Compare regular CNN with depthwise separable CNN for lightweight network

Smart Automation Controller for Precision Agriculture

This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".

The preparation for the Lung X-Ray Mask Segmentation project included the use of augmentation methods like flipping to improve the dataset, along with measures to ensure data uniformity and quality. The model architecture was explored with two types of ResNets: the traditional CNN layers and Depthwise Separable.

Performance Evaluation between Normal and Depthwise Seperable Convolutions for Medical Image Classification.

MobileNet V2 transfer learning with TensorFlow 2.

Implementation and research paper of MobileNet

Lightweight Attention U-Net for Breast Cancer Semantic Segmentation

Neural Network for Low Complexity Acoustic Scene Classification

PyTorch implementation of Depthwise Separable Convolution

This repository contains research on real-time domain adaptation in semantic segmentation, aiming at bridging the gap between synthetic and real-world imagery for urban scenes and autonomous driving, utilizing STDC models and advanced domain adaptation methods.

Depthwise Separable Convolution and Albumentations

This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.

Project crafted by Antonio Ferrigno, Giulia Di Fede and Vittorio Di Giorgio for the Advanced Machine Learning course at Politecnico di Torino (2023/2024)

Implementation of state-of-the-art models to do segmentation over our own dataset.

I Implemented some of the custom complex Convolutional Neural Network architecture using tensorow.keras Functional API.

Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification"

"Advanced Machine Learning" project @ Politecnico di Torino, a.y. 2021/2022.

A TensorFlow2.0 implementation of Xception Deep Learning with Depthwise Separable Convolutions

Efficient Deep Learning for Real-time Classification of Astronomical Transients and Multivariate Time-series