Update the Readme.md file

README.md

@@ -6,4 +6,59 @@ tags:
 
 This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
 - Library: [More Information Needed]
-- Docs: [More Information Needed]
+- Docs: [More Information Needed]
+# Image to GPS Model: DINO-ResNet Fusion
+
+## Training Data Statistics
+The following mean and standard deviation values were used to normalize the GPS coordinates:
+
+- **Latitude Mean**: {lat_mean:.6f}
+- **Latitude Std**: {lat_std:.6f}
+- **Longitude Mean**: {lon_mean:.6f}
+- **Longitude Std**: {lon_std:.6f}
+
+## How to use the model
+
+Please include the definition of the model first before loading the checkpoint:
+
+```python
+# Import all the dependencies
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification, AutoModel
+from huggingface_hub import PyTorchModelHubMixin
+from PIL import Image
+import os
+import numpy as np
+
+class EfficientNetGPSModel(nn.Module):
+    def __init__(self, eff_name="efficientnet_b0", num_outputs=2):
+        super(EfficientNetGPSModel, self).__init__()
+        
+        # Load the EfficientNet backbone
+        self.efficientnet = getattr(models, eff_name)(pretrained=True)
+        
+        # Replace the classifier head while keeping the overall structure simple
+        in_features = self.efficientnet.classifier[1].in_features
+        self.efficientnet.classifier = nn.Sequential(
+            nn.Linear(in_features, num_outputs)  # Directly map to GPS coordinates
+        )
+
+    def forward(self, x):
+        return self.efficientnet(x)
+    
+    def save_model(self, save_path):
+        self.save_pretrained(save_path)
+
+    def push_model(self, repo_name):
+        self.push_to_hub(repo_name)
+```
+
+Then you can download the model from HF by running, and this will also load the checkpoint automatically:
+
+```python
+model = EfficientNetGPSModel.from_pretrained("cis519/efficient-net-gps")
+```