diff --git a/aimodel/src/lib/ai/model_rainfallwater_mono.py b/aimodel/src/lib/ai/model_rainfallwater_mono.py
index ad2edc0..843c63e 100644
--- a/aimodel/src/lib/ai/model_rainfallwater_mono.py
+++ b/aimodel/src/lib/ai/model_rainfallwater_mono.py
@@ -69,12 +69,13 @@ def model_rainfallwater_mono(metadata, model_arch_enc="convnext_xtiny", model_ar
 	
 	# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
 	logger.warning("Warning: TODO implement attention from https://ieeexplore.ieee.org/document/9076883")
-	layer_next = tf.keras.layers.Dense(32, activation="gelu")(layer_next)
+	# Ref https://stackoverflow.com/a/54031459/1460422, no activation functions in segmentation head!
+	layer_next = tf.keras.layers.Dense(32)(layer_next)
 	# LOSS cross entropy
 	# layer_next = tf.keras.layers.Conv2D(water_bins, activation="gelu", kernel_size=1, padding="same")(layer_next)
 	# layer_next = tf.keras.layers.Softmax(axis=-1)(layer_next)
 	# LOSS dice
-	layer_next = tf.keras.layers.Conv2D(1, activation="gelu", kernel_size=1, padding="same")(layer_next)
+	layer_next = tf.keras.layers.Conv2D(1, kernel_size=1, padding="same")(layer_next)
 	layer_next = tf.keras.layers.Reshape(layer_next.shape[1:-1])(layer_next) # Strip the channels
 	
 	model = tf.keras.Model(
@@ -92,7 +93,7 @@ def model_rainfallwater_mono(metadata, model_arch_enc="convnext_xtiny", model_ar
 		# loss=LossCrossentropy(batch_size=batch_size),
 		loss=LossDice(),
 		# loss=tf.keras.losses.CategoricalCrossentropy(),
-		metrics=[tf.keras.metrics.CategoricalAccuracy()]
+		metrics=[tf.keras.metrics.BinaryAccuracy()]
 	)
 	
 	return model
diff --git a/aimodel/src/lib/ai/model_rainfallwater_segmentation.py b/aimodel/src/lib/ai/model_rainfallwater_segmentation.py
index bb53182..99ab884 100644
--- a/aimodel/src/lib/ai/model_rainfallwater_segmentation.py
+++ b/aimodel/src/lib/ai/model_rainfallwater_segmentation.py
@@ -44,8 +44,8 @@ def model_rainfallwater_segmentation(metadata, shape_water_out, model_arch="conv
 	
 	# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
 	logger.warning("Warning: TODO implement attention from https://ieeexplore.ieee.org/document/9076883")
-	layer_next = tf.keras.layers.Dense(32, activation="relu")(layer_next)
-	layer_next = tf.keras.layers.Conv2D(water_bins, activation="relu", kernel_size=1, padding="same")(layer_next)
+	layer_next = tf.keras.layers.Dense(32)(layer_next)
+	layer_next = tf.keras.layers.Conv2D(water_bins, kernel_size=1, padding="same")(layer_next)
 	layer_next = tf.keras.layers.Softmax(axis=-1)(layer_next)
 	
 	model = tf.keras.Model(