Update README.md

README.md

@@ -3,27 +3,37 @@ tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
 ---
+
+# GPS Prediction Using ResNet Model
+
+This repository contains a trained model for GPS coordinate prediction using a ResNet-based architecture. The model predicts latitude and longitude values from input images and has been deployed on the Hugging Face Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin).
+
+## Inference Script
+
+Below is the Python implementation of the inference process, where we predict GPS coordinates and evaluate the model's performance using the Root Mean Squared Error (RMSE) based on geodesic distances.
+
+```python
 from geopy.distance import geodesic
 import numpy as np
 import torch
-import torch.nn as nn
-import torchvision.models as models
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader, Dataset
-from transformers import AutoImageProcessor, AutoModelForImageClassification
+from torch.utils.data import DataLoader
 from huggingface_hub import PyTorchModelHubMixin
 
+# Load the pre-trained model from the Hub
 model = CustomResNetModel.from_pretrained("5190final/model1")
 
+# Normalization constants for latitude and longitude
 lat_mean = 39.951611366653395
 lat_std = 0.0006686190927448403
 lon_mean = -75.19145880459313
 lon_std = 0.0006484111794126842
 
+# Set up device (use MPS if available, otherwise fallback to CPU)
 device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
 model.to(device)
 model.eval() 
 
+# Prepare the dataset and dataloader
 test_dataset = GPSImageDataset(
     hf_dataset=dataset_test,
     transform=inference_transform,
@@ -34,35 +44,18 @@ test_dataset = GPSImageDataset(
 )
 test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
 
+# Initialize lists to store predictions and actual values
 all_preds = []
 all_actuals = []
 
-with torch.no_grad():  # Disable gradient calculations during inference
+# Run inference
+with torch.no_grad():
     for images, gps_coords in test_dataloader:
         images = images.to(device)
         gps_coords = gps_coords.to(device)
         
         outputs = model(images)
-        logits = outputs.logits  # Extract the predictions
-
-        all_preds.extend(logits.cpu().numpy())  # Append predictions to the list
-        all_actuals.extend(gps_coords.cpu().numpy())  # Append actual values to the list
-
-all_preds = np.array(all_preds)
-all_actuals = np.array(all_actuals)
-
-all_preds_denorm = all_preds * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])
-all_actuals_denorm = all_actuals * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean]) 
-
-squared_errors = []
-for pred, actual in zip(all_preds_denorm, all_actuals_denorm):
-    # Calculate geodesic distance between predicted and actual coordinates
-    distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters  
-    squared_errors.append(distance**2)  # Square the distance for RMSE
-
-rmse = np.sqrt(np.mean(squared_errors))
-
+        logits = outputs.logits  # Extract predictions
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+        all_preds.extend(logits.cpu().numpy())  # Store predictions
+        all_actuals.extend(gps_coords.cpu