Preprocessing machine learning training data

user_12997132 0
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I am currently looking to train a breed classifier for animals passing through a metal race, i am working in a permanent static environment, so the cameras and the static features in the environment do not change. I had an idea to use an image mask to remove the parts of the image that i don't need hence removing features that may lead to poor results in classification. I plan to apply this same pre-processing to the inference data. Is this a good idea? or should i simply train the network on the entire image?

Any advice is much appreciated.

deep-learning classification machine-learning
2 Answers

If you have the time and resources, I'd try both: a network with mask and one without.

As a general rule, pre-processing performed prior to training should typically also be performed prior to inference. Yes, in this case, I would apply the same pre-processing. It helps that you can count on your mask since your frame position is static. Your theory on it improving performance seems very reasonable.

user_95717770

I think if those parts of image is constant all the time, a good model, will learn that and if you mask them, it hurts the generalization. I would suggest to train with a large dataset, and deeper network, and use it as a baseline and compare it to your results, so you could benchmark your results.

user_49578210
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