Push model using huggingface_hub.

README.md

@@ -6,62 +6,4 @@ tags:
 
 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]
-# Image to GPS Model: DINO-ResNet Fusion
-
-## Training Data Statistics
-The following mean and standard deviation values were used to normalize the GPS coordinates:
-
-
-
-
-- **Latitude Mean**: {39.95156391970743}
-- **Latitude Std**: {0.0007633062105681285}
-- **Longitude Mean**: {-75.19148737056214}
-- **Longitude Std**: {0.0007871346840888362}
-
-## How to use the model
-
-Please include the definition of the model first before loading the checkpoint:
-
-```python
-# Import all the dependencies
-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, AutoModel
-from huggingface_hub import PyTorchModelHubMixin
-from PIL import Image
-import os
-import numpy as np
-
-class EfficientNetGPSModel(nn.Module, PyTorchModelHubMixin):
-    def __init__(self, eff_name="efficientnet_b0", num_outputs=2):
-        super(EfficientNetGPSModel, self).__init__()
-        
-        # Load the EfficientNet backbone
-        self.efficientnet = getattr(models, eff_name)(pretrained=True)
-        
-        # Replace the classifier head while keeping the overall structure simple
-        in_features = self.efficientnet.classifier[1].in_features
-        self.efficientnet.classifier = nn.Sequential(
-            nn.Linear(in_features, num_outputs)  # Directly map to GPS coordinates
-        )
-
-    def forward(self, x):
-        return self.efficientnet(x)
-    
-    def save_model(self, save_path):
-        self.save_pretrained(save_path)
-
-    def push_model(self, repo_name):
-        self.push_to_hub(repo_name)
-```
-
-Then you can download the model from HF by running, and this will also load the checkpoint automatically:
-
-```python
-model = EfficientNetGPSModel.from_pretrained("cis519/efficient-Net")
-```
+- Docs: [More Information Needed]

model.safetensors

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