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Python notebook for building semantic segmentation from high resolution satellite imagery. A part of project SEERI, a collaboration between Braga Tech. and GIZ in developing website for estimating solar potential on building rooftop.
This repository contains a Jupyter Notebook that implements Gaussian Mixture Model (GMM) for semantic segmentation and background extraction. GMM class is implemented from scratch without using any libraries like sklearn.
In this notebook, I employed built-in Keras Sequential model and pretrained ResNet50 model using Pytorch to perform segmentation of satellite images of water bodies.
Semantic Segmentation of CMR with a U-Net based architecture. Implemented in TF2.X. Trainings, prediction and evaluation scripts/notebooks for heatmap based right ventricle insertion point detection on cine CMR images. Koehler et al. 2022, BVM
Semantic segmentation of LIDAR point clouds from the KITTI-360 dataset using a modified PointNet2. This is a Python and PyTorch based implementation using Jupyter Notebooks.
This repository contains the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.
The repository contains the Jupyter Notebook that perform semantic segmentation using the famous U-Net. The encoder of the U-Net is replaced with the pretrained encoder.
In this repository you can find the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.