# spytensor/prepare_detection_dataset

convert dataset to coco/voc format

Repository: https://github.com/spytensor/prepare_detection_dataset
Canonical: https://ross.abutalabs.com/products/prepare_detection_dataset
Language: Python
License: MIT
License Family: permissive
Topics: coco, voc, labelme, csv, detection
Last push: 2026-08-06T17:30:27+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 8, longevity 100
- inputs: {"age_days": 2724, "days_push": 27, "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 1107, forks 353 (observed 2026-08-28T04:03:36.685691+00:00)

## What it is
A collection of Python scripts that convert object detection datasets between common annotation formats, including CSV, LabelMe JSON, COCO, and Pascal VOC. It also documents the standard directory layouts for each format to help prepare training data.

## Use cases
- convert labelme annotations to coco format
- convert csv bounding boxes to voc xml
- prepare object detection dataset for training
- convert labelme json to pascal voc
- split detection dataset into train and val sets
- convert csv annotations to coco json

## When to choose
- you need to reformat detection annotations between CSV, LabelMe, COCO, or VOC layouts
- you are preparing a custom object detection dataset for training frameworks that expect COCO or VOC
- you want reference scripts showing the standard COCO/VOC directory structures

## When to avoid
- you need segmentation or keypoint annotation conversion rather than bounding boxes
- you want a maintained GUI annotation tool rather than conversion scripts
- your annotation format differs significantly from the expected CSV schema and you prefer a configurable tool

## Facets
- artifact type: library
- maturity: active
- function: data-science, computer-vision, csv, json, data-generation
- domain: computer-vision, machine-learning, files
- platform: python, cli, cross-platform
- tags: object-detection, dataset-conversion, coco-format, voc-format, labelme, annotation-tools, data-preparation, data-engineering

## Member repositories
- spytensor/prepare_detection_dataset (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.685691+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 2026-08-30T06:44:05.545266+00:00, confidence not recorded.
  - readme: https://github.com/spytensor/prepare_detection_dataset (fetched 2026-08-28T04:03:36.685691+00:00, sha c302530444e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.