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Hi we have a problem on image classification where data is quite less. However we have some flexibility on the classification.We can change the number of classes in our problem by clubbing and splitting many classes.
So my question is that is there a relation between number of classes and the amount of data required to train.
Training a CNN with scarce data is challenging because of the lack of generalization needed to differentiate pictures.
So you need a quite good quantity of data (at least 20, but it depends on the data set) per class to make a good classification.
Nevertheless, you can apply 3 solutions:
Data Augmentation by applying various transformations (rotation, scaling, etc.) on your images to make them more numerous. Therefore you can use the ImageGenerator from Keras.
from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator( featurewise_center=True, featurewise_std_normalization=True, rotation_range=10, fill_mode='nearest', validation_split = 0.2 )
Use a shallow CNN with fewer parameters to prevent overfitting.
Use a dimensional reduction algorithm like UMAP to classify scarce data in an unsupervised way. You can apply UMAP directly or on the CNN output to improve the classification.
I recommend applying the 3 solutions because they are interesting and could be connected to each other to improve the results.
