  Nowadays, many of the images captured are "observed" by machines only and not
by humans, for example, robots' or autonomous cars' cameras. High-level machine
vision models, such as object recognition, assume images are transformed to
some canonical image space by the camera ISP. However, the camera ISP is
optimized for producing visually pleasing images to human observers and not for
machines, thus, one may spare the ISP compute time and apply the vision models
directly to the raw data. Yet, it has been shown that training such models
directly on the RAW images results in a performance drop. To mitigate this drop
in performance (without the need to annotate RAW data), we use a dataset of RAW
and RGB image pairs, which can be easily acquired with no human labeling. We
then train a model that is applied directly to the RAW data by using knowledge
distillation such that the model predictions for RAW images will be aligned
with the predictions of an off-the-shelf pre-trained model for processed RGB
images. Our experiments show that our performance on RAW images is
significantly better than a model trained on labeled RAW images. It also
reasonably matches the predictions of a pre-trained model on processed RGB
images, while saving the ISP compute overhead.
