How to use data augmentation on uneven multiclass dataset?

@ZER-0-NE wrote:

I have 12 classes(images) and uneven distributed data in each of these classes.

They are as follows(all images):

X1 = 16

X2 = 203

X3 = 192

X4 = 220

X5 = 172

X6 = 143

X7 = 22

X8 = 89

X9 = 31

X10 = 89

X11 = 10

X12 = 204

I am trying to train a CNN using the given datset. I want to know whether should I apply data augmentation to only the classes having less data or to all of the classes? Has anyone trained a similar model as mine? Also, what architecture of CNN should I use? I have used this(by applying data augmentation to all classes), but I stopped since the accuracy was around 14%(I stopped in between the first epoch)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape)) # input_shape = (150,150)
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(12))
model.add(Activation('sigmoid'))

Any help would be appreciated. If anyone has any tips, I would like to hear some. It’s giving me a hard time lately.

Posts: 1

Participants: 1

Read full topic