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Use classified test images from alexnet after training to iterate and improve dataset

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I am pre-training a neural network for classifying three classes of images.
My datasets, due to their size, are difficult to filter out and clean up and there are many outliers which are not part of any class.
I would like to filter misclassified images out from the test image set (which is a datastore) based on their score and also set a parameter that classifies only if the scores is high enough (let's say 0.9). If the condition is not met, these images are separated from the dataset into a new folder, for example.
Is there a specific example or a script that would use these [YPred, scores] outputs and correlate them to the images that are falsly predicted in the test image set?
Thanks a lot in advance!


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Accepted Answer

Saurav Chaudhary
Saurav Chaudhary on 30 Oct 2020
The one way to deal with this is:


Filip Fedorowicz
Filip Fedorowicz on 30 Oct 2020
Thank you for the reply, the first solution is pretty neat and exactly what I was looking for. I managed to however come up with the same TF vector by a script with for loops. It's good to know that there is an alternative. I basically used to output of [YPred, scores] as a filter. Thanks a lot again.

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