I’m not sure, if you could add this new information to a linear layer in the resnet, as the shape might not fit and you might destroy the processing of the image features by retraining this layer.
You could generally pass these additional features through a new model and concatenate the penultimate activations of this new model and the resnet before feeding them to a final classifier.
This approach was done a couple of times by users in this board. Often the model tends to focus on only one feature branch and I’m not sure, if there is a good workaround besides normalizing the activation tensors to the same value range.