Create README.md

README.md

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+Mean and STD:
+  - lat_mean: 39.95177538047139
+  - lat_std: 0.000688423824245344
+  - lon_mean: -75.19147811784511
+  - lon_std: 0.0006632296829719546
+
+Implemented a ResNet50-based model using PyTorch: |
+  import torch
+  import torch.nn as nn
+  from torchvision.models import resnet50
+
+  class CustomResNet50(nn.Module):
+      def __init__(self, num_classes=2):
+          super().__init__()
+          self.model = resnet50(pretrained=False)
+          num_features = self.model.fc.in_features
+          self.model.fc = nn.Linear(num_features, num_classes)
+
+      def forward(self, x):
+          return self.model(x)
+
+Run the following code to access the model: |
+  from huggingface_hub import hf_hub_download
+  import torch
+  import torch.nn as nn
+  from torchvision.models import resnet50
+
+  repo_id = "ImageGPSProj/ResNet50Model"
+  filename = "custom_resnet50.pth"
+  model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+  # Re-instantiate the architecture
+  loaded_model = resnet50(pretrained=False)
+  num_features = loaded_model.fc.in_features
+  loaded_model.fc = nn.Linear(num_features, 2)
+
+  # Load the state_dict
+  state_dict = torch.load(model_path, map_location=torch.device('cpu'))
+  loaded_model.load_state_dict(state_dict)
+
+  loaded_model.eval()
+
+dataset_info:
+  features:
+    - name: image
+      dtype: image
+    - name: Latitude
+      dtype: float64
+    - name: Longitude
+      dtype: float64
+  splits:
+    - name: train
+      num_bytes: 6747451504
+      num_examples: 825
+    - name: test
+      num_bytes: 928890377
+      num_examples: 105
+    - name: val
+      num_bytes: 791887265
+      num_examples: 102
+  download_size: 7405818019
+  dataset_size: 8468229146
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train-*
+      - split: test
+        path: data/test-*
+      - split: val
+        path: data/val-*