# IDEA-Research/GroundingDINO

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

Repository: https://github.com/IDEA-Research/GroundingDINO
Canonical: https://ross.abutalabs.com/products/groundingdino
Homepage: https://arxiv.org/abs/2303.05499
Language: Python
License: Apache-2.0
License Family: permissive
Topics: object-detection, open-world, open-world-detection, vision-language, vision-language-transformer
Last push: 2024-08-12T08:52:02+00:00

## Health v2 (maintenance only)
Score: 21/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 90
- inputs: {"age_days": 1273, "days_push": 751, "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 10515, forks 1065 (observed 2026-08-28T04:10:42.206796+00:00)

## What it is
Official PyTorch implementation of Grounding DINO, an open-set object detector that fuses a Transformer-based DINO detector with grounded vision-language pre-training. It detects arbitrary objects specified by text inputs such as category names or referring expressions, achieving state-of-the-art zero-shot results on COCO, LVIS, ODinW, and RefCOCO benchmarks.

## Use cases
- detect arbitrary objects in images using text prompts
- zero-shot object detection without training on target categories
- detect objects by referring expressions like 'the man in a red hat'
- generate bounding boxes to feed into SAM for segmentation
- label datasets automatically with open-vocabulary detection
- integrate open-world detection into robotics or video tracking pipelines

## When to choose
- you need to detect objects beyond a fixed set of trained categories using natural language
- you want strong zero-shot detection performance with pretrained checkpoints
- you need a detector backbone to combine with SAM or Grounded SAM pipelines
- you want a Hugging Face-compatible open-vocabulary detection model

## When to avoid
- you need a lightweight detector for edge devices with strict latency budgets
- you only need closed-set detection on a fixed category list where a smaller YOLO-style model suffices
- you need a GPU-free CPU-only deployment
- you need a maintained API service rather than a research codebase

## Facets
- artifact type: library
- maturity: stable
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, machine-learning, artificial-intelligence, image-processing
- platform: python, cross-platform
- tags: object-detection, open-set-detection, vision-language, zero-shot-detection, transformer, pytorch, grounded-pretraining, eccv-2024, gpu

## Member repositories
- IDEA-Research/GroundingDINO (main) score 21

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:42.206796+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 2026-08-29T17:18:35.131650+00:00, confidence not recorded.
  - readme: https://github.com/IDEA-Research/GroundingDINO (fetched 2026-08-28T04:10:42.206796+00:00, sha 5211c2a0edb0)
  - homepage: https://arxiv.org/abs/2303.05499 (fetched 2026-08-29T08:18:00.617310+00:00, sha 339f5f1a37c5)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T08:18:00.620147+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T08:18:00.623587+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T08:18:00.625398+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T08:18:00.621860+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.