v0

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

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)