# anuragxel/salt

Segment Anything Labelling Tool

Repository: https://github.com/anuragxel/salt
Canonical: https://ross.abutalabs.com/products/anuragxel-salt
Language: Python
License: MIT
License Family: permissive
Last push: 2024-02-19T16:49:54+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 88
- inputs: {"age_days": 1244, "days_push": 926, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1048, forks 126 (observed 2026-08-28T04:03:22.531505+00:00)

## What it is
SALT is a Python-based image labeling tool built on Meta AI's Segment Anything Model, providing a barebones GUI for annotating images with predicted segmentation masks. It saves annotations in COCO format and uses precomputed embeddings and ONNX models so the labeling machine does not need a GPU.

## Use cases
- label images for instance segmentation datasets
- generate COCO format annotations for object detection training
- speed up image segmentation annotation with SAM
- annotate datasets without a GPU on the labeling machine
- create segmentation masks by clicking on objects

## When to choose
- you need COCO-format segmentation annotations for training models
- you want SAM-assisted click-based labeling to speed up annotation
- your labeling machine lacks a GPU since inference runs elsewhere via embeddings/ONNX

## When to avoid
- you need a polished, stable annotation UI - the tool is explicitly rough and under active development
- you need polygon/box-only annotation without segmentation masks
- you want a web-based or multi-user annotation platform

## Facets
- artifact type: application
- maturity: active
- function: image-processing, computer-vision, machine-learning, gui
- domain: computer-vision, image-processing, machine-learning, developer-tools
- platform: python, cross-platform
- tags: segment-anything, image-annotation, coco-format, data-labeling, onnx, computer-vision-datasets, desktop, gpu

## Member repositories
- anuragxel/salt (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:22.531505+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 2026-08-30T07:00:41.213539+00:00, confidence not recorded.
  - readme: https://github.com/anuragxel/salt (fetched 2026-08-28T04:03:22.531505+00:00, sha 2a38e4f7bffa)
- Data as of 2026-08-30T08:39:29.467469+00:00.