Create README.md

README.md

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+lat mean = 39.951614360789364
+
+lat std = 0.0007384844437841076
+
+lon mean = -75.19140262762761
+
+lon std = 0.0007284591160342192
+
+**To load model:**
+```
+from huggingface_hub import hf_hub_download
+import torch
+
+repo_id = "thestalkers/ImageToGPSproject_base_resnet18_v2" 
+filename = "resnet_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
+```
+
+**Load a hf dataset:**
+```
+from datasets import load_dataset, Image
+
+dataset_test = load_dataset("gydou/released_img", split="train")
+
+inference_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+test_dataset = GPSImageDataset(
+    hf_dataset=dataset_test,
+    transform=inference_transform,
+    lat_mean=lat_mean,
+    lat_std=lat_std,
+    lon_mean=lon_mean,
+    lon_std=lon_std
+)
+test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
+```
+
+**Perform inference:**
+```
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+
+# Initialize lists to store predictions and actual values
+all_preds = []
+all_actuals = []
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f'Using device: {device}')
+
+with torch.no_grad():
+    for images, gps_coords in test_dataloader:
+        images, gps_coords = images.to(device), gps_coords.to(device)
+
+        outputs = model_test(images)
+
+        # Denormalize predictions and actual values
+        preds = outputs.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])
+        actuals = gps_coords.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])
+
+        all_preds.append(preds)
+        all_actuals.append(actuals)
+
+# Concatenate all batches
+all_preds = torch.cat(all_preds).numpy()
+all_actuals = torch.cat(all_actuals).numpy()
+
+# Compute error metrics
+mae = mean_absolute_error(all_actuals, all_preds)
+rmse = mean_squared_error(all_actuals, all_preds, squared=False)
+
+print(f'Mean Absolute Error: {mae}')
+print(f'Root Mean Squared Error: {rmse}')
+```