Delete README.md

README.md

@@ -1,136 +0,0 @@
-# LAJ CNN Image-to-GPS Model Iteration 1
-
-This project features a convolutional neural network (CNN) for predicting GPS coordinates (latitude and longitude) from image inputs. Below, you'll find details on loading the model, performing inference, and the architecture of the network.
-
----
-
-## 1. Loading the Model
-
-To load the model, look at the sampleRun_v3.ipynb and run the same commands.
-
-## 2. Running the Model
-
-To perform inference on our model, just normalize the latitudes and longitudes to our means and standard deviations below.
-Then run code similar to the code provided to test code provided below:
-
-```
-# Evaluate on Test Set
-model.eval()
-all_preds, all_actuals = [], []
-with torch.no_grad():
-    for images, gps_coords in val_loader:
-        images, gps_coords = images.to(device), gps_coords.to(device)
-        outputs = model(images)
-        all_preds.append(outputs.cpu())
-        all_actuals.append(gps_coords.cpu())
-all_preds = torch.cat(all_preds).numpy()
-all_actuals = torch.cat(all_actuals).numpy()
-
-# Denormalize Predictions
-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])
-
-# Compute Error Metrics
-mae = mean_absolute_error(all_actuals_denorm, all_preds_denorm)
-rmse = mean_squared_error(all_actuals_denorm, all_preds_denorm, squared=False)
-print(f"Test Set Mean Absolute Error: {mae:}")
-print(f"Test Set Root Mean Squared Error: {rmse:}")
-```
-
-## 3. Latitude and Longitude Means and Standard Deviations
-
-The following values represent the **means** and **standard deviations** of the latitude and longitude used in this model:
-
-- **Latitude Mean**: `39.95173729922173`
-- **Latitude Standard Deviation**: `0.0006877829213952256`
-- **Longitude Mean**: `-75.19138804851796`
-- **Longitude Standard Deviation**: `0.0006182574854250925`
-  These values are used to normalize and denormalize the latitude and longitude predictions during inference.
-
-## 4. CNN Architecture
-
-Finally here is the architecture of the CNN we used:
-
-```
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision.models import efficientnet_v2_s
-from torch.optim.lr_scheduler import CosineAnnealingLR
-from torchvision import transforms
-from torch.utils.data import DataLoader, Dataset
-from torchvision.transforms import functional as F
-from PIL import Image
-import numpy as np
-from sklearn.metrics import mean_absolute_error, mean_squared_error
-from huggingface_hub import PyTorchModelHubMixin
-import os
-
-# Model Definition
-class CustomGPSModel(nn.Module):
-    def __init__(self):
-        super(CustomGPSModel, self).__init__()
-
-        # Load EfficientNetV2-S with pretrained weights
-        self.efficientnet = efficientnet_v2_s(pretrained=True)
-
-        # Modify the final layer for regression (predicting latitude and longitude)
-        num_features = self.efficientnet.classifier[1].in_features
-        self.efficientnet.classifier[1] = nn.Linear(num_features, 2)  # Output layer has 2 outputs for latitude & longitude
-
-        # Don't freeze earlier layers
-        for param in self.efficientnet.features.parameters():
-            param.requires_grad = True
-
-    def forward(self, x):
-        return self.efficientnet(x)  # Forward pass through EfficientNet
-
-```
-
-## 5. Sample Run Code (how to install and run everything)
-
-```
-!pip install datasets
-!pip install huggingface_hub
-!pip install requests
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision.models import efficientnet_b0
-from torch.optim.lr_scheduler import CosineAnnealingLR
-from torchvision import transforms
-from torch.utils.data import DataLoader, Dataset
-from torchvision.transforms import functional as F
-from PIL import Image
-import numpy as np
-from sklearn.metrics import mean_absolute_error, mean_squared_error
-from huggingface_hub import PyTorchModelHubMixin
-import os
-# Model Definition
-class CustomGPSModel(nn.Module):
-    def __init__(self):
-        super(CustomGPSModel, self).__init__()
-        # Load EfficientNet-B0 with pretrained weights
-        self.efficientnet = efficientnet_b0(pretrained=True)
-        # Modify the final layer for regression (predicting latitude and longitude)
-        num_features = self.efficientnet.classifier[1].in_features
-        self.efficientnet.classifier[1] = nn.Linear(num_features, 2)  # Output layer has 2 outputs for latitude & longitude
-        # Freeze earlier layers except the last few
-        for param in self.efficientnet.features.parameters():
-            param.requires_grad = True
-    def forward(self, x):
-        return self.efficientnet(x)  # Forward pass through EfficientNet
-
-from huggingface_hub import hf_hub_download
-import torch
-path_name = "efficientnet_gps_regressor_complete.pth"
-repo_name = "CustomGPSModel_EfficientNetB0_Run2"
-organization_name = "LAJ-519-Image-Project"
-# Specify the repository and the filename of the model you want to load
-repo_id = f"{organization_name}/{repo_name}"
-filename = f"{path_name}"
-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()
-```