Deep learning is great. Image segmentation using neural networks is awesome. Creating datasets to train those networks is awful. It’s not just selecting which class an image belongs to or trace an easy rectangle around an object, you need to go pixel by pixel to create the best dataset. This work is fastidious to do manually. The easy solution is to use existing datasets, which are not really diversified and kind of rare if you want to work on something that’s not road scenes.

An example from a real-like segmentation dataset available here : http://adas.cvc.uab.es/elektra/enigma-portfolio/cvc10-semantic-segmentation-dataset/

You don’t absolutely need real world images labelled by hand to work with image segmentation. Simulated…

Jeff Grenier

Montréal, Canada, linkedin.com/in/jfgrenier

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