My CNN image classification model gives good predictions in all but 2 classes. What should I do?

Question

My CNN image classification model gives good predictions in all but 2 classes. What should I do?

NBK

2022年5月31日 16:47

I built a CNN image classifier for a dataset that contains 6 classes. The dataset is balanced in all 6 classes. After training, the model gives pretty good prediction accuracy in all but 2 classes. To elaborate further, let us label these 6 classes with integers from '0' to '5'. The trained model does well in predicting classes from '0' to '3'. But almost 5%-10% of class '4' image is predicted as class '5' and similarly, 5%-10% of class '5' image is predicted as class '4'.

How should I deal with this situation?

Topic cnn image-classification

Category Data Science

lanenok · Accepted Answer · 2022年5月31日 16:47

If these two classes are important to you relative to other classes, you should increase their class_weight during training.

In Keras, for example, this would be a parameter in the fit function. Like

c_weights = {0: 1., 1: 1., 2: 1., 3: 1., 4:2., 5:2.}
model.fit(..., class_weight=c_weights)

In this case, most probably, you get better accuracy for your preferred classes and slightly worse accuracy for other classes. Is this what you are looking for?

Lynn · Accepted Answer · 2022年5月31日 11:22

How well does a model trained to predict just classes 4 and 5 (trained and tested on the data for these two classes) perform? If that performs well, you could use this model as a second classifier. So initially classify the image using your original classifier; then if that predicts either class 4 or class 5, re-classify it using the second classifier.

My CNN image classification model gives good predictions in all but 2 classes. What should I do?

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