Awesome
<div align="center"> <p> <a align="center" href="" target="_blank"> <img width="850" src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png" > </a> </p> </div>Autodistill OWL-ViT Module
This repository contains the code supporting the OWL-ViT base model for use with Autodistill.
OWL-ViT is a transformer-based object detection model developed by Google Research.
Read the full Autodistill documentation.
Read the OWL-ViT Autodistill documentation.
Installation
To use OWL-ViT with autodistill, you need to install the following dependency:
pip3 install autodistill-owl-vit
Quickstart
from autodistill_owl_vit import OWLViT
# 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"
}
)
)
base_model.label("./context_images", extension=".jpg")
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!