Update README.md

README.md

@@ -3,53 +3,4 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID (Use this model for Leaderboard)
-
-This is a fine-tuned Vision Transformer (ViT) model from Google. The model was loaded and fine-tuned on the training data collected. 
-Compared to Attempt 1, we are using the expanded dataset, trained for 7 epochs instead of 5, and only updated the classifier parameters at training time.
-
-Link: https://huggingface.co/google/vit-base-patch16-224-in21k
-
-lat_mean = 39.951640614844095
-
-lat_std = 0.0007502796001097172
-
-lon_mean = -75.19143196896502
-
-lon_std = 0.0007452186171662059
-
-
-```python
-model_name = "AppliedMLReedShreya/ViT_Attempt_3"
-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()
-```
+# DO NOT USE THIS MODEL