Overview

OWL-ViT is a transformer-based object detection model developed by Google Research.

Performance

Use This Model

First, install Autodistill and Autodistill OWL-ViT:


pip install autodistill autodistill-owl-vit

Then, run:


from autodistill_owl_vit import OWLViT
from autodistill.detection import CaptionOntology

# define an ontology to map class names to our OWLViT 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 = OWLViT(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
result = base_model.predict("image.jpeg")
print(result)

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Label Data Automatically with OWL ViT

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

OWL-ViT