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Currently the data.npz output, by default is split into a test and train set with 80% of the data going into training and 20% of the data going into the test set. While the ratio, number of split sets, and names of split sets can be changed, it could be better to return a data.npz and let users handle splitting the data outside of label-maker for increased flexibility.
I think it could be useful if the new version of the data.npz file returned as the x-y-z tile id as the key and the values is a list with 2 elements, the first being the label in numpy array format, and the second being the image in numpy array format.
Another benefit of this change will be that the image label pairs in numpy array format will retrain the x-y-z tile id. The x-y-z tile information is currently only associated with the labels.npz file and not associated with the data.npz file.
instead of data.npz output will explore the final output using x-array for more flexibility but including a utils script in the repo or example script to go from the final output to tf-records for modeling purposes
Currently the
data.npz
output, by default is split into atest
andtrain
set with 80% of the data going into training and 20% of the data going into the test set. While the ratio, number of split sets, and names of split sets can be changed, it could be better to return adata.npz
and let users handle splitting the data outside of label-maker for increased flexibility.I think it could be useful if the new version of the
data.npz
file returned as thex-y-z
tile id as the key and the values is a list with 2 elements, the first being the label in numpy array format, and the second being the image in numpy array format.Another benefit of this change will be that the image label pairs in numpy array format will retrain the
x-y-z
tile id. Thex-y-z
tile information is currently only associated with thelabels.npz
file and not associated with thedata.npz
file.thoughts? @drewbo
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