diff --git a/.napari/DESCRIPTION.md b/.napari/DESCRIPTION.md
index 9a3143bb..d379ed68 100644
--- a/.napari/DESCRIPTION.md
+++ b/.napari/DESCRIPTION.md
@@ -117,7 +117,7 @@ this information here.
 ## Getting Help
 
 If you would like to report an issue with the plugin,
-please open an [issue on Github](https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues)
+please open an [issue on Github](https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues)
 <!--
 This section should point users to your preferred support tools, whether this be raising
 an issue on GitHub, asking a question on image.sc, or using some other method of contact.
diff --git a/.napari/config.yml b/.napari/config.yml
index e88d83ae..3082df1b 100644
--- a/.napari/config.yml
+++ b/.napari/config.yml
@@ -2,11 +2,11 @@ visibility: public
 # note : remember to add napari-hub preview app to repo
 
 project_urls:
-  Bug Tracker: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
-  Documentation: https://adaptivemotorcontrollab.github.io/CellSeg3d/res/welcome.html
-  Source Code: https://github.com/AdaptiveMotorControlLab/CellSeg3d
-  Project Site: https://github.com/AdaptiveMotorControlLab/CellSeg3d
-  User Support: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
+  Bug Tracker: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
+  Documentation: https://adaptivemotorcontrollab.github.io/CellSeg3D/res/welcome.html
+  Source Code: https://github.com/AdaptiveMotorControlLab/CellSeg3D
+  Project Site: https://github.com/AdaptiveMotorControlLab/CellSeg3D
+  User Support: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
 
 # Add labels from the EDAM Bioimaging ontology
 labels:
diff --git a/README.md b/README.md
index b2b922f8..635403a6 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,9 @@
 [![PyPI](https://img.shields.io/pypi/v/napari-cellseg3d.svg?color=green)](https://pypi.org/project/napari-cellseg3d)
 [![Downloads](https://static.pepy.tech/badge/napari-cellseg3d)](https://pepy.tech/project/napari-cellseg3d)
 [![Downloads](https://static.pepy.tech/badge/napari-cellseg3d/month)](https://pepy.tech/project/napari-cellseg3d)
-[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3d/raw/main/LICENSE)
+[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/AdaptiveMotorControlLab/CellSeg3D/raw/main/LICENSE)
 [![Python Version](https://img.shields.io/pypi/pyversions/napari-cellseg-annotator.svg?color=green)](https://python.org)
-[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3d)
+[![codecov](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D/branch/main/graph/badge.svg?token=hzUcn3XN8F)](https://codecov.io/gh/AdaptiveMotorControlLab/CellSeg3D)
 [![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-cellseg3d)](https://www.napari-hub.org/plugins/napari-cellseg3d)
 
 - A napari plugin for 3D cell segmentation: training, inference, and data review. In particular, this project was developed for analysis of mesoSPIM-acquired (cleared tissue + lightsheet) datasets.
@@ -20,7 +20,7 @@
 
 ## Documentation
 
-📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3d
+📚 A lot of documentation is available at https://AdaptiveMotorControlLab.github.io/CellSeg3D
 
 You can also generate docs by running ``make html`` in the docs/ folder.
 
@@ -70,6 +70,7 @@ To avoid issues when installing on the ARM64 architecture, please follow these s
         conda env create -f conda/CellSeg3D_ARM64.yml
         conda activate napari_CellSeg3D_ARM64
 
+
 2) Install a Qt backend (PySide or PyQt5)
 3) Launch napari, the plugin should be available in the plugins menu.
 
@@ -137,7 +138,7 @@ Distributed under the terms of the [MIT] license.
 
 [napari-hub]: https://www.napari-hub.org/plugins/napari-cellseg3d
 
-[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
+[file an issue]: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
 [napari]: https://github.com/napari/napari
 [Cookiecutter]: https://github.com/audreyr/cookiecutter
 [@napari]: https://github.com/napari
@@ -146,7 +147,7 @@ Distributed under the terms of the [MIT] license.
 [tox]: https://tox.readthedocs.io/en/latest/
 [pip]: https://pypi.org/project/pip/
 [PyPI]: https://pypi.org/
-[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3d/source/guides/installation_guide.html
+[Installation page]: https://adaptivemotorcontrollab.github.io/CellSeg3D/source/guides/installation_guide.html
 [the PyTorch website for installation instructions]: https://pytorch.org/get-started/locally/
 [PyTorch]: https://pytorch.org/get-started/locally/
 [MONAI's optional dependencies]: https://docs.monai.io/en/stable/installation.html#installing-the-recommended-dependencies
diff --git a/docs/_config.yml b/docs/_config.yml
index 0cdba9ab..6687602c 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -21,7 +21,7 @@ bibtex_bibfiles:
 
 # Information about where the book exists on the web
 repository:
-  url: https://github.com/AdaptiveMotorControlLab/CellSeg3d  # Online location of your book
+  url: https://github.com/AdaptiveMotorControlLab/CellSeg3D  # Online location of your book
   path_to_book: docs  # Optional path to your book, relative to the repository root
   branch: main  # Which branch of the repository should be used when creating links (optional)
 
diff --git a/docs/source/guides/custom_model_template.rst b/docs/source/guides/custom_model_template.rst
index 7d7f2264..db8000ec 100644
--- a/docs/source/guides/custom_model_template.rst
+++ b/docs/source/guides/custom_model_template.rst
@@ -31,4 +31,4 @@ To add a custom model, you will need a **.py** file with the following structure
 .. note::
     **WIP** : Currently you must modify :doc:`model_framework.py <../code/_autosummary/napari_cellseg3d.code_models.model_framework>` as well : import your model class and add it to the ``model_dict`` attribute
 
-.. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
+.. _file an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
diff --git a/docs/source/guides/detailed_walkthrough.rst b/docs/source/guides/detailed_walkthrough.rst
index b458ab28..4df7bd45 100644
--- a/docs/source/guides/detailed_walkthrough.rst
+++ b/docs/source/guides/detailed_walkthrough.rst
@@ -207,7 +207,7 @@ To visualise some of the results when running on a folder, you can leave the **V
 .. note::
     Too plot your results, check out the `provided notebooks`_
 
-.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks
+.. _provided notebooks: https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks
 
 
 You can then launch inference and the results will be saved in your specified folder.
@@ -271,6 +271,6 @@ for the plots to work.
    Example of the plot present in the notebooks.
    Coordinates are based on centroids, the size represents the volume, the color, and the sphericity.
 
-.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/tree/main/notebooks
+.. _notebooks folder of the repository: https://github.com/AdaptiveMotorControlLab/CellSeg3D/tree/main/notebooks
 
 With this complete, you can repeat the workflow as needed.
diff --git a/docs/source/guides/installation_guide.rst b/docs/source/guides/installation_guide.rst
index ad2fffe6..bbd97b19 100644
--- a/docs/source/guides/installation_guide.rst
+++ b/docs/source/guides/installation_guide.rst
@@ -8,7 +8,7 @@ Please refer to the :ref:`section below <source/guides/installation_guide:ARM64
 .. warning::
     If you encounter any issues during installation, feel free to open an issue on our `GitHub repository`_.
 
-.. _GitHub repository: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
+.. _GitHub repository: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
 
 
 Installing pre-requisites
@@ -82,11 +82,11 @@ ______________________________
 
 .. _miniconda3: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html
 
-1. **Clone the repository** (`link <https://github.com/AdaptiveMotorControlLab/CellSeg3d>`_):
+1. **Clone the repository** (`link <https://github.com/AdaptiveMotorControlLab/CellSeg3D>`_):
 
 .. code-block::
 
-    git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git
+    git clone https://github.com/AdaptiveMotorControlLab/CellSeg3D.git
 
 2. **Create the Conda Environment** :
 In the terminal, navigate to the CellSeg3D folder:
diff --git a/docs/source/guides/training_wnet.rst b/docs/source/guides/training_wnet.rst
index c1d9fd68..21fff524 100644
--- a/docs/source/guides/training_wnet.rst
+++ b/docs/source/guides/training_wnet.rst
@@ -96,4 +96,4 @@ Common issues troubleshooting
 
 
 .. _WNet, A Deep Model for Fully Unsupervised Image Segmentation: https://arxiv.org/abs/1711.08506
-.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues
+.. _open an issue: https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues
diff --git a/examples/README.md b/examples/README.md
index d71d6c3d..000f56c7 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -4,4 +4,5 @@ Here is a very small volume (from [IDR project 853](https://idr.openmicroscopy.o
 You can install, launch `napari`, activate the CellSeg3D plugin app, and drag & drop this volume into the canvas.
 Then, for example, run `inference` with one of our models.
 
-See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3d/welcome.html) for more details.
+See [CellSeg3D documentation](https://adaptivemotorcontrollab.github.io/CellSeg3D/welcome.html) for more details.
+
diff --git a/napari_cellseg3d/_tests/test_plugin_inference.py b/napari_cellseg3d/_tests/test_plugin_inference.py
index de518b3d..03565045 100644
--- a/napari_cellseg3d/_tests/test_plugin_inference.py
+++ b/napari_cellseg3d/_tests/test_plugin_inference.py
@@ -31,7 +31,7 @@ def test_inference(make_napari_viewer_proxy, qtbot):
 
     assert widget.check_ready()
 
-    widget.model_choice.setCurrentText("WNet")
+    widget.model_choice.setCurrentText("WNet3D")
     widget._restrict_window_size_for_model()
     assert widget.use_window_choice.isChecked()
     assert widget.window_size_choice.currentText() == "64"
diff --git a/napari_cellseg3d/_tests/test_plugin_training.py b/napari_cellseg3d/_tests/test_plugin_training.py
index 41102bbd..7ea61200 100644
--- a/napari_cellseg3d/_tests/test_plugin_training.py
+++ b/napari_cellseg3d/_tests/test_plugin_training.py
@@ -33,7 +33,7 @@ def test_worker_configs(make_napari_viewer_proxy):
                 worker.config, attr
             )
     # test unsupervised config and worker
-    widget.model_choice.setCurrentText("WNet")
+    widget.model_choice.setCurrentText("WNet3D")
     widget._toggle_unsupervised_mode(enabled=True)
     default_config = config.WNetTrainingWorkerConfig()
     worker = widget._create_worker(additional_results_description="TEST_1")
diff --git a/napari_cellseg3d/_tests/test_plugin_utils.py b/napari_cellseg3d/_tests/test_plugin_utils.py
index 9e668fc5..3f3da7b3 100644
--- a/napari_cellseg3d/_tests/test_plugin_utils.py
+++ b/napari_cellseg3d/_tests/test_plugin_utils.py
@@ -38,7 +38,7 @@ def test_crop_widget(make_napari_viewer_proxy):
     view = make_napari_viewer_proxy()
     widget = Cropping(view)
 
-    image = rand_gen.random((10, 10, 10)).astype(np.uint8)
+    image = rand_gen.random((10, 10, 10)).astype(np.int8)
     image_layer_1 = view.add_image(image, name="image")
     image_layer_2 = view.add_labels(image, name="image2")
 
diff --git a/napari_cellseg3d/_tests/test_training.py b/napari_cellseg3d/_tests/test_training.py
index c095b4e9..15ec119e 100644
--- a/napari_cellseg3d/_tests/test_training.py
+++ b/napari_cellseg3d/_tests/test_training.py
@@ -86,8 +86,8 @@ def test_unsupervised_training(make_napari_viewer_proxy):
     widget = Trainer(viewer)
     widget.log = LogFixture()
     widget.worker = None
+    widget.model_choice.setCurrentText("WNet3D")
     widget._toggle_unsupervised_mode(enabled=True)
-    widget.model_choice.setCurrentText("WNet")
 
     widget.patch_choice.setChecked(True)
     [w.setValue(4) for w in widget.patch_size_widgets]
diff --git a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json
index d9e1e4b0..8bf72b27 100644
--- a/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json
+++ b/napari_cellseg3d/code_models/models/pretrained/pretrained_model_urls.json
@@ -4,6 +4,7 @@
   "VNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/VNet_latest.tar.gz",
   "SwinUNetR": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/SwinUNetR_latest.tar.gz",
   "WNet": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_latest.tar.gz",
+  "WNet3D": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_latest.tar.gz",
   "WNet_ONNX": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/wnet_onnx.tar.gz",
   "test": "https://huggingface.co/C-Achard/cellseg3d/resolve/main/test.tar.gz"
 }
diff --git a/napari_cellseg3d/code_models/models/wnet/model.py b/napari_cellseg3d/code_models/models/wnet/model.py
index 4817e307..e0e5b764 100644
--- a/napari_cellseg3d/code_models/models/wnet/model.py
+++ b/napari_cellseg3d/code_models/models/wnet/model.py
@@ -17,7 +17,7 @@
 
 
 class WNet_encoder(nn.Module):
-    """WNet with encoder only."""
+    """WNet3D with encoder only."""
 
     def __init__(
         self,
diff --git a/napari_cellseg3d/code_models/worker_inference.py b/napari_cellseg3d/code_models/worker_inference.py
index c69dfe45..46ba77eb 100644
--- a/napari_cellseg3d/code_models/worker_inference.py
+++ b/napari_cellseg3d/code_models/worker_inference.py
@@ -277,7 +277,7 @@ def load_layer(self):
 
         normalization = (
             QuantileNormalization()
-            if self.config.model_info.name != "WNet"
+            if self.config.model_info.name != "WNet3D"
             else lambda x: x
         )
         volume = np.reshape(volume, newshape=(1, *volume.shape))
diff --git a/napari_cellseg3d/code_models/worker_training.py b/napari_cellseg3d/code_models/worker_training.py
index 9ad08d24..0ffec8f9 100644
--- a/napari_cellseg3d/code_models/worker_training.py
+++ b/napari_cellseg3d/code_models/worker_training.py
@@ -431,9 +431,9 @@ def train(
                 wandb.init(
                     config=config_dict,
                     project="CellSeg3D - WNet",
-                    name=f"WNet_training - {utils.get_date_time()}",
+                    name=f"WNet3D_training - {utils.get_date_time()}",
                     mode=self.wandb_config.mode,
-                    tags=["WNet", "training"],
+                    tags=["WNet3D", "training"],
                 )
 
             set_determinism(seed=self.config.deterministic_config.seed)
@@ -487,7 +487,7 @@ def train(
                     )
 
                     weights_file = WNet_.weights_file
-                    self.downloader.download_weights("WNet", weights_file)
+                    self.downloader.download_weights("WNet3D", weights_file)
                     weights = str(PRETRAINED_WEIGHTS_DIR / Path(weights_file))
                     self.config.weights_info.path = weights
 
@@ -781,7 +781,7 @@ def train(
 
             if WANDB_INSTALLED and self.wandb_config.save_model_artifact:
                 model_artifact = wandb.Artifact(
-                    "WNet",
+                    "WNet3D",
                     type="model",
                     description="CellSeg3D WNet",
                     metadata=self.config.__dict__,
@@ -1127,6 +1127,11 @@ def train(
         weights_config = self.config.weights_info
         deterministic_config = self.config.deterministic_config
 
+        if self.config.device == "mps":
+            from os import environ
+
+            environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
+
         start_time = time.time()
 
         try:
diff --git a/napari_cellseg3d/code_plugins/plugin_helper.py b/napari_cellseg3d/code_plugins/plugin_helper.py
index 710d6ded..f58b38b7 100644
--- a/napari_cellseg3d/code_plugins/plugin_helper.py
+++ b/napari_cellseg3d/code_plugins/plugin_helper.py
@@ -21,10 +21,10 @@ def __init__(self, viewer: "napari.viewer.Viewer"):
         """Creates a widget with links to documentation and about page."""
         super().__init__()
 
-        self.help_url = "https://adaptivemotorcontrollab.github.io/CellSeg3d/"
+        self.help_url = "https://adaptivemotorcontrollab.github.io/CellSeg3D/"
 
         self.about_url = "https://wysscenter.ch/advances/3d-computer-vision-for-brain-analysis"
-        self.repo_url = "https://github.com/AdaptiveMotorControlLab/CellSeg3d"
+        self.repo_url = "https://github.com/AdaptiveMotorControlLab/CellSeg3D"
         self._viewer = viewer
 
         logo_path = str(
diff --git a/napari_cellseg3d/code_plugins/plugin_model_inference.py b/napari_cellseg3d/code_plugins/plugin_model_inference.py
index 27ff2d5e..fd4c37e5 100644
--- a/napari_cellseg3d/code_plugins/plugin_model_inference.py
+++ b/napari_cellseg3d/code_plugins/plugin_model_inference.py
@@ -258,7 +258,7 @@ def __init__(self, viewer: "napari.viewer.Viewer", parent=None):
         self._remove_unused()
 
     def _toggle_crf_choice(self):
-        if self.model_choice.currentText() == "WNet":
+        if self.model_choice.currentText() == "WNet3D":
             self.use_crf.setVisible(True)
         else:
             self.use_crf.setVisible(False)
@@ -335,7 +335,7 @@ def check_ready(self):
     def _restrict_window_size_for_model(self):
         """Sets the window size to a value that is compatible with the chosen model."""
         self.wnet_enabled = False
-        if self.model_choice.currentText() == "WNet":
+        if self.model_choice.currentText() == "WNet3D":
             self.wnet_enabled = True
             self.window_size_choice.setCurrentIndex(self._default_window_size)
             self.use_window_choice.setChecked(self.wnet_enabled)
diff --git a/napari_cellseg3d/code_plugins/plugin_model_training.py b/napari_cellseg3d/code_plugins/plugin_model_training.py
index a668e155..67af40e8 100644
--- a/napari_cellseg3d/code_plugins/plugin_model_training.py
+++ b/napari_cellseg3d/code_plugins/plugin_model_training.py
@@ -451,7 +451,7 @@ def check_ready(self):
 
     def _toggle_unsupervised_mode(self, enabled=False):
         """Change all the UI elements needed for unsupervised learning mode."""
-        if self.model_choice.currentText() == "WNet" or enabled:
+        if self.model_choice.currentText() == "WNet3D" or enabled:
             unsupervised = True
             self.start_btn = self.start_button_unsupervised
             if self.image_filewidget.text_field.text() == "Images directory":
@@ -799,7 +799,7 @@ def _build(self):
         ui.add_blank(advanced_tab, advanced_tab.layout)
         ##################
         model_params_group_w, model_params_group_l = ui.make_group(
-            "WNet parameters", r=20, b=5, t=11
+            "WNet3D parameters", r=20, b=5, t=11
         )
         ui.add_widgets(
             model_params_group_l,
diff --git a/napari_cellseg3d/config.py b/napari_cellseg3d/config.py
index 002c6024..f319802d 100644
--- a/napari_cellseg3d/config.py
+++ b/napari_cellseg3d/config.py
@@ -26,7 +26,7 @@
     "VNet": VNet_,
     "TRAILMAP_MS": TRAILMAP_MS_,
     "SwinUNetR": SwinUNETR_,
-    "WNet": WNet_,
+    "WNet3D": WNet_,
     # "TRAILMAP": TRAILMAP,
     # "test" : DO NOT USE, reserved for testing
 }
diff --git a/napari_cellseg3d/dev_scripts/classifier_test.ipynb b/napari_cellseg3d/dev_scripts/classifier_test.ipynb
index 42afe988..75f3ba41 100644
--- a/napari_cellseg3d/dev_scripts/classifier_test.ipynb
+++ b/napari_cellseg3d/dev_scripts/classifier_test.ipynb
@@ -483,7 +483,7 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\Cyril\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\classifier_test.ipynb Cell 12\u001b[0m line \u001b[0;36m<cell line: 34>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3d/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m                 preds[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3d/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m                 rejected[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m crop\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3d/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m view \u001b[39m=\u001b[39m napari\u001b[39m.\u001b[39;49mview_image(preds, colormap\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mturbo\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3d/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m view\u001b[39m.\u001b[39madd_image(test_image, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3d/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m view\u001b[39m.\u001b[39madd_image(rejected, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n",
+      "\u001b[1;32mc:\\Users\\Cyril\\Desktop\\Code\\CellSeg3D\\napari_cellseg3d\\dev_scripts\\classifier_test.ipynb Cell 12\u001b[0m line \u001b[0;36m<cell line: 34>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3D/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=30'>31</a>\u001b[0m                 preds[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3D/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=31'>32</a>\u001b[0m                 rejected[crop_location_i:crop_location_i\u001b[39m+\u001b[39mcube_size, crop_location_j:crop_location_j\u001b[39m+\u001b[39mcube_size, crop_location_k:crop_location_k\u001b[39m+\u001b[39mcube_size] \u001b[39m=\u001b[39m crop\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3D/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m view \u001b[39m=\u001b[39m napari\u001b[39m.\u001b[39;49mview_image(preds, colormap\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mturbo\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3D/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m view\u001b[39m.\u001b[39madd_image(test_image, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Cyril/Desktop/Code/CellSeg3D/napari_cellseg3d/dev_scripts/classifier_test.ipynb#X40sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m view\u001b[39m.\u001b[39madd_image(rejected, colormap\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mturbo\u001b[39m\u001b[39m\"\u001b[39m, blending\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39madditive\u001b[39m\u001b[39m\"\u001b[39m)\n",
       "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\view_layers.py:178\u001b[0m, in \u001b[0;36mview_image\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    176\u001b[0m \u001b[39m@_merge_layer_viewer_sigs_docs\u001b[39m\n\u001b[0;32m    177\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mview_image\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m--> 178\u001b[0m     \u001b[39mreturn\u001b[39;00m _make_viewer_then(\u001b[39m'\u001b[39;49m\u001b[39madd_image\u001b[39;49m\u001b[39m'\u001b[39;49m, args, kwargs)[\u001b[39m0\u001b[39m]\n",
       "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\view_layers.py:156\u001b[0m, in \u001b[0;36m_make_viewer_then\u001b[1;34m(add_method, args, kwargs)\u001b[0m\n\u001b[0;32m    154\u001b[0m viewer \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mviewer\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m    155\u001b[0m \u001b[39mif\u001b[39;00m viewer \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 156\u001b[0m     viewer \u001b[39m=\u001b[39m Viewer(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvkwargs)\n\u001b[0;32m    157\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mkwargs\u001b[39m\u001b[39m\"\u001b[39m, {}))\n\u001b[0;32m    158\u001b[0m method \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(viewer, add_method)\n",
       "File \u001b[1;32mc:\\Users\\Cyril\\anaconda3\\envs\\cellseg3d\\lib\\site-packages\\napari\\viewer.py:67\u001b[0m, in \u001b[0;36mViewer.__init__\u001b[1;34m(self, title, ndisplay, order, axis_labels, show)\u001b[0m\n\u001b[0;32m     63\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnapari\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwindow\u001b[39;00m \u001b[39mimport\u001b[39;00m Window\n\u001b[0;32m     65\u001b[0m _initialize_plugins()\n\u001b[1;32m---> 67\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_window \u001b[39m=\u001b[39m Window(\u001b[39mself\u001b[39;49m, show\u001b[39m=\u001b[39;49mshow)\n\u001b[0;32m     68\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_instances\u001b[39m.\u001b[39madd(\u001b[39mself\u001b[39m)\n",
diff --git a/napari_cellseg3d/dev_scripts/colab_training.py b/napari_cellseg3d/dev_scripts/colab_training.py
index ef5245e8..e8ca1f91 100644
--- a/napari_cellseg3d/dev_scripts/colab_training.py
+++ b/napari_cellseg3d/dev_scripts/colab_training.py
@@ -332,7 +332,7 @@ def train(
                     project="CellSeg3D (Colab)",
                     name=f"{self.config.model_info.name} training - {utils.get_date_time()}",
                     mode=self.wandb_config.mode,
-                    tags=["WNet", "Colab"],
+                    tags=["WNet3D", "Colab"],
                 )
 
             set_determinism(seed=self.config.deterministic_config.seed)
@@ -379,7 +379,7 @@ def train(
             if self.config.weights_info.use_custom:
                 if self.config.weights_info.use_pretrained:
                     weights_file = "wnet.pth"
-                    self.downloader.download_weights("WNet", weights_file)
+                    self.downloader.download_weights("WNet3D", weights_file)
                     weights = PRETRAINED_WEIGHTS_DIR / Path(weights_file)
                     self.config.weights_info.path = weights
                 else:
@@ -596,7 +596,7 @@ def train(
 
             if WANDB_INSTALLED and self.wandb_config.save_model_artifact:
                 model_artifact = wandb.Artifact(
-                    "WNet",
+                    "WNet3D",
                     type="model",
                     description="CellSeg3D WNet",
                     metadata=self.config.__dict__,
diff --git a/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb b/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb
index 12707e9b..dcb7ace9 100644
--- a/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb
+++ b/napari_cellseg3d/dev_scripts/test_new_evaluation.ipynb
@@ -24,7 +24,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'evaluate_labels' from 'C:\\\\Users\\\\Cyril\\\\Desktop\\\\Code\\\\CellSeg3d\\\\napari_cellseg3d\\\\dev_scripts\\\\evaluate_labels.py'>"
+       "<module 'evaluate_labels' from 'C:\\\\Users\\\\Cyril\\\\Desktop\\\\Code\\\\CellSeg3D\\\\napari_cellseg3d\\\\dev_scripts\\\\evaluate_labels.py'>"
       ]
      },
      "execution_count": 2,
@@ -62,8 +62,8 @@
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
       "Cell \u001b[1;32mIn[16], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m labels \u001b[38;5;241m=\u001b[39m imread(path_model_label)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;66;03m# labels.shape\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mevl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_model_performance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_true_labels\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43mvisualize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43mplot_according_to_gt_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:58\u001b[0m, in \u001b[0;36mevaluate_model_performance\u001b[1;34m(labels, model_labels, threshold_correct, print_details, visualize, return_graphical_summary, plot_according_to_gt_label)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Evaluate the model performance.\u001b[39;00m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;124;03m----------\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;124;03mgraph_true_positive_ratio_model: ndarray\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     57\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMapping labels...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 58\u001b[0m tmp \u001b[38;5;241m=\u001b[39m \u001b[43mmap_labels\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     59\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mthreshold_correct\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_total_number_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     64\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     65\u001b[0m \u001b[43m    \u001b[49m\u001b[43mplot_according_to_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplot_according_to_gt_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_graphical_summary:\n\u001b[0;32m     68\u001b[0m     (\n\u001b[0;32m     69\u001b[0m         map_labels_existing,\n\u001b[0;32m     70\u001b[0m         map_fused_neurons,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     75\u001b[0m         graph_true_positive_ratio_model,\n\u001b[0;32m     76\u001b[0m     ) \u001b[38;5;241m=\u001b[39m tmp\n",
-      "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3d\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:422\u001b[0m, in \u001b[0;36mmap_labels\u001b[1;34m(gt_labels, model_labels, threshold_correct, return_total_number_gt_labels, return_dict_map, accuracy_function, return_graphical_summary, plot_according_to_gt_labels)\u001b[0m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;66;03m# remove from new_labels the labels that are in map_labels_existing\u001b[39;00m\n\u001b[0;32m    420\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(new_labels)\n\u001b[0;32m    421\u001b[0m i_new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misin(\n\u001b[1;32m--> 422\u001b[0m     \u001b[43mnew_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_map\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_label\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m,\n\u001b[0;32m    423\u001b[0m     map_labels_existing[:, dict_map[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_label\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[0;32m    424\u001b[0m     invert\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    425\u001b[0m )\n\u001b[0;32m    426\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m new_labels[i_new_labels, :]\n\u001b[0;32m    427\u001b[0m \u001b[38;5;66;03m# find the fused neurons: multiple gt labels are mapped to the same model label\u001b[39;00m\n",
+      "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3D\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:58\u001b[0m, in \u001b[0;36mevaluate_model_performance\u001b[1;34m(labels, model_labels, threshold_correct, print_details, visualize, return_graphical_summary, plot_according_to_gt_label)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Evaluate the model performance.\u001b[39;00m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;124;03m----------\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;124;03mgraph_true_positive_ratio_model: ndarray\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     57\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMapping labels...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 58\u001b[0m tmp \u001b[38;5;241m=\u001b[39m \u001b[43mmap_labels\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     59\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mthreshold_correct\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_total_number_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     64\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_graphical_summary\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     65\u001b[0m \u001b[43m    \u001b[49m\u001b[43mplot_according_to_gt_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplot_according_to_gt_label\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_graphical_summary:\n\u001b[0;32m     68\u001b[0m     (\n\u001b[0;32m     69\u001b[0m         map_labels_existing,\n\u001b[0;32m     70\u001b[0m         map_fused_neurons,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     75\u001b[0m         graph_true_positive_ratio_model,\n\u001b[0;32m     76\u001b[0m     ) \u001b[38;5;241m=\u001b[39m tmp\n",
+      "File \u001b[1;32m~\\Desktop\\Code\\CellSeg3D\\napari_cellseg3d\\dev_scripts\\evaluate_labels.py:422\u001b[0m, in \u001b[0;36mmap_labels\u001b[1;34m(gt_labels, model_labels, threshold_correct, return_total_number_gt_labels, return_dict_map, accuracy_function, return_graphical_summary, plot_according_to_gt_labels)\u001b[0m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;66;03m# remove from new_labels the labels that are in map_labels_existing\u001b[39;00m\n\u001b[0;32m    420\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(new_labels)\n\u001b[0;32m    421\u001b[0m i_new_labels \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39misin(\n\u001b[1;32m--> 422\u001b[0m     \u001b[43mnew_labels\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_map\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_label\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m,\n\u001b[0;32m    423\u001b[0m     map_labels_existing[:, dict_map[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_label\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[0;32m    424\u001b[0m     invert\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    425\u001b[0m )\n\u001b[0;32m    426\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m new_labels[i_new_labels, :]\n\u001b[0;32m    427\u001b[0m \u001b[38;5;66;03m# find the fused neurons: multiple gt labels are mapped to the same model label\u001b[39;00m\n",
       "\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
      ]
     }
diff --git a/notebooks/colab_wnet_training.ipynb b/notebooks/colab_wnet_training.ipynb
index 5b984673..b5b4c98a 100644
--- a/notebooks/colab_wnet_training.ipynb
+++ b/notebooks/colab_wnet_training.ipynb
@@ -24,7 +24,7 @@
         "---\n",
         "*Disclaimer:*\n",
         "\n",
-        "This notebook, part of the [CellSeg3D project](https://github.com/AdaptiveMotorControlLab/CellSeg3d) under the [Mathis Lab of Adaptive Motor Control](https://www.mackenziemathislab.org/), is a work-in-progress resource for training the WNet model for unsupervised cell segmentation.\n",
+        "This notebook, part of the [CellSeg3D project](https://github.com/AdaptiveMotorControlLab/CellSeg3D) under the [Mathis Lab of Adaptive Motor Control](https://www.mackenziemathislab.org/), is a work-in-progress resource for training the WNet model for unsupervised cell segmentation.\n",
         "\n",
         "The foundation of this notebook owes much to the **[ZeroCostDL4Mic](https://github.com/HenriquesLab/ZeroCostDL4Mic)** project —a collaborative effort between the Jacquemet and Henriques laboratories, and created by Daniel Krentzel. Except for the model provided herein, all credits are duly given to their team."
       ],
@@ -226,7 +226,7 @@
       ],
       "source": [
         "#@markdown ##Play to install WNet dependencies\n",
-        "!git clone https://github.com/AdaptiveMotorControlLab/CellSeg3d.git --branch cy/wnet-extras --single-branch ./CellSeg3D\n",
+        "!git clone https://github.com/AdaptiveMotorControlLab/CellSeg3D.git --branch cy/wnet-extras --single-branch ./CellSeg3D\n",
         "!pip install -e CellSeg3D"
       ]
     },
diff --git a/pyproject.toml b/pyproject.toml
index e76cb4b7..b0a7b711 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -49,9 +49,9 @@ dependencies = [
 dynamic = ["version", "entry-points"]
 
 [project.urls]
-Homepage  = "https://github.com/AdaptiveMotorControlLab/CellSeg3d"
+Homepage  = "https://github.com/AdaptiveMotorControlLab/CellSeg3D"
 Documentation = "https://adaptivemotorcontrollab.github.io/cellseg3d-docs/res/welcome.html"
-Issues = "https://github.com/AdaptiveMotorControlLab/CellSeg3d/issues"
+Issues = "https://github.com/AdaptiveMotorControlLab/CellSeg3D/issues"
 
 [build-system]
 requires = ["setuptools", "wheel"]