I am attempting to build a convolutional neural network from scratch for object recognition. My input image size is (3, 256, 256)
Here’s my architecture for CNN.
class NutSnackClassication(MultiLabelImageClassificationBase):
    def __init__(self):
        super().__init__()
        
        self.network = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size = 3, stride = 1, padding = 1),
            nn.ReLU(),
            nn.MaxPool2d(2,2),
            nn.Conv2d(32, 32, kernel_size = 3, stride = 1, padding = 1),
            nn.ReLU(),
            nn.MaxPool2d(2,2),
            
            nn.Conv2d(32, 64, kernel_size = 3, stride = 1, padding = 1),
            nn.ReLU(),
            nn.Conv2d(64, 128, kernel_size = 3, stride = 1, padding = 1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(128, 256, kernel_size = 3, stride = 1, padding = 1),
            nn.ReLU(),
            nn.Conv2d(256, 256, kernel_size = 3, stride = 1, padding = 1), 
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            
            nn.Flatten(),
            nn.Linear(7*7*256, 512),
            nn.ReLU(),
            nn.Linear(512, 258),
            nn.LogSoftmax(dim = 1),
        )
    
    def forward(self, xb):
        return self.network(xb)

Instead of using pretrained model I want to initialize pretrained weights from ResNet34 and then use it for predictions. A lot of examples show how to download the model and then use it which doesn’t solve my query. So how do I approach this?