Image segmentation is an important task in computer vision that involves dividing an image into meaningful segments or regions. This can be used for a variety of applications such as object detection, medical imaging, and scene understanding.
U-Net is a popular architecture for image segmentation that was introduced by Ronneberger et al. in 2015. It consists of a contracting path to capture context and a symmetric expanding path to enable precise localization. The U-Net architecture has shown excellent results on various datasets and has become a standard baseline for many segmentation tasks.
This project aims to implement an image segmentation algorithm using a U-Net-like architecture. The goal is to train the network on various datasets and evaluate its performance on different metrics such as accuracy, precision, recall, and F1-score. The project will also explore techniques such as data augmentation, transfer learning, and ensembling to improve the model’s performance.
