latitude_mean: 39.951631102585964\ latitude_std: 0.0006960598068888123\ longitude_mean: -75.1914340210287\ longitude_std: 0.0006455062924978866 ``` from huggingface_hub import hf_hub_download import torch import torch.nn as nn import torch.nn.functional as F from huggingface_hub import PyTorchModelHubMixin import torchvision.models as models class SimpleCNN(nn.Module, PyTorchModelHubMixin): def __init__(self): super().__init__() # Convolutional layers self.conv3to32 = nn.Conv2d(in_channels=3, out_channels=15, kernel_size=9, stride=1, padding=4) self.conv32to32kernel5 = nn.Conv2d(in_channels=15, out_channels=15, kernel_size=5, stride=1, padding=2) self.conv32to64 = nn.Conv2d(in_channels=15, out_channels=30, kernel_size=3, stride=1, padding=1) self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) self.dropout = nn.Dropout(0.5) self.linear_input_dims = 30*56*56 self.fc_1 = nn.Linear(self.linear_input_dims, 100) self.fc_2 = nn.Linear(100, 2) def forward(self, x): x = F.relu(self.conv3to32(x)) x = F.relu(self.conv32to32kernel5(x)) x = self.pool2(x) x = F.relu(self.conv32to64(x)) x = self.pool2(x) x = self.dropout(x) x = x.view(-1, self.linear_input_dims) x = F.relu(self.fc_1(x)) x = self.fc_2(x) return x def save_model(self, save_path): """Save model locally using the Hugging Face format.""" self.save_pretrained(save_path) def push_model(self, repo_name): """Push the model to the Hugging Face Hub.""" self.push_to_hub(repo_name) # Specify the repository and the filename of the model you want to load repo_id = "IanAndJohn/Model_Ian" # Replace with your repo name filename = model_save_path model_path = hf_hub_download(repo_id=repo_id, filename=filename) # Load the model using torch model = SimpleCNN() model.load_state_dict(torch.load(model_path)) model.eval() # Set the model to evaluation mode ```