version 1.1

app.py

@@ -9,7 +9,7 @@ demo = gr.Interface(
         gr.Image(label="Top-1 Class Example"),
         gr.Label(label="Top-5 Probabilities")
     ],
-    title="Scene Classification with Reference Image Testing using Large SWIN version 1.0",
+    title="Scene Classification with Reference Image Testing using Large SWIN version 1.1.0",
     description="Upload an image to get the predicted class with a sample image and top-5 prediction chart."
 )
 

predict.py

@@ -36,7 +36,7 @@ class SwinCustom(nn.Module):
         outputs = self.model(images)
         return outputs.logits
 
-model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="swin_large_quantised.pth")
+model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="large_swin_best_model.pth")
 print("Model path:", model_path)
 model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
 state_dict = torch.load(model_path, map_location="cpu")