Tensorflow U-net Segmentation Mask Input

I am new to tensorflow and Semantic segmentation. I am designing a U-Net for semantic segmentaion. Each image has one object that I want to classify. But in total I have images of

Solution 1:

Each image has one object that I want to classify. But in total I have images of 10 different objects. I am confused, how can I prepare my mask input? Is it considered as multi-label segmentation or only for one class?

If your dataset has N different labels (i.e: 0 - background, 1 - dogs, 2 -cats...), you have a multi class problem, even if your images contain only kind of object.

Should I convert my input to one hot encoded? Should I use to_categorical?

Yes, you should one-hot encode your labels. Using to_categorical boils down to the source format of your labels. Say you have N classes and your labels are (height, width, 1), where each pixel has a value in range [0,N). In that case keras.utils.to_categorical(label, N) will provide a float (height,width,N) label, where each pixel is 0 or 1. And you don't have to divide by 255.

if your source format is different, you may have to use a custom function to get the same output format.

Check out this repo (not my work): keras-unet. The notebooks folder contain two examples to train a u-net on small datasets. They are not multiclass, but it is easy to go step by step to use your own dataset. Star by loading your labels as:

im = Image.open(mask).resize((512,512))
im = to_categorical(im,NCLASSES)

reshape and normalize like this:

x = np.asarray(imgs_np, dtype=np.float32)/255y = np.asarray(masks_np, dtype=np.float32)
y = y.reshape(y.shape[0], y.shape[1], y.shape[2], NCLASSES)
x = x.reshape(x.shape[0], x.shape[1], x.shape[2], 3)

adapt your model to NCLASSES

model = custom_unet(
input_shape,
use_batch_norm=False,
num_classes=NCLASSES,
filters=64,
dropout=0.2,
output_activation='softmax')

select the correct loss:

from keras.losses import categorical_crossentropy
model.compile(    
   optimizer=SGD(lr=0.01, momentum=0.99),
   loss='categorical_crossentropy',    
   metrics=[iou, iou_thresholded])

Hope it helps