For one of our applications, we need to detect small objects that can occur in multiple subsets of the image, i.e., we know that the detected object will not exceed a particular size and it will never fit the entire window/ROI.
Is there a way to specify the max_scale of the tf.classification so that it does not run the function on the entire image and starts from, say, a scale of 0.5 and goes down to a scale of 0.25? Currently the documentation says the function assumes a default value of 1:
The sliding window method works by multiplying a default scale of 1 by
scale_mulwhile the result is over