### Train Dataset Means and stds
lat_mean = 39.951572994535354
lat_std = 0.0006556104083785816
lon_mean = -75.19137012508818
lon_std = 0.0006895844560639971

### Custom Model Class
from transformers import ViTModel
class ViTGPSModel(nn.Module):
    def __init__(self, output_size=2):
        super().__init__()
        self.vit = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")
        self.regression_head = nn.Linear(self.vit.config.hidden_size, output_size)

    def forward(self, x):
        cls_embedding = self.vit(x).last_hidden_state[:, 0, :]
        return self.regression_head(cls_embedding)

### Running Inference

model_path = hf_hub_download(repo_id="Latitude-Attitude/vit-gps-coordinates-predictor", filename="vit-gps-coordinates-predictor.pth")
model = torch.load(model_path)
model.eval() 

with torch.no_grad():
    for images in dataloader:
        images = images.to(device)
        outputs = model(images)
        preds = outputs.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])