🚧 Some refactoring changes + add patchdataset

predict-idlab · Aug 15, 2024 · 1244d97 · 1244d97
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diff --git a/.gitignore b/.gitignore
@@ -30,9 +30,10 @@ test_notebooks
 .vscode
 tags
 junit
+
 # Unittest and coverage
 htmlcov/*
-.coverage
+.coverage*
 .tox
 junit.xml
 coverage.xml
@@ -47,4 +48,12 @@ wheel/*
 docs/_build/*
 cover/*
 MANIFEST
-.pdm-python
+.pdm-python
+
+# example files
+examples/configs/*
+examples/datasets/*
+examples/lightning_logs/*
+examples/lightning_models/*
+examples/main.py
+*model_checkpoints/*
diff --git a/README.md b/README.md
@@ -13,7 +13,7 @@
 [![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 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.
+Landmarker is a [PyTorch](https://pytorch.org/)-based toolkit for (anatomical) landmark localization in 2D/3D images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art landmark localization 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 localization problem.
 
 ## 🛠️ Installation
 

diff --git a/examples/adaptive_scn_isbi2015.ipynb → examples/ISBI2015.ipynb b/examples/adaptive_scn_isbi2015.ipynb → examples/ISBI2015.ipynb