spytensor/prepare_detection_dataset

Health v2 · maintenance only

Adoption not part of the score

1107 stars · 353 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

library · maturity active

data-science computer-vision csv json data-generation computer-vision machine-learning files python cli cross-platform object-detection dataset-conversion coco-format voc-format labelme annotation-tools data-preparation data-engineering

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markdown · JSON · MCP: product_card(name="spytensor/prepare_detection_dataset")