diff --git a/src/eynollah/training/train.py b/src/eynollah/training/train.py
index 5305ee3..233c6a4 100644
--- a/src/eynollah/training/train.py
+++ b/src/eynollah/training/train.py
@@ -14,6 +14,7 @@ from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
 from tensorflow.keras.layers import StringLookup
 from tensorflow.keras.utils import image_dataset_from_directory
 from tensorflow.keras.backend import one_hot
+from tensorflow_addons.image import connected_components
 from sacred import Experiment
 from sacred.config import create_captured_function
 
@@ -74,6 +75,42 @@ def configuration():
     except:
         print("no GPU device available", file=sys.stderr)
 
+def num_connected_components_regression(alpha: float):
+    """
+    metric/loss function capturing the separability of segmentation maps
+
+    For both sides (true and predicted, resp.), computes
+    1. the argmax() of class-wise softmax input (i.e. the segmentation map)
+    2. the connected components (i.e. the instance label map)
+    3. the max() (i.e. the highest label = nr of components)
+
+    Then calculates a regression formula between those two targets:
+    - overall mean squared (to incentivise exact fit)
+    - additive component (to incentivise more over less segments;
+      this prevents neighbours of spilling into each other;
+      oversegmentation is usually not as bad as undersegmentation)
+    """
+    def metric(y_true, y_pred):
+        # [B, H, W, C]
+        l_true = tf.math.argmax(y_true, axis=-1)
+        l_pred = tf.math.argmax(y_pred, axis=-1)
+        # [B, H, W]
+        c_true = connected_components(l_true)
+        c_pred = connected_components(l_pred)
+        # [B, H, W]
+        n_batch = tf.shape(y_true)[0]
+        c_true = tf.reshape(c_true, (n_batch, -1))
+        c_pred = tf.reshape(c_pred, (n_batch, -1))
+        # [B, H*W]
+        n_true = tf.math.reduce_max(c_true, axis=1)
+        n_pred = tf.math.reduce_max(c_pred, axis=1)
+        # [B]
+        diff = tf.cast(n_true - n_pred, tf.float32)
+        return tf.reduce_mean(tf.math.sqrt(tf.math.square(diff) + alpha * diff), axis=-1)
+
+    metric.__name__ = 'nCC'
+    return metric
+
 @tf.function
 def plot_layout_tf(in_: tf.Tensor, out:tf.Tensor) -> tf.Tensor:
     """
@@ -502,11 +539,13 @@ def run(_config,
             loss = 'mean_squared_error'
         model.compile(loss=loss,
                       optimizer=Adam(learning_rate=learning_rate),
-                      metrics=['accuracy', MeanIoU(n_classes,
-                                                   name='iou',
-                                                   ignore_class=0,
-                                                   sparse_y_true=False,
-                                                   sparse_y_pred=False)])
+                      metrics=['accuracy',
+                               num_connected_components_regression(0.1),
+                               MeanIoU(n_classes,
+                                       name='iou',
+                                       ignore_class=0,
+                                       sparse_y_true=False,
+                                       sparse_y_pred=False)])
 
         def _to_cv2float(img):
             # rgb→bgr and uint8→float, as expected by Eynollah models
diff --git a/train/requirements.txt b/train/requirements.txt
index 8ad884d..6f23d76 100644
--- a/train/requirements.txt
+++ b/train/requirements.txt
@@ -4,3 +4,4 @@ numpy
 tqdm
 imutils
 scipy
+tensorflow-addons # for connected_components