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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
 
@@ -28,13 +28,11 @@ Technical documentation is available at [documentation](https://predict-idlab.gi
 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.
 - ...