The effect of the image type and the image conversion on deep learning CNN model
I haven't seen this discussed anywhere really, so an interesting thought.

I think the answer will be that it doesn't matter. I think you should stick to one format (not important which, but don't mix them). Once the images are read into memory for processing and training a model, they are going to just be numbers in an array, regardless of the format they were loaded from.

Perhaps the numbers will differ slightly, but we usually normalise the input to something like the range [0, 1], so that wouldn't matter.

If your file formats change the image in a way, such that some features of the image are different (like the hue), then perhaps results will differ slightly, but the contents and spatial positioning of objects remain the same relative to themselves.

EDIT:

I should mention that converting between "image types" is nothing more than using a different compression algorithm to store the data. So when a photo is taken, the camera software compresses the raw sensor data into the target format (jpg, png, ...) and that format will have characteristic artefacts. Perhaps we don't recognise them, but they are there, and there is research done trying to reverse the information loss due to such compression algorithms. Here just a random example.