diff --git a/mmp/a1/main.py b/mmp/a1/main.py
index fd807bf..e134645 100644
--- a/mmp/a1/main.py
+++ b/mmp/a1/main.py
@@ -1,5 +1,6 @@
 from typing import Sequence
 import torch
+import torchvision
 from torchvision.transforms import functional as F
 from torchvision import transforms, models
 from PIL import Image
@@ -51,7 +52,31 @@ def main():
 
     Put all your code for exercise 1.3 here.
     """
-    raise NotImplementedError()
+
+    paths = [
+        "./images/golden retriever.jpg",
+        "./images/koala.jpg",
+        "./images/pacifier.jpg",
+        "./images/rubber duck sculpture.jpg",
+        "./images/rubber ducks.jpg",
+        "./images/shoehorn.jpg",
+        "./images/zoo.jpg",
+    ]
+    batch = build_batch(paths)
+    model = get_model()
+
+    with torch.no_grad():
+        outputs = model(batch)
+
+    max_scores, preds = outputs.max(dim=1)
+
+    class_names = torchvision.models.ResNet18_Weights.DEFAULT.meta["categories"]
+
+    for i, (p, s) in enumerate(zip(preds, max_scores)):
+        print(f"Image: {paths[i]}")
+        print(f"  Model output score: {s.item():.4f}")
+        print(f"  Predicted class: {class_names[p.item()]}")
+        print()
 
 
 if __name__ == "__main__":