Metadata-Version: 2.1
Name: autodistill-grounding-dino
Version: 0.1.2
Summary: GroundingDINO module for use with Autodistill
Home-page: https://github.com/autodistill/autodistill-grounding-dino
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
Provides-Extra: dev
License-File: LICENSE

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# Autodistill Grounding DINO Module

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

[Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) is a zero-shot object detection model developed by IDEA Research. You can distill knowledge from Grounding DINO into a smaller model using Autodistill.

Read the [Grounding DINO Autodistill documentation](https://autodistill.github.io/autodistill/base_models/grounding-dino/).

## Installation

To use the Grounding DINO base model, you will need to install the following dependency:

```bash
pip3 install autodistill-grounding-dino
```

## Quickstart

```python
from autodistill_grounding_dino import GroundingDINO
from autodistill_yolov8 import YOLOv8


# define an ontology to map class names to our GroundingDINO 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 = GroundedSAM(ontology=CaptionOntology({"shipping container": "container"}))

# label all images in a folder called `context_images`
base_model.label("./context_images", extension=".jpeg")
```

## 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!
