A PyTorch Computer Vision (CV) module library for building n-D networks flexibly ~

The dataset used for the "A non-contact SpO2 estimation using video magnification and infrared data" publication

3D CNN for protein structures

This project involves the identification of different actions from video clips where the action may or may not be performed throughout the entire duration of the video. This is done using two CNN models which are 3D-CNN and LSTM models.

The objectives of this project are: Developing an extension of the methodology described in the paper “Growing Neural Cellular Automata” to facilitate complex structure generation in three dimensions and to simulate the growth of multicellular structure starting from a single cell.

Multi-Label-Prediction-For-videos-using-Zero_Shot_Learning

Pseudo-3D CNN networks in PyTorch.

A motion gesture detection and recognition model with depth estimation made with a 3D-CNN using the 20bnJester dataset.

This project focuses on the segmentation of brain tumors in 3D MRI images using Convolutional Neural Network (CNN) models. The research compares the performance of SegNet, V-Net, and U-Net architectures for brain tumor segmentation and evaluates them based on complexity, training time, and segmentation accuracy.