| 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 |
| ``` |
