I finished this assignment without issues but I have a couple of questions:
Where did the true mask for each image come from? From manual labeling (unlikely…) or some other type of clustering algorithm? If it is the latter, then why do we want to train a U-net to do the clustering if an existing algorithm is already doing so well by giving us the ‘true’ mask? Is it because U-net is computationally efficient?
As the true masks for the images are used for training the U-net, the accuracy computed in this assignment thus reflects an ‘in-sample’ error of the trained U-net. I would be interested in testing the ‘out-of-sample’ accuracy by using the trained U-net for prediction on a test image. Is there a way I can upload some test images of my own and use U-net for prediction? What would the prediction in tensorflow language be like?