lat_mean=39.951853021309034
lat_std=0.0005336591548786815
lon_mean=-75.19122782343982
lon_std=0.0004335530163099028
---
license: mit
---


#ResNet GPS Prediction Model
This model predicts GPS coordinates (latitude and longitude) from input images using a custom ResNet-based architecture.

##How to Use
1. Download `resnet_gps_model.pth` and `config.json` from this repository.
2. Define the model architecture (as shown in the usage example below).
3. Load the model weights and configuration.

## Example Usage
```python
import torch
import json

# Load config
config = json.load(open("config.json", "r"))

# Define and load model
resnet = CustomResNetModel(model_name="microsoft/resnet-18", num_classes=config["num_classes"])
state_dict = torch.load("resnet_gps_model.pth")
resnet.load_state_dict(state_dict)
resnet.eval()

This is our customresnetmodel

class CustomResNetModel(nn.Module):
    def __init__(self, model_name="microsoft/resnet-18", num_classes=2):
        super(CustomResNetModel, self).__init__()
        # Load pre-trained ResNet from Hugging Face
        self.resnet = AutoModelForImageClassification.from_pretrained(model_name)

        # Adjust the classifier layer to output the desired number of classes
        in_features = self.resnet.classifier[0].in_features  # Assuming the last layer is a Linear layer
        self.resnet.classifier = nn.Sequential(
            nn.Flatten(),
            nn.Linear(in_features, num_classes)
        )

    def forward(self, x):
        return self.resnet(x)

    def save_model(self, save_path):
        """Save model locally using the Hugging Face format."""
        self.save_pretrained(save_path)

    def push_model(self, repo_name):
        """Push the model to the Hugging Face Hub."""
        self.push_to_hub(repo_name)