Update README.md

README.md

@@ -77,17 +77,17 @@ def IQA_preprocess():
 batch = torch.stack([IQA_preprocess()(image) for _ in range(15)]).to(device)  # Shape: (15, 3, 224, 224)
 
 with torch.no_grad():
-    iqa_score = model(batch).cpu().numpy()
+    scores = model(batch).cpu().numpy()
  
 iqa_score = np.mean(scores)
 
 # maps the predicted score from the model's range [min_pred, max_pred] 
 # to the actual range [min_score, max_score] using min-max scaling.
 
-min_pred =
-max_pred =
-max_score =
-min_score =
+  min_pred = -6.52
+  max_pred = 3.11
+  max_score = 4.32
+  min_score = 1
 
 normalized_score = ((iqa_score - min_pred) / (max_pred - min_pred)) * (max_score - min_score) + min_score
 print(f"Predicted quality Score: {normalized_score:.4f}")