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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
``` |