There is not any CAP by any Framework Or technology.
This is how components will be impacted -
This will be directly impacted by the total count and size of each image
This is dependent on your layer's depth, batch size, Kernel count and size of the individual image. Please check SE/Internet on "RAM needed during CNN learning"
It means it will not change much whether image count is 1000 Or 100K if above-mentioned parameters are fixed
This will depend on your hardware and the level of parallelism but you can't do more than the batch size as backdrop need to reconcile gradients after every batch
What you must ponder
Does your images have so much of variation. Otherwise learning will be stopped after a certain image count.
In case it has got variance across all images, then learning will be very slow. What it means is that Training time is the only thing you may worry about