# ultralytics/JSON2YOLO

Legacy JSON-to-YOLO dataset converter for COCO, LabelMe, Labelbox, VoTT, INFOLKS, and ATH annotations. Superseded by convert_coco() in the Ultralytics package.

Repository: https://github.com/ultralytics/JSON2YOLO
Canonical: https://ross.abutalabs.com/products/json2yolo
Homepage: https://docs.ultralytics.com
Language: Python
License: AGPL-3.0
License Family: copyleft
Topics: yolo, json, coco, labelbox, ultralytics, annotation-conversion, dataset-conversion, labelme, legacy, object-detection, vott
Last push: 2026-08-20T19:34:06+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 8, longevity 100
- inputs: {"age_days": 2671, "days_push": 13, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1231, forks 260 (observed 2026-08-28T04:04:04.310100+00:00)

## What it is
A legacy Python toolkit that converts JSON-format annotation datasets (COCO, LabelMe, Labelbox, VoTT, INFOLKS, ATH) into the YOLO format for object detection training. It has been superseded by the convert_coco() function in the main Ultralytics package and is no longer actively updated.

## Use cases
- convert COCO JSON annotations to YOLO format
- convert LabelMe annotations for YOLO training
- convert Labelbox or VoTT datasets to YOLO
- prepare object detection datasets for Ultralytics YOLO
- batch convert annotation files to YOLO labels

## When to choose
- you need a standalone script to convert legacy annotation formats like VoTT, INFOLKS, or ATH
- you're maintaining an older pipeline that already uses JSON2YOLO
- you want a minimal converter without installing the full ultralytics package

## When to avoid
- starting a new project - use convert_coco() in the ultralytics package instead
- you need actively maintained or updated conversion features
- you need formats beyond the supported JSON annotation sources

## Facets
- artifact type: library
- maturity: maintenance
- function: etl, data-generation, machine-learning
- domain: computer-vision, machine-learning, deep-learning, files
- platform: python, windows, cross-platform
- tags: yolo, coco, annotation-conversion, dataset-conversion, labelme, labelbox, vott, object-detection, legacy, json, linux, macos

## Member repositories
- ultralytics/JSON2YOLO (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.310100+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:32.543917+00:00, confidence not recorded.
  - readme: https://github.com/ultralytics/JSON2YOLO (fetched 2026-08-28T04:04:04.310100+00:00, sha 13b12e08d47c)
  - homepage: https://docs.ultralytics.com (fetched 2026-08-29T12:22:37.986499+00:00, sha fba5847c3417)
  - site_page: https://docs.ultralytics.com/integrations (fetched 2026-08-29T12:22:37.999948+00:00, sha e1ab3aff79a6)
  - site_page: https://docs.ultralytics.com/quickstart (fetched 2026-08-29T12:22:37.997450+00:00, sha 2e46a5002390)
- Data as of 2026-08-30T08:39:29.467469+00:00.