Semantic Segmentation Tutorial | semantic segmentation tutorial
IntroductionUshumansaresupremelyadeptatglancingatanyimageandunderstandingwhat’swithinit.Infact,it’sanalmostimperceptiblereactionfromus.Ittakesusafractionofasecondtoanalyze.It’sacompletelydifferentballgameformachines.Therehavebeennumerousattemptsoverthelastcoupleofdecadestomakemachinessmarteratthistask–andwemightfinallyhavecrackedit,thankstodeeplearning(andcomputervision[1])techniques!Thesedeeplearningalgorithmsareespeciallyprevalentinoursmartphonecameras.Theyanalyzeeverypixelinagivenimagetod...
IntroductionUs humans are supremely adept at glancing at any image and understanding what’s within it. In fact, it’s an almost imperceptible reaction from us. It takes us a fraction of a second to analyze.
It’s a completely different ball game for machines. There have been numerous attempts over the last couple of decades to make machines smarter at this task – and we might finally have cracked it, thanks to deep learning (and computer vision[1]) techniques!
These deep learning algorithms are especially prevalent in our smartphone cameras. They analyze every pixel in a given image to detect objects, blur the background, and a whole host of tricks.
Most of these smartphones use multiple cameras to create that atmosphere. Google is in a league of its own, though. And I am delighted to be sharing an approach using their DeepLab V3+ model, which is present in Google Pixel phones, in this article!
Let’s build your first image segmentation model togeth...