Update README.md

README.md

@@ -1,13 +1,39 @@
 <h1>Train Dataset Means and stds</h1>
-lat_mean = 39.95157130295544 
 
-lat_std = 0.0006593704228342234
+```
+lat_mean = 39.951648580775
+lat_std = 0.0006491166433423773
+lon_mean = -75.19144282374886
+lon_std = 0.0006635364490202568
+```
 
-lon_mean = -75.19136178838008
+<h1>Custom Class Model</h1>
 
-lon_std = 0.0006865423903444358
+```
+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)
+
+```
 
 <h1>Running Inference</h1>
-model_path = hf_hub_download(repo_id="Latitude-Attitude/vit-gps-coordinates-predictor-with-filter", filename="vit-gps-coordinates-predictor-with-filter-3.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])
+```
+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])
+
+```