Models

What is GroundingDINO?

Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.

About the model

Here is an overview of the

GroundingDINO

model:

Date of Release
Model Type Object Detection
Architecture
Framework Used PyTorch
Annotation Format
Stars on GitHub 1100+

Grounding DINO is a zero-shot object detection model made by combining a Transformer-based DINO detector and grounded pre-training.

According to the Grounding DINO paper abstract, the model achieves "a 52.5 AP on the COCO detection zero-shot transfer benchmark".

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Model Performance

name backbone Data box AP on COCO Checkpoint Config
1 GroundingDINO-T Swin-T O365,GoldG,Cap4M 48.4 (zero-shot) / 57.2 (fine-tune) Github link | HF link link

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GroundingDINO Annotation Format

GroundingDINO

uses the

uses the

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with GroundingDINO

You can automatically label a dataset using

GroundingDINO

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

GroundingDINO

to train a computer vision model.

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