diff --git a/scripts/train_float32_standalone.py b/scripts/train_float32_standalone.py
index 8c3cf67..8714fe9 100755
--- a/scripts/train_float32_standalone.py
+++ b/scripts/train_float32_standalone.py
@@ -22,6 +22,7 @@ import sys
 import time
 from pathlib import Path
 
+import cv2
 import numpy as np
 import torch
 import torch.nn as nn
@@ -88,12 +89,15 @@ class Float32YOLODataset(Dataset):
         elif img.ndim == 3 and img.shape[2] == 1:
             img = np.repeat(img, 3, axis=2)
 
-        return img  # float32 (H,W,3) [0,1]
+        # Resize to model input size
+        img = cv2.resize(img, (self.img_size, self.img_size))
 
-    def _parse_label(self, path: Path) -> np.ndarray:
+        return img  # float32 (img_size, img_size, 3) [0,1] BGR
+
+    def _parse_label(self, path: Path) -> list:
         """Parse YOLO label with variable-length  rows."""
         if not path.exists():
-            return np.zeros((0, 5), dtype=np.float32)
+            return []
 
         labels = []
         with open(path, "r") as f:
@@ -102,11 +106,7 @@ class Float32YOLODataset(Dataset):
                 if len(vals) >= 5:
                     labels.append([float(v) for v in vals])
 
-        return (
-            np.array(labels, dtype=np.float32)
-            if labels
-            else np.zeros((0, 5), dtype=np.float32)
-        )
+        return labels
 
     def __getitem__(self, idx):
         img_path = self.paths[idx]
@@ -144,8 +144,6 @@ def get_pytorch_model(ul_model):
     # Try common patterns
     if hasattr(ul_model, "model"):
         pt_model = ul_model.model
-    if pt_model and hasattr(pt_model, "model"):
-        pt_model = pt_model.model
 
     # Find loss
     if pt_model and hasattr(pt_model, "loss"):
@@ -194,6 +192,8 @@ def train(args):
 
     pt_model.to(device)
     pt_model.train()
+    for param in pt_model.parameters():
+        param.requires_grad = True
 
     # Create datasets
     train_ds = Float32YOLODataset(str(train_img), str(train_lbl), args.imgsz)
@@ -254,11 +254,12 @@ def train(args):
                         # This may fail - see logs
                         try:
                             loss_out = loss_fn(preds, labels_list)
-                            loss = (
-                                loss_out[0]
-                                if isinstance(loss_out, (tuple, list))
-                                else loss_out
-                            )
+                            if isinstance(loss_out, dict):
+                                loss = loss_out["loss"]
+                            elif isinstance(loss_out, (tuple, list)):
+                                loss = loss_out[0]
+                            else:
+                                loss = loss_out
                         except Exception as e:
                             logger.error(f"Loss computation failed: {e}")
                             logger.error(
@@ -273,6 +274,7 @@ def train(args):
                 raise RuntimeError(f"Unexpected preds format: {type(preds)}")
 
             # Backward
+            loss = loss.mean()
             loss.backward()
             optimizer.step()