diff --git a/gcam/backends/base.py b/gcam/backends/base.py
index 5ce114d..e5b0fe3 100644
--- a/gcam/backends/base.py
+++ b/gcam/backends/base.py
@@ -78,6 +78,9 @@ def _mask_output(self, output, label):
             indices = torch.argmax(output).detach().cpu().numpy()
             mask = np.zeros(output.shape)
             np.put(mask, indices, 1)
+            # indices = torch.argmax(output).unsqueeze(0).unsqueeze(0)
+            # mask = torch.zeros_like(self.logits).to(self.device)
+            # mask.scatter_(1, indices, 1.0)
         elif isinstance(label, int):  # Only for classification
             indices = (output == label).nonzero()
             indices = [index[0] * output.shape[1] + index[1] for index in indices]
@@ -145,6 +148,6 @@ def _set_postprocessor_and_label(self, output):
                 self.postprocessor = "sigmoid"
             elif output.shape[0] == self.output_batch_size and len(output.shape) == 4 and output.shape[1] > 1:  # 3D segmentation (nnUNet)
                 self.postprocessor = torch.nn.Softmax(dim=2)
-        if self.model.gcam_dict['label'] is None:
-            if output.shape[0] == self.output_batch_size and len(output.shape) == 2:  # classification
-                self.model.gcam_dict['label'] = "best"
+        # if self.model.gcam_dict['label'] is None:  # TODO: Best for classification can lead to empty attention maps in some cases, reason is that computed weights are negative and relu filters them out. No idea if it should be like that or if its a bug
+        #     if output.shape[0] == self.output_batch_size and len(output.shape) == 2:  # classification
+        #         self.model.gcam_dict['label'] = "best"