# lxtGH/OMG-Seg

Official Repo For OMG-LLaVA and OMG-Seg codebase [CVPR-24 and NeurIPS-24]

Repository: https://github.com/lxtGH/OMG-Seg
Canonical: https://ross.abutalabs.com/products/omg-seg
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-10-15T13:07:49+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 47, release rhythm 35, longevity 69
- inputs: {"age_days": 971, "days_push": 322, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1354, forks 55 (observed 2026-08-28T04:04:29.055146+00:00)

## What it is
Official research codebase for OMG-Seg (CVPR 2024) and OMG-LLaVA (NeurIPS 2024), unified models for image-level, object-level, and pixel-level visual perception and reasoning. OMG-LLaVA combines a universal segmentation encoder with an LLM to support text and visual prompts for flexible interaction.

## Use cases
- run promptable image segmentation with a single unified model
- combine an LLM with pixel-level segmentation for visual reasoning
- reproduce OMG-Seg and OMG-LLaVA paper results
- build a multimodal chatbot that outputs segmentation masks
- train a universal segmentation model on multiple benchmarks
- interact with images using point, box, or text prompts

## When to choose
- you need one model handling semantic, instance, and panoptic segmentation plus reasoning
- you want a research baseline for LLM-driven segmentation
- you need pixel-level outputs from a multimodal LLM

## When to avoid
- you need a lightweight production segmentation service without LLM overhead
- you lack GPU resources for large multimodal training or inference
- you need a permissively licensed library for commercial embedding

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, image-processing, machine-learning, deep-learning, llm-inference, nlp
- domain: computer-vision, image-processing, large-language-models, artificial-intelligence, deep-learning
- platform: python
- tags: segmentation, multimodal, visual-reasoning, research-code, cvpr, neurips, promptable-segmentation, gpu, linux

## Member repositories
- lxtGH/OMG-Seg (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:29.055146+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 2026-08-30T04:42:02.691338+00:00, confidence not recorded.
  - readme: https://github.com/lxtGH/OMG-Seg (fetched 2026-08-28T04:04:29.055146+00:00, sha 6e296adcd068)
- Data as of 2026-08-30T08:39:29.467469+00:00.