I have multi-label data for semantic segmentation. For semantic segmentation for one class I get a high accuracy but I can't do it for multi-class segmentation.
I have 6 class labels so my Y train matrix is equal [78,480,480,6] ('channel last'), where 78 - number of images 480,480 -image size, 6-number of masks and X train matrix [78, 480, 480, 1]
The last lines of my CNN model:
l = Conv2D(filters=64, kernel_size=(1,1), activation='relu')(l) output_layer = Conv2D(filters=6, kernel_size=(1,1), activation='sigmoid')(l) model = Model(input_layer, output_layer) model.compile(optimizer=Adam(2e-4), loss='categorical_crossentropy', metrics=['accuracy'])
I don't know what I hove done wrong that my multi-label semantic segmentation model doesn't have proper results.
Image and masks: