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It's very straightforward for binary semantic segmentation: black color (0s) is responsible for background, whereas white color (1s) is responsible for objects of interest.

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But what about multiclass semantic segmentation? As far as I understand, these masks must be RGB images since we use more than two colors. Is it correct? Or should I have a separate binary mask for every class?

If I can use RGB images with multiple colors as masks, should I use some specific colors for masking? If not, should I specify colors I chose somewhere in a network as class parameters? Or will any CNN automatically detect any number of different colors in my masks?

These questions may seem naive and primitive, but I was unable to find any clear explanantions of thus aspect of multiclass semantic segmentation.

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You should create a separate binary mask (1 for the pixlels belonging to that class and 0 for the rest of pixels) for each class. Therefore, your mask array should have a shape of (BATCH_SIZE, WIDTH, HEIGHT, NUM_CHANNELS), where NUM_CHANNELS is the number of class.

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  • $\begingroup$ Can you please explain to me how to plot a mask with shape (img_height, img_width, n_classes), because I have stored my masks in that shape but I couldn't visualize them, and the same thing for my predicted masks by the UNet model, I couldn't visualize them even when I tried the argmax method test_mask_argmax=np.argmax(test_mask, axis=2), So can you please help me $\endgroup$ Feb 9, 2023 at 9:57

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