Metadata-Version: 2.4
Name: litdet
Version: 0.1.0b3
Summary: An auto-ML python library for computer vision based on pytorch lightning and hydra.
Author-email: Loic Tetrel <loic.tetrel@kitware.com>, Louis Pagnier <louis.pagnier@kitware.com>
Maintainer-email: Kitware SAS <keu-vision@kitware.com>
Project-URL: Homepage, https://gitlab.kitware.com/litdet/litdet
Project-URL: Bug Tracker, https://gitlab.kitware.com/litdet/litdet/-/boards
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Information Technology
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch<2.6,>=2.5
Requires-Dist: torchvision<0.21,>=0.20.0
Requires-Dist: lightning<2.6,>=2.5
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Requires-Dist: hydra-core>=1.3
Requires-Dist: hydra-colorlog>=1.2
Requires-Dist: hydra-optuna-sweeper>=1.2
Requires-Dist: rich<14.1,>=14.0
Provides-Extra: extras
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Requires-Dist: aim>=3.16.2; extra == "extras"
Requires-Dist: plotly>=6.5.2; extra == "extras"
Requires-Dist: pyqt5; extra == "extras"
Provides-Extra: test
Requires-Dist: litdet[extras]; extra == "test"
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-cov; extra == "test"
Provides-Extra: dev
Requires-Dist: pre-commit<4; extra == "dev"
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Provides-Extra: doc
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Requires-Dist: furo>=2023.08.17; extra == "doc"
Dynamic: license-file

# LitDet

![Version](https://img.shields.io/badge/Version-v0.1.0-blue)
[![PyPI](https://img.shields.io/badge/PyPI-available-blue?logo=pypi&logoColor=white)](https://pypi.org/search/?q=litdet)
[![Documentation Status](https://readthedocs.org/projects/litdet/badge/?version=latest)](https://litdet.readthedocs.io/en/?badge=latest)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellogreen.svg)](https://opensource.org/licenses/Apache-2.0)

[![Python](https://img.shields.io/badge/python-3.10--3.14-blue?logo=python)](https://www.python.org/)
[![pytorch](https://img.shields.io/badge/PyTorch_2.5+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
[![lightning](https://img.shields.io/badge/-Lightning_2.5+-792ee5?logo=pytorchlightning&logoColor=white)](https://pytorchlightning.ai/)
[![hydra](https://img.shields.io/badge/Config-Hydra_1.3+-89b8cd)](https://hydra.cc/)

![Platform](https://img.shields.io/badge/Platform-linux--64_%7C_win--64_wsl2%7C_aarch64-gray)
[![CUDA](https://img.shields.io/badge/CUDA-v11.8_%7c_12.1_%7c_v12.4-%2376B900?logo=nvidia)](https://developer.nvidia.com/cuda-toolkit-archive)

*LitDet* is a domain-agnostic AutoML framework to accelerate the development of 2D object detection models 🚀⚡🔥.

- **ML Workflows**: Handles model pre-training, fine-tuning or re-training at scale.
- **Key Features**: Provides a high-level CLI and python API for seamless hardware support (CPU/GPU), COCO-formatted dataset integration, and built-in experiment tracking.
- **Tech Stack**: Built on the `Lightning` framework, it leverages `PyTorch` and `Hydra` for flexible, configuration-driven deep learning workflows.

<!-- [![tests](https://github.com/ashleve/lightning-hydra-template/actions/workflows/test.yml/badge.svg)](https://github.com/ashleve/lightning-hydra-template/actions/workflows/test.yml)
[![codecov](https://codecov.io/gh/ashleve/lightning-hydra-template/branch/main/graph/badge.svg)](https://codecov.io/gh/ashleve/lightning-hydra-template)
[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/ashleve/lightning-hydra-template/pulls) -->

<img src="./logo.png" width="500" alt="LitDet overview">

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## Quickstart

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You will need at least `python 3.10` and a recent GPU driver (e.g. [NVIDIA 525.147.05](https://docs.nvidia.com/datacenter/tesla/tesla-release-notes-525-147-05/index.html)).

1. First install the package (best in a [python venv](https://docs.python.org/3/library/venv.html)).

   ```bash
   python -m pip install "litdet[extras]"
   ```

2. Train a Faster-RCNN on your dataset annotated in COCO format at `/path/to/your/dir/coco_dataset_name`:

   ```bash
   light-train task.model=faster-rcnn paths.data_dir=/path/to/your/dir data.data_name=coco_dataset_name task.model.num_classes=10 trainer.max_epochs=100
   ```

You can find configuration examples in the `/examples` directory.

To build your own configuration file instead, use our [cookiecutter](https://www.cookiecutter.io/templates):

```bash
cookiecutter https://gitlab.kitware.com/litdet/litdet.git --directory "cookiecutter-litdet"
```

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For more information on the usage, configuration, API documentation, and getting started guides, refer to our [online documentation](https://litdet.readthedocs.io/en/latest).

## Developers

See [CONTRIBUTING](CONTRIBUTING.md) for developers instructions such as code practices, or the review process.
Check also our [advanced usage guide](https://litdet.readthedocs.io/en/advanced/configuration.html), and [debugging process](https://litdet.readthedocs.io/en/developper/debugging.html).

## Citation

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You can cite *LitDet* with the following:

```
@article{litdet2026tetrel,
  title={LitDet: Finetuning detection models has never been so easy},
  author={},
  journal={JOSS},
  year={2026}
}
```

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## Model Zoo

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The *LitDet* model zoo serves as a centralized hub for users and developers to discover models.

You can explore the complete list of available pre-trained models directly in [our GitLab repository](https://gitlab.kitware.com/litdet/model-zoo).

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## Acknowledgements

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