Overview

OWLv2 is a transformer-based object detection model developed by Google Research. OWLv2 is the successor to OWL ViT.

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Performance

Use This Model

First, install Autodistill and Autodistill OWLv2:


pip install autodistill autodistill-owlv2

Then, run:


from autodistill_owlv2 import OWLv2
from autodistill.detection import CaptionOntology
from autodistill.utils import plot
import cv2

# define an ontology to map class names to our OWLv2 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 = OWLv2(
    ontology=CaptionOntology(
        {
            "person": "person",
            "dog": "dog"
        }
    )
)

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

plot(
    image=cv2.imread("dog.jpeg"),
    classes=base_model.ontology.classes(),
    detections=results
)

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Label Data Automatically with OWLv2

You can automatically label a dataset using OWLv2 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 OWLv2 to train a computer vision model.

OWLv2