Metadata-Version: 2.1
Name: autodistill_detr
Version: 0.1.0
Summary: DETR module for use with Autodistill
Home-page: https://github.com/autodistill/autodistill-detr
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

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# Autodistill DETR Module

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

[DETR](https://huggingface.co/facebook/detr-resnet-50) is a transformer-based computer vision model you can use for object detection. Autodistill supports use of the DETR Resnet 50 model developed by Meta Research.

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

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

## Installation

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


```bash
pip3 install autodistill-detr
```

## Quickstart

```python
from autodistill_detr import DETR

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

## License

The code in this repository is licensed under an .

## 🏆 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!
