CNN training on Different Datasets I currently want to train a CNN but I have two small datasets that are slightly different because of the camera setup that captured the images. I'm interested in ultimately tuning the neural network to only one of the camera setups and using the network for that one setup only from here on out.

Since I'm not going to be able to expand my dataset during this time, I realize I'll need to use both training datasets to get enough of variability. So I'm wondering what approach to this might be better -- should I combine the two datasets and train the CNN, or should I train on one dataset and finetune it to the second dataset? Or is there another method I should be using?
I suggest you to use as much data as possible. If the images are coming from two different cameras, it could be an original way to fight overfitting. I would use both datasets, and operate a train-validation-test split that is transversal to both.

Additionally, since the dataset is small, I strongly enourage you to use lots and lots of data augmentation, which is a very powerful technique to artificially increase the size of your dataset and fight overfitting.