2YOLO](https://github.com/ultralytics/JSON2YOLO) repository! This toolkit is designed to help you convert datasets in [JSON](https://www.ultralytics.com/glossary/json) format, particularly those following the [COCO (Common Objects in Context)](https://cocodataset.org/#home) standards, into the [YOLO format](https://docs.ultralytics.com/datasets/#yolo-format). The YOLO format is widely recognized for its efficiency in [real-time](https://www.ultralytics.com/glossary/real-time-inference) [object detection](https://docs.ultralytics.com/tasks/detect/) tasks.
This conversion process is essential for [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) practitioners looking to train object detection models using frameworks compatible with the YOLO format, such as [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/). Our code is flexible and designed to run across various platforms including Linux, macOS, and Windows.
[](https://github.com/ultralytics/JSON2YOLO/actions/workflows/format.yml)
[](https://discord.com/invite/ultralytics)
[](https://community.ultralytics.com/)
[](https://reddit.com/r/ultralytics)
> **đĸ Important Update**: The JSON2YOLO project is now integrated into the main Ultralytics package at https://github.com/ultralytics/ultralytics. The standalone scripts in this repository are no longer being actively updated. For the latest functionality, please use the new `convert_coco()` method described in our updated [data converter documentation](https://docs.ultralytics.com/reference/data/converter/).
## âī¸ Requirements
To get started with JSON2YOLO, you'll need a [Python](https://www.python.org/) environment running version 3.8 or later. Additionally, you'll need to install all the necessary dependencies listed in the `requirements.txt` file. You can install these dependencies using the following [pip](https://pip.pypa.io/en/stable/) command in your terminal:
```bash
pip install -r requirements.txt # Installs all the required packages
```
## đĄ Usage
JSON2YOLO functionality is now part of the main `ultralytics` Python package. To use the converter, first install the package:
```bash
pip install ultralytics
```
You can then easily convert COCO JSON datasets to YOLO format using the `convert_coco` method. Here's an example using keypoint annotations:
```python
from ultralytics.data.converter import convert_coco
convert_coco(
labels_dir="path/to/labels.json",
save_dir="path/to/output_dir",
use_keypoints=True,
)
```
This method processes your JSON file, converts annotations (bounding boxes and keypoints), and saves the labels in YOLO format (`.txt` files) within the specified directory. For more details, refer to our [dataset format documentation](https://docs.ultralytics.com/datasets/).
## đ Citation
If you find our tool useful for your research or development, please consider citing it:
[](https://zenodo.org/badge/latestdoi/186122711)
## đ¤ Contribute
We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge đ and thank you to all of our contributors!
[](https://github.com/ultralytics/ultralytics/graphs/contributors)
## Šī¸ License
Ultralytics offers two licensing options to accommodate diverse needs:
- **AGPL-3.0 License**: Ideal for students and enthusiasts, this [OSI-approved](https://opensource.org/license/agpl-v3) open-source license promotes collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial inquiries, please contact us through [Ultralytics Licensing](https://www.ultralytics.com/license).
## đŦ Contact Us
For bug reports, feature requests, and contributions, please visit [GitHub Issues](https://github.com/ultralytics/JSON2YOLO/issues). For broader questions and discussions about this project and other Ultralytics initiatives, join our vibrant community on [Discord](https://discord.com/invite/ultralytics)!
