Mammeri, OumeimaMedkour, RomaissaChibani, Meriem2022-11-152022-11-152022http://hdl.handle.net/123456789/14306Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. It is a building block for scene understanding by classifying all pixels of an image in a dense way; it is then possible to build abstract representations focusing on the objects and their shapes. In this project, our work exploits a convolutional neural network (CNN) architecture "unet" for semantic segmentation; the choice of this architecture is due to its phenomenal success, it can solve the most complex problems in deep learning. It is a particularly effective tool for many types of images. Our model is trained on the Oxford-iiit pet dataset.enComputer visionDeep learningSemantic segmentationUnetOther