What is the optimal image resolution to train an image classifier with CreateML?
I want to create an image classifier model using CreateML. I have images available in very high resolution but that comes at a cost in terms of data traffic and processing time, so I prefer to use images as small as possible.
The docs say that:
The images (...) don’t have to be a particular size, nor do they need to be the same size as each other. However, it’s best to use images that are at least 299 x 299 pixels.
I trained a test model with images of various sizes > 299x299px and the model parameters in Xcode show the dimension 299x299px which I understand is the normalized image size:
This dimension seems to be determined by the CreateML Image Classifier algorithm and is not configurable.
- Does it make any sense to train the model with images that are larger than 299x299px?
- If the image dimensions are not a square (same height as width) will the training image be center cropped to 299x299px during the process of normalization, or will the parts of the image that are outside the square influence the model?
From reading and experience training image classification models (but no direct inside Apple knowledge), it appears that Create ML scales incoming images to fit a square image 299 x 299. You would be wasting disk space and preprocessing time by providing larger images. The best documentation I can find is to look at the mlmodel file created by CreateML for an image classifier template. The input is explicitly defined as color image 299 x 299. No option to change that setting in the stand-alone app. Here is some documentation (applies to Classifer template which uses ScenePrint by default):
There may be a Center/Crop option in the Playground workspace, but I never found it in the standalone app version of Create ML.