Autodistill: GroundedSAM Base Model

This repository contains the code implementing GroundedSAM as a Base Model for use with autodistill.

GroundedSAM combines GroundingDINO with the Segment Anything Model to identify and segment objects in an image given text captions.

Read the full Autodistill documentation.

Read the GroundedSAM Autodistill documentation.

Tip

You can use Autodistill Grounded SAM on your own hardware using the instructions below, or use the Roboflow hosted version of Autodistill to label images in the cloud.

Installation

To use the GroundedSAM Base Model, simply install it along with a Target Model supporting the detection task:

pip3 install autodistill-grounded-sam autodistill-yolov8

You can find a full list of detection Target Models on the main autodistill repo.

Quickstart

from autodistill_grounded_sam import GroundedSAM
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2

# define an ontology to map class names to our GroundedSAM 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 = GroundedSAM(
    ontology=CaptionOntology(
        {
            "person": "person",
            "shipping container": "shipping container",
        }
    )
)

# run inference on a single image
results = base_model.predict("logistics.jpeg")

plot(
    image=cv2.imread("logistics.jpeg"),
    classes=base_model.ontology.classes(),
    detections=results
)
# label all images in a folder called `context_images`
base_model.label("./context_images", extension=".jpeg")

License

The code in this repository is licensed under an Apache 2.0 license.

🏆 Contributing

We love your input! Please see the core Autodistill contributing guide to get started. Thank you 🙏 to all our contributors!