You could use another model architecture or modify your current model to accept later images. It’s hard to tell which approach would work better and you would have to run some experiments.
The torchvision.models.resnet... implementations accept larger input images, since an adaptive pooling layer is used before flattening the activation and feeding into the first linear layer of the classifier.


You could test both approaches for your use case. The best strategy depends on your current use case and used dataset. If your dataset is “similar” to ImageNet, freezing the layers might work fine.


I’m not completely sure what “embeddings” are in the posted model, but given your current implementation I would assume you want to reuse the resnet for both inputs and then add a custom linear layer to get the final output.


Try to replace the self.conv module with e.g. the modified resnet.