diff --git a/zoobot/pytorch/training/finetune.py b/zoobot/pytorch/training/finetune.py
index 0a846cb9..144ce79b 100644
--- a/zoobot/pytorch/training/finetune.py
+++ b/zoobot/pytorch/training/finetune.py
@@ -421,15 +421,24 @@ class FinetuneableZoobotRegressor(FinetuneableZoobotAbstract):
 
     def __init__(
             self,
+            unit_interval=False, 
             **super_kwargs) -> None:
 
         super().__init__(**super_kwargs)
 
+        self.unit_interval = unit_interval
+        if self.unit_interval:
+            logging.info('unit_interval=True, using sigmoid activation for finetunng head')
+            head_activation = torch.nn.functional.sigmoid
+        else:
+            head_activation = None
+    
         logging.info('Using classification head and cross-entropy loss')
         self.head = LinearHead(
             input_dim=self.encoder_dim,
             output_dim=1,
-            dropout_prob=self.dropout_prob
+            dropout_prob=self.dropout_prob,
+            activation=head_activation
         )
         self.loss = mse_loss
         # rmse metrics. loss is mse already.
@@ -540,17 +549,21 @@ def upload_images_to_wandb(self, outputs, batch, batch_idx):
 
 # https://github.com/inigoval/byol/blob/1da1bba7dc5cabe2b47956f9d7c6277decd16cc7/byol_main/networks/models.py#L29
 class LinearHead(torch.nn.Module):
-    def __init__(self, input_dim, output_dim, dropout_prob=0.5):
+    def __init__(self, input_dim, output_dim, dropout_prob=0.5, activation=None):
         # input dim is representation dim, output_dim is num classes
         super(LinearHead, self).__init__()
         self.output_dim = output_dim
         self.dropout = torch.nn.Dropout(p=dropout_prob)
         self.linear = torch.nn.Linear(input_dim, output_dim)
+        if activation is not None:
+            self.activation = activation
 
     def forward(self, x):
         # returns logits, as recommended for CrossEntropy loss
         x = self.dropout(x)
         x = self.linear(x)
+        if self.activation is not None:
+            x = self.activation(x)
         if self.output_dim == 1:
             return x.squeeze()
         else: