💎A high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations, can easily install via pip.
I will just freeze the weights in the PipNet model itself, but I'd like to get gradients (to adjust aspects of the input image) from a loss created by the Landmarks vs some target Landmarks.
It looks like the code 'flows through' from input image to Landmark predictions - does this sound right? Or is there some strange discretisation step that would make the gradients nonsense?
I will just freeze the weights in the PipNet model itself, but I'd like to get gradients (to adjust aspects of the input image) from a loss created by the Landmarks vs some target Landmarks.
It looks like the code 'flows through' from input image to Landmark predictions - does this sound right? Or is there some strange discretisation step that would make the gradients nonsense?