Metadata-Version: 2.1
Name: autodistill-detic
Version: 0.1.3
Summary: DETIC module for use with Autodistill
Home-page: https://github.com/autodistill/autodistill-detic
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.md
Requires-Dist: torch
Requires-Dist: supervision
Requires-Dist: numpy
Requires-Dist: autodistill
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'

<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 DETIC Module

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

[DETIC](https://github.com/facebookresearch/Detic) is a transformer-based object detection and segmentation model developed by Meta Research.

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

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

## Installation

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


```bash
pip3 install autodistill-detic
```

## Quickstart

```python
from autodistill_detic import DETIC

# define an ontology to map class names to our DETIC 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 = DETIC(
    ontology=CaptionOntology(
        {
            "person": "person",
        }
    )
)
base_model.label("./context_images", extension=".jpg")
```

## License

The code in this repository is licensed under an [MIT license](LICENSE).

See the Meta Research DETIC repository for more information on the [DETIC license](https://github.com/facebookresearch/Detic).

## 🏆 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!
