# microsoft/GLIP

Grounded Language-Image Pre-training

Repository: https://github.com/microsoft/GLIP
Canonical: https://ross.abutalabs.com/products/glip
Language: Python
License: MIT
License Family: permissive
Last push: 2024-01-24T04:56:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1744, "days_push": 952, "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 2607, forks 218 (observed 2026-08-28T04:07:04.167983+00:00)

## What it is
GLIP is Microsoft's official implementation of Grounded Language-Image Pre-training, a vision-language model that unifies object detection and phrase grounding by reformulating detection as a grounding task. It achieves strong zero-shot and few-shot transfer to object-level recognition tasks like COCO and LVIS detection.

## Use cases
- detect arbitrary objects in images using natural language prompts
- zero-shot object detection without training on target dataset
- ground text phrases to image regions
- fine-tune a detection model on custom classes with few examples
- evaluate open-vocabulary detection benchmarks like COCO and LVIS

## When to choose
- you need open-vocabulary or text-prompted object detection
- you want a research-grade pretrained vision-language detection model
- you have few labeled examples and need strong few-shot transfer

## When to avoid
- you need a lightweight production detector with minimal dependencies
- you only need standard closed-set detection with fixed classes
- you lack GPU resources for large vision-language models

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, machine-learning, image-processing, deep-learning
- domain: computer-vision, machine-learning, artificial-intelligence
- platform: python
- tags: object-detection, grounded-language-image-pretraining, zero-shot-detection, vision-language, open-set-detection, research-code, linux, gpu

## Member repositories
- microsoft/GLIP (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.167983+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 2026-08-30T02:20:57.210638+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/GLIP (fetched 2026-08-28T04:07:04.167983+00:00, sha d29b9bf48442)
- Data as of 2026-08-30T08:39:29.467469+00:00.