For people familiar with Mask R-CNN, how might this model be used to detect clouds (in the sky) in all-sky imager data? An all-sky imager is basically a camera with a fisheye lens, mounted on the ground, and facing the sky. Assume training data is available on a pixel-by-pixel basis and is classified as either: (sky, cloud)<p>Since clouds are amorphous, it seems there would be problems trying to feed training data to the model. Could one simply use the entire training image by specifying the bounding box to the cloud to be the entire image bounds?<p>I'm exploring new models, having already tried Fully Convolutional DenseNet with semi-satisfactory results (but with very large GPU memory footprint).
Magic results but R-CNN (and other similar architectures with large number of forward passes per frame) is pretty darn slow, which severely limits its applicability.