diff --git a/README.md b/README.md
index 09252dab..7021fe63 100644
--- a/README.md
+++ b/README.md
@@ -52,13 +52,13 @@ The strength of our approach is we can match supervised model performance with p
 
 ![FIG1 (1)](https://github.com/user-attachments/assets/0d970b45-79ff-4c58-861f-e1e7dc9abc65)
 
-**Figure 1. Performance of 3D Semantic and Instance Segmentation Models.** 
-**a:** Raw mesoSPIM whole-brain sample, volumes and corresponding ground truth labels from somatosensory (S1) and visual (V1) cortical regions. 
+**Figure 1. Performance of 3D Semantic and Instance Segmentation Models.**
+**a:** Raw mesoSPIM whole-brain sample, volumes and corresponding ground truth labels from somatosensory (S1) and visual (V1) cortical regions.
 **b:** Evaluation of instance segmentation performance for baseline
 thresholding-only, supervised models: Cellpose, StartDist, SwinUNetR, SegResNet, and our self-supervised model WNet3D over three data subsets.
 F1-score is computed from the Intersection over Union (IoU) with ground truth labels, then averaged. Error bars represent 50% Confidence Intervals
-(CIs). 
-**c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation. 
+(CIs).
+**c:** View of 3D instance labels from supervised models, as noted, for visual cortex volume in b evaluation.
 **d:** Illustration of our WNet3D architecture showcasing the dual 3D U-Net structure with our modifications.
 
 
@@ -141,7 +141,7 @@ Before testing, install all requirements using ``pip install napari-cellseg3d[te
 
 To run tests locally:
 
-- Locally : run ``pytest`` in the plugin folder
+- Locally : run ``pytest napari_cellseg3d\_tests`` in the plugin folder.
 - Locally with coverage : In the plugin folder, run ``coverage run --source=napari_cellseg3d -m pytest`` then ``coverage xml`` to generate a .xml coverage file.
 - With tox : run ``tox`` in the plugin folder (will simulate tests with several python and OS configs, requires substantial storage space)
 
diff --git a/napari_cellseg3d/code_models/crf.py b/napari_cellseg3d/code_models/crf.py
index 4fa2264c..b70c92e4 100644
--- a/napari_cellseg3d/code_models/crf.py
+++ b/napari_cellseg3d/code_models/crf.py
@@ -18,6 +18,7 @@
 
 Implemented using the pydense library available at https://github.com/lucasb-eyer/pydensecrf.
 """
+
 import importlib
 
 import numpy as np
@@ -28,13 +29,7 @@
 
 spec = importlib.util.find_spec("pydensecrf")
 CRF_INSTALLED = spec is not None
-if not CRF_INSTALLED:
-    logger.info(
-        "pydensecrf not installed, CRF post-processing will not be available. "
-        "Please install by running : pip install pydensecrf "
-        "This is not a hard requirement, you do not need it to install it unless you want to use the CRF post-processing step. "
-    )
-else:
+if CRF_INSTALLED:
     import pydensecrf.densecrf as dcrf
     from pydensecrf.utils import (
         create_pairwise_bilateral,
@@ -124,6 +119,11 @@ def crf(image, prob, sa, sb, sg, w1, w2, n_iter=5):
         np.ndarray: Array of shape (K, H, W, D) containing the refined class probabilities for each pixel.
     """
     if not CRF_INSTALLED:
+        logger.info(
+            "pydensecrf not installed, CRF post-processing will not be available. "
+            "Please install by running : pip install pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master"
+            "This is not a hard requirement, you do not need it to install it unless you want to use the CRF post-processing step."
+        )
         return None
 
     d = dcrf.DenseCRF(
@@ -232,7 +232,12 @@ def __init__(
     def _run_crf_job(self):
         """Runs the CRF post-processing step for the W-Net."""
         if not CRF_INSTALLED:
-            raise ImportError("pydensecrf is not installed.")
+            logger.info(
+                "pydensecrf not installed, CRF post-processing will not be available. "
+                "Please install by running : pip install pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master"
+                "This is not a hard requirement, you do not need it to install it unless you want to use the CRF post-processing step."
+            )
+        # raise ImportError("pydensecrf is not installed.")
 
         if len(self.images) != len(self.labels):
             raise ValueError("Number of images and labels must be the same.")
diff --git a/pyproject.toml b/pyproject.toml
index ed02dcc1..c5f566a3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -123,9 +123,6 @@ profile = "black"
 line_length = 79
 
 [project.optional-dependencies]
-crf = [
-   # "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
-]
 pyqt5 = [
     "pyqt5",
 ]
@@ -164,7 +161,6 @@ test = [
     "coverage",
     "tox",
     "twine",
-    # "pydensecrf@git+https://github.com/lucasb-eyer/pydensecrf.git#egg=master",
     "onnx",
     "onnxruntime",
 ]