This repository contains the code supporting the Grounding DINO base model for use with Autodistill.
Grounding DINO is a zero-shot object detection model developed by IDEA Research. You can distill knowledge from Grounding DINO into a smaller model using Autodistill.
Read the Grounding DINO Autodistill documentation.
Tip
You can use Autodistill Grounding DINO on your own hardware, or use the Roboflow hosted version of Autodistill to label images in the cloud.
To use the Grounding DINO base model, you will need to install the following dependency:
pip3 install autodistill autodistill-yolov8 autodistill-grounding-dinofrom autodistill_grounding_dino import GroundingDINO
from autodistill.detection import CaptionOntology
from autodistill_yolov8 import YOLOv8
# define an ontology to map class names to our GroundingDINO prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = GroundingDINO(ontology=CaptionOntology({"shipping container": "container"}))
# label all images in a folder called `context_images`
base_model.label("./context_images", extension=".jpeg")The code in this repository is licensed under an Apache 2.0 license.
We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!