# SkalskiP/make-sense

Free to use online tool for labelling photos. https://makesense.ai

Repository: https://github.com/SkalskiP/make-sense
Canonical: https://ross.abutalabs.com/products/make-sense
Language: TypeScript
License: GPL-3.0
License Family: copyleft
Topics: deep-learning, image-annotation, detection, tagging, object-detection, labeling-tool, computer-vision, landmark-detection, pascal-voc, labeling-photos, posenet-model, ssd-model
Last push: 2024-08-15T14:50:41+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2637, "days_push": 748, "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 3562, forks 587 (observed 2026-08-28T04:08:09.844109+00:00)

## What it is
makesense.ai is a free, browser-based tool for labeling photos to prepare datasets for computer vision projects. It runs entirely client-side with TensorFlow.js-powered AI assistance (SSD, YOLOv5, PoseNet) for suggesting annotations, and exports labels in multiple formats.

## Use cases
- annotate images with bounding boxes for object detection training
- label photos for a small deep learning dataset without installing software
- export image annotations in Pascal VOC or YOLO format
- use AI-assisted auto-labeling to speed up image annotation
- label pose keypoints on images with PoseNet
- annotate images privately without uploading photos to a server

## When to choose
- you need a quick, free, no-install image annotation tool for small-to-medium CV projects
- you want AI-assisted labeling that keeps your images on your device
- you need multiple export formats like Pascal VOC, YOLO, or CSV

## When to avoid
- you need large-team annotation workflows with task assignment and quality control
- you require video or 3D/point-cloud annotation
- you need offline desktop tooling or heavy customization beyond a web app

## Facets
- artifact type: application
- maturity: active
- function: image-processing, computer-vision, machine-learning, ui-components
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence, web-development
- platform: browser, cross-platform
- tags: image-annotation, labeling-tool, object-detection, bounding-boxes, pascal-voc, yolo, posenet, tensorflowjs, react, typescript, dataset-preparation, privacy-preserving, web-server

## Member repositories
- SkalskiP/make-sense (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.844109+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 2026-08-29T18:34:12.295599+00:00, confidence not recorded.
  - readme: https://github.com/SkalskiP/make-sense (fetched 2026-08-28T04:08:09.844109+00:00, sha 42129bcfd0bc)
- Data as of 2026-08-30T08:39:29.467469+00:00.