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This repository provides an implementation of semantic segmentation for road networks using PyTorch and the U-Net architecture. It focuses specifically on processing aerial images from the Massachusetts dataset.
This research work basically highlights my undergrad thesis works. In my thesis, I have worked on the BraTS 2020 dataset. My total journey of thesis from building various models to writing paper is presented here.
This project explores the application of the U-Net convolutional neural network architecture for the task of aerial image segmentation of the Dubai landscape.
Segmentation of cancerous tumors using Mamba. Code, resources, and paper provided. We manage to make a small (42k param) model that can segment pretty well.
Project outside of course scope at (BSc) Machine Learning and Data Science education programme. Colab between NGI and DIKU at University of Copenhagen.