Metadata-Version: 2.1
Name: autodistill-detr
Version: 0.1.1
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
License-File: LICENSE
Requires-Dist: supervision ==0.9.0
Requires-Dist: transformers
Requires-Dist: torch
Requires-Dist: numpy
Requires-Dist: Pillow
Requires-Dist: autodistill
Requires-Dist: timm
Requires-Dist: pytorch-lightning
Requires-Dist: torchvision
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 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/docs/transformers/model_doc/detr) is a transformer-based computer vision model you can use for object detection. Autodistill supports training a model using the Meta Research Resnet 50 checkpoint.

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

Read the [DETR Autodistill documentation](https://autodistill.github.io/autodistill/target_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

# load the model
target_model = DETR()

# train for 10 epochs
target_model.train("./roads", epochs=10)

# run inference on an image
target_model.predict("./roads/valid/-3-_jpg.rf.bee113a09b22282980c289842aedfc4a.jpg")
```

## License

This project is licensed under an [Apache 2.0 license](LICENSE). See the [Hugging Face model card for the DETR Resnet 50](https://huggingface.co/facebook/detr-resnet-50) model for more information on the model 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!
