| 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 | |
| ``` |