Hoping these are the last fixes I am ever going to make

train_vit_triplet.py

@@ -152,15 +152,9 @@ def main() -> None:
                 loss.backward()
                 optimizer.step()
                 
-                # Collect metrics
-                compatibility_metrics = calculate_outfit_compatibility_metrics(
-                    torch.cat([ea, ep, en], dim=0).detach(),
-                    torch.cat([torch.ones(ea.size(0)), torch.ones(ep.size(0)), torch.zeros(en.size(0))], dim=0)
-                )
-                metrics_collector.add_batch(
-                    predictions=torch.cat([ea, ep, en], dim=0).detach(),
-                    targets=torch.cat([torch.ones(ea.size(0)), torch.ones(ep.size(0)), torch.zeros(en.size(0))], dim=0)
-                )
+                # Collect metrics (simplified for ViT training)
+                # Note: ViT training uses outfit-level embeddings, not classification predictions
+                # So we skip the problematic metrics collection that expects binary targets
                 
                 running_loss += loss.item()
                 steps += 1
@@ -251,8 +245,15 @@ def main() -> None:
     # Write comprehensive metrics
     metrics_path = os.path.join(export_dir, "vit_metrics.json")
     
-    # Get advanced metrics
-    advanced_metrics = metrics_collector.calculate_all_metrics()
+    # Get advanced metrics (simplified for ViT training)
+    # Note: ViT training doesn't collect classification metrics, so we create empty metrics
+    advanced_metrics = {
+        "total_predictions": 0,
+        "total_targets": 0, 
+        "total_scores": 0,
+        "total_embeddings": 0,
+        "total_outfit_scores": 0
+    }
     
     final_metrics = {
         "best_val_triplet_loss": best_loss if best_loss != float("inf") else None,