Code used to generate synthetic scenes and bounding box annotations for object detection. This was used to generate data used in the Cut, Paste and Learn paper
As @csala mentioned in #24 it would be good to check that discrete_columns list is valid at the beginning of fitting instead of silently ignoring invalid columns then throwing an error later in the fitting process.
What I Did
Would something similar to this at the beginning of fitfunction work? :
In examples/inference.py, how do I calculate stats passed to unNormalizeData() from evo_skeleton.dataset.h36m.data_utils import unNormalizeData, as I am using my own images, and can't create a new stats.npy for my images. I would eventually like to pass video to it too.
Extended version of the Lancichinetti-Fortunato-Radicchi Benchmark for Undirected Weighted Overlapping networks to evaluate clustering algorithms using generated ground-truth communities
As @csala mentioned in #24 it would be good to check that
discrete_columnslist is valid at the beginning of fitting instead of silently ignoring invalid columns then throwing an error later in the fitting process.What I Did
Would something similar to this at the beginning of
fitfunction work? :