A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps

Implemented 3 neural network architectures: 1) Combination of RNN LSTM nodes and CNN, 2) CNN with residual blocks similar to ResNet, 3) Deep RNN LSTM network; and compared their performance to detect 12 speech commands.

Weed Detection in Sugar Beet Plants

Graduation Project. Applying Generative Adversarial Networks(GAN) with Residual-In-Residual(RIR) blocks.

This is an implementation of FRED-Net using keras.

Python Keras CNN Implementations

Semantic segmentation for brain tumors

Fast bare-bones implementation of convolutional layers, residual blocks, Adam optimizer, backpropagation and custom accuracy and loss functions (F1 score per pixel and binary-crossentropy)

Attention Gate Residual UNet for vein image segmentation in the field of biometric identification

Deep learning model to predict the normal flow between two consecutive frames, being the normal flow the projection of the optical flow on the gradient directions.

Emotion and Facial Key-Point Detection Classify emotions and detect facial key-points using deep learning! This project combines CNNs and Residual Blocks to predict 15 facial key-points and categorize facial expressions into five emotions: Angry, Disgust, Sad, Happy, and Surprise.

This repository contains code and data for medical image processing tasks. It includes various scripts for processing, analyzing, and evaluating medical images using deep learning techniques, with a focus on models like U-Net and ResNet.