Update README.md

README.md

@@ -74,10 +74,12 @@ def IQA_preprocess():
     ])
     return transform
 
-image = IQA_preprocess()(image).unsqueeze(0).to(device)
+batch = torch.stack([IQA_preprocess()(image) for _ in range(15)]).to(device)  # Shape: (15, 3, 224, 224)
 
 with torch.no_grad():
-    iqa_score = model(image).item()
+    iqa_score = 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.
@@ -88,4 +90,4 @@ max_score =
 min_score =
 
 normalized_score = ((iqa_score - min_pred) / (max_pred - min_pred)) * (max_score - min_score) + min_score
-print(f"Predicted quality Score: {iqa_score:.4f}")
+print(f"Predicted quality Score: {normalized_score:.4f}")