Misclassification of Custom Model On IBM Watson Visual Recognition

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Hello Guys, PLEASE I NEED YOUR ADVICE ON THIS.

I have a problem with the IBM Visual Recognition Service, I am creating a weed detection Model using IBM Visual Recognition Service. I have carefully labelled and trained my images across the different classes.

The Model Performs well when I test for unseen images that belong to this two classes(CORN AND CHENOPODIUM ALBUM) as indicated below:

Output From Model enter image description here

But My Major Problem is when I try to test for plants outside my labelled images, The Model Identifies that as part of of my labelled images with a very high accuracy. (Plantain and Cassava)

What might be the reason for this, and how can I correct this issue..??

enter image description here enter image description here

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pythonobject-detectionibm-watsonwatson-studiovisual-recognition

1 Answers

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So you have trained the model with images of CORN AND CHENOPODIUM ALBUM, and then are testing it with images of Plantain and Cassava, is that right?

The general "best practice" for training any machine learning model, whether a classifier or object detector, is to have your training data match the test data as nearly as possible. This is summarized as "You get what you train for."

It is not always possible, but to the extent that you have knowledge of what the test data will be like then you want to sample your training data from a similar distribution.

Think of yourself like a teacher preparing a student for a test. If you teach them Spanish and then the test is in Italian, the results will not be good.

In this case, to detect Plantain and Cassava, you will need to add Plantain and Cassava examples to your training set.