| # Information about the Dataset | |
| **Mean Latitude**: 39.95156391970743 | |
| **Latitude Std**: 0.0007633062105681285 | |
| **Mean Longitude**: -75.19148737056214 | |
| **Longitude Std**: 0.0007871346840888362 | |
| # Model definition | |
| ```python | |
| class ConvNeXtGPSPredictor(nn.Module, PyTorchModelHubMixin): | |
| def __init__(self, model_name="facebook/convnext-tiny-224", num_outputs=2): | |
| super(ConvNeXtGPSPredictor, self).__init__() | |
| # Load the ConvNeXt backbone from Hugging Face | |
| self.backbone = AutoModel.from_pretrained(model_name) | |
| # Get feature dimension from the backbone's output | |
| config = AutoConfig.from_pretrained(model_name) | |
| feature_dim = config.hidden_sizes[-1] # Corrected attribute for ConvNeXt | |
| # Define the GPS regression head | |
| self.gps_head = nn.Sequential( | |
| nn.AdaptiveAvgPool2d((1, 1)), # Pool to a single spatial dimension | |
| nn.Flatten(), # Flatten the tensor | |
| nn.LayerNorm(feature_dim), | |
| nn.Linear(feature_dim, num_outputs) # Directly map to 2 GPS coordinates | |
| ) | |
| def forward(self, x): | |
| # Extract features from the backbone | |
| features = self.backbone(x).last_hidden_state | |
| # Pass through the GPS head | |
| coords = self.gps_head(features) | |
| return coords | |
| def save_model(self, save_path): | |
| self.save_pretrained(save_path) | |
| def push_model(self, repo_name): | |
| self.push_to_hub(repo_name) | |
| ``` | |
| # How to load the model | |
| You can simply load the model by | |
| ```python | |
| model = ConvNeXtGPSPredictor.from_pretrained("cis519/convNext-GPSPredictor") | |
| ``` |