Update README.md

README.md

@@ -6,11 +6,49 @@ tags: []
 # Model Card for Model ID
 
 lat_mean = 39.95164939753852
+
 lat_std = 0.0007290994359226359
+
 lon_mean = -75.191420541785
+
 lon_std = 0.000733160718757529
 
 
+```python
+model_name = "AppliedMLReedShreya/ViT_Attempt_1"
+config = AutoConfig.from_pretrained(model_name)
+config.num_labels = 2  # We need two outputs: latitude and longitude
+
+# Load the pre-trained ViT model
+vit_model = AutoModelForImageClassification.from_pretrained(model_name, config=config)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f'Using device: {device}')
+vit_model = vit_model.to(device)
+
+# Initialize lists to store predictions and actual values
+all_preds = []
+all_actuals = []
+
+vit_model.eval()
+with torch.no_grad():
+    for images, gps_coords in val_dataloader:
+        images, gps_coords = images.to(device), gps_coords.to(device)
+
+        outputs = vit_model(images).logits
+
+        # Denormalize predictions and actual values
+        preds = outputs.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])
+        actuals = gps_coords.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])
+
+        all_preds.append(preds)
+        all_actuals.append(actuals)
+
+# Concatenate all batches
+all_preds = torch.cat(all_preds).numpy()
+all_actuals = torch.cat(all_actuals).numpy()
+```
+
 
 ## Model Details