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<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
>
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# Autodistill OWL-ViT Module
This repository contains the code supporting the OWL-ViT base model for use with [Autodistill](https://github.com/autodistill/autodistill).
[OWL-ViT](https://huggingface.co/google/owlvit-base-patch32) is a transformer-based object detection model developed by Google Research.
Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).
Read the [OWL-ViT Autodistill documentation](https://autodistill.github.io/autodistill/base_models/owlvit/).
## Installation
To use OWL-ViT with autodistill, you need to install the following dependency:
```bash
pip3 install autodistill-owl-vit
```
## Quickstart
```python
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](LICENSE).
## 🏆 Contributing
We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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