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✍️ Update README for 3D landmarks
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jejon committed Jun 13, 2024
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[![Testing](https://github.com/predict-idlab/landmarker/actions/workflows/tests.yml/badge.svg)](https://github.com/predict-idlab/landmarker/actions/workflows/tests.yml)


Landmarker is a [PyTorch](https://pytorch.org/)-based toolkit for (anatomical) landmark detection in images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark detection algorithms for small and large datasets. Landmarker was developed for landmark detection in medical images. However, it can be used for any type of landmark detection problem.
Landmarker is a [PyTorch](https://pytorch.org/)-based toolkit for (anatomical) landmark detection in 2D/3D images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark detection algorithms for small and large datasets. Landmarker was developed for landmark detection in medical images. However, it can be used for any type of landmark detection problem.

## 🛠️ Installation

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Examples and tutorials are available at [examples](https://predict-idlab.github.io/landmarker/examples/index.html)

## ✨ Features
- **Modular**: Landmarker is designed to be modular. It is easy to add new models, datasets, and loss functions.
- **Flexible**: Landmarker provides a flexible framework for landmark detection. It is easy to customize the training and evaluation process.
- **Easy to use**: Landmarker is easy to use. It provides a simple API for training and evaluation.
- **Modular**: Landmarker is designed to be modular. Almost all components can be used independently.
- **Flexible**: Landmarker provides a flexible framework for landmark detection, allowing you to easily customize your model, loss function, and data loaders.
- **State-of-the-art**: Landmarker provides state-of-the-art landmark detection models and loss functions.

## 📈 Future Work
- Extension to 3D landmark detection.
- Extension to landmark detection in videos.
- Add uncertainty estimation.
- ...
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