Metadata-Version: 2.1
Name: autodistill-owlv2
Version: 0.1.1
Summary: OWLv2 base model for use with Autodistill.
Home-page: https://github.com/autodistill/autodistill-owlv2
Author: Roboflow
Author-email: support@roboflow.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: autodistill
Requires-Dist: supervision
Provides-Extra: dev
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: black ==22.3.0 ; extra == 'dev'
Requires-Dist: isort ; extra == 'dev'
Requires-Dist: twine ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: wheel ; extra == 'dev'

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# Autodistill OWLv2 Module

This repository contains the code supporting the OWLv2 base model for use with [Autodistill](https://github.com/autodistill/autodistill).

OWLv2 is a zero-shot object detection model that follows from on the OWL-ViT architecture. OWLv2 has an open vocabulary, which means you can provide arbitrary text prompts for the model. You can use OWLv2 with autodistill for object detection.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [OWLv2 Autodistill documentation](https://autodistill.github.io/autodistill/base_models/owlv2/).

## Installation

To use OWLv2 with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-owlv2
```

## Quickstart

```python
from autodistill_owlv2 import OWLv2
from autodistill.detection import CaptionOntology

# 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",
            "a forklift": "forklift"
        }
    )
)

# run inference on a single image
results = base_model.predict("./context_images/image.png")

base_model.label("./context_images", extension=".jpeg")
```


## License

This model is licensed under an [Apache 2.0](LICENSE) ([see original model implementation license](https://huggingface.co/docs/transformers/main/en/model_doc/owlv2), and the corresponding [HuggingFace Transformers documentation](https://huggingface.co/docs/transformers/main/en/model_doc/owlv2)).

## 🏆 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!
