#4343rd of 50 in Data and scraping for AI
json2yolo
Scripts that convert COCO and LabelMe JSON annotations to YOLO labels
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- Stars
- 1.2k
- License
- AGPL-3.0
- Last commit
- Oct 2026
- Last release
- May 2019
- Language
- Python
1,189 on 21 Apr 20261,240 on 10 Nov 2026
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Checked on GitHub.
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Overview
JSON2YOLO is a set of Python scripts that convert COCO detection, segmentation and keypoint annotations, plus LabelMe JSON, into the Ultralytics YOLO dataset layout (images/, labels/, YAML of class names). The README states the standalone scripts are no longer actively updated and points to convert_coco() in the ultralytics package instead.
Who it is for: ML engineers preparing COCO or LabelMe datasets for YOLO training
Strengths
- Handles COCO boxes, segments and keypoints via --use-segments and --use-keypoints flags
- Also reads LabelMe polygon, rectangle, circle, linestrip and mask annotations
- Runs on Linux, macOS and Windows with Python 3.8+ and a requirements.txt
- Maintained equivalent exists as convert_coco() in the ultralytics package
Weaknesses
- Standalone scripts are no longer actively updated, per the README
- --save-dir is deleted and recreated on every run
- Labelbox mask-only objects are skipped with a warning
- AGPL-3.0 license; commercial use needs a separate enterprise license
What it needs
- no GPU
- Needs Python 3.8+, ultralytics (for convert_coco)
- Models: COCO JSON, LabelMe JSON, Labelbox JSON, YOLO format
Quick answers
Is json2yolo open source?
Yes. Its code is on GitHub under the AGPL-3.0 license.
Does json2yolo need a GPU?
No, it runs without one.
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