Computer Vision and Machine Learning related projects of Udacity's Self-driving Car Nanodegree Program
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Updated
Jan 23, 2019 - Jupyter Notebook
Computer Vision and Machine Learning related projects of Udacity's Self-driving Car Nanodegree Program
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.
TensorFlow (Keras) implementation of MobileNetV3 and its segmentation head
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
Built a Fully Convolutional Neural Network to identify and track a person with a Drone.
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.
Deep learning architectures for semantic segmentation run on Jupyter Notebook: U-Net, MANet, MAResU-Net, MACU-Net.
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.
Wound healing analysis and segment the wound area
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.
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.
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.
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