CNN

Projet personnel de classification des maladies des feuilles d'arbre. Article technique disponible sur mon blog : https://say-it-with-data-18ca51.gitlab.io/

Face Authenticator

A Face-Mask detection system.

A real-time human counting system with age and gender classification.

Darknet got illuminated by PyTorch ~ Meet Lightnet

Projet réalisé dans le cadre de l'UE - Analyse de données médicales et Deep Learning

Real-time Gender and Age Recognition from Audio

En este trabajo se varían distintos hiperparámetros de una CNN para mejorar la clasificación multiclase de imágenes

java neural networks

A project focused on weather classification using advanced deep learning techniques, specifically leveraging TensorFlow and a custom Convolutional Neural Network (CNN). The project involved the integration of four diverse weather datasets, namely ACDC, MWD, UAVid, and Syndrone, covering various weather conditions, including clear sky, cloudy, rainy, and sunny weather. Developed a custom CNN architecture using TensorFlow's Keras API, incorporating convolutional layers for feature extraction and dense layers for classification.

Just a simple object detection tested on RoboCup simulation but actually used for my own robot grasping capability.

Prediction of age from X-Ray images of hand bones using deep learning models. We used 3 models: Shallow, ResNet50, and InceptionV4. The best result achieved was with a mean absolute error of 10 months using InceptionV4. The preprocessing of data included computer vision techniques like CLAHE filter and reducing channels, and also creativities such as using the Google MediaPipe library to detect hands and crop on them.

Introduction to AI & Data Analysis: Classification of Iris-dataset with classic Perceptron Classification of MNIST-dataset with MLP and CNN

POC of a chess engine to study alphazero on a single PC using deep4j / CUDA

MLP and CNN to classify TinyImage30 dataset. Fine Tuned models. Applied Grad-Cam to identify parts of the image that highly impact the classification based on model convolution gradients. Feature-2-Seq RNN encoder/decoder network trained on the COCO dataset. The produced model is able to predict reasonable captions for provided test images.

Deep learning (CNN) deployment pipeline

A 3rd year course project at Irkutsk State University. Used custom dataset from Yandex.Toloka to train. The dataset was provided by github.com/xitowzys

CNN Scheduling Framework