Autodistill: YOLO-World Base Model

This repository contains the code implementing YOLO-World as a Base Model for use with autodistill.

YOLO-World combines YOLO-World brings YOLO like efficiency for training and inferring open-vocabulary models

Read the full Autodistill documentation.

Installation

To use the YOLO-World, simply install it along with a Target Model supporting the detection task:

pip3 install autodistill-yolo-world

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

Quickstart

from autodistill_yolo_world import YoloWorld
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 = YoloWorld(
    ontology=CaptionOntology(
        {
            "person": "person",
            "car": "car",
        }
    ),
    model_type = "yolov8s-world.pt"
)

# run inference on a single image
results = base_model.predict("assets/test.jpg")

plot(
    image=cv2.imread("assets/test.jpg"),
    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.

🏆 Thanks

Thanks to autodistill and ultralytics