Hi! Thank you for the Core ML Performance Report, it's a great tool! Is there a way to get peak memory footprint in addition to runtime? Thanks!

Starting in iOS 16 and macOS Ventura, OneComponent16Half will be a new scalar type for Images. Ideally, we would also like to use the 16-bit support for RGBA images. As of now, we need to make an indirection using MLMultiArray with Float (Float16 with the update) set as type and copy the data into the desired image buffer. Direct usage of 16-bit RGBA predictions in Image format would be ideal for some applications requiring high precision outputs, like models that are trained on EDR image data. This is also useful when integrating Core ML into Core Image pipelines since CI’s internal image format is 16-bit RGBA by default. When passing that into a Neural Style Transfer model with (8-bit) RGBA image input/output type, conversions are always necessary (as demonstrated in WWDC2022-10027). If we could modify the models to use 16-bit RGBA images instead, no conversion would be necessary anymore. Thanks for the consideration!

Hi all Great talk! What image sharpening pytorch model did you use in the presentation/demo? thanks