Create README.md

README.md

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+Dataset stats: \
+lat_mean = 39.951564548022596 \
+lat_std = 0.0006361722351128644 \
+lon_mean = -75.19150880602636 \
+lon_std = 0.000611411894337979 
+
+The model can be loaded using:
+```
+from huggingface_hub import hf_hub_download
+import torch
+# Specify the repository and the filename of the model you want to load
+repo_id = "FinalProj5190/ImageToGPSproject-resnet_vit-base"  # Replace with your repo name
+filename = "resnet_vit_gps_regressor_complete.pth"
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+# Load the model using torch
+model_test = torch.load(model_path)
+model_test.eval()  # Set the model to evaluation mode
+```
+
+The model implementation is here:
+```
+from transformers import ViTModel
+class HybridGPSModel(nn.Module):
+    def __init__(self, num_classes=2):
+        super(HybridGPSModel, self).__init__()
+        # Pre-trained ResNet for feature extraction
+        self.resnet = resnet18(pretrained=True)
+        self.resnet.fc = nn.Identity()
+        # Pre-trained Vision Transformer
+        self.vit = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+        # Combined regression head
+        self.regression_head = nn.Sequential(
+            nn.Linear(512 + self.vit.config.hidden_size, 128),
+            nn.ReLU(),
+            nn.Linear(128, num_classes) 
+        )
+    def forward(self, x):
+        resnet_features = self.resnet(x)
+        vit_outputs = self.vit(pixel_values=x)
+        vit_features = vit_outputs.last_hidden_state[:, 0, :]  # CLS token
+        combined_features = torch.cat((resnet_features, vit_features), dim=1)
+        # Predict GPS coordinates
+        gps_coordinates = self.regression_head(combined_features)
+        return gps_coordinates
+```