ODIR-2019: Ocular Disease Intelligent Recognition is a project leveraging state-of-the-art deep learning architectures to analyze and classify ocular diseases based on medical imaging data. This repository implements advanced machine learning techniques and modern neural network architectures to push the boundaries of intelligent recognition

Implementation of the model-agnostic meta-learning framework on CWRU bearing fault dataset to address cross-domain few-shot fault diagnosis problem.

Thesis work, University of Groningen : Lifelong 3D Object Recognition and Grasp Synthesis using Dual Memory Recurrent Self-Organization Networks

A machine learning tool built with TensorFlow and the VGG16 model. It classifies waste items from images, assisting in efficient recycling. Users upload waste images, and the system identifies the waste type.

Göz görüntülerinde diyabetik retinopati belirtilerinin tespiti

Incremental Learning using MobileNetV2 of Logo Dataset

Classifying various food using transfer learning.

An image classifier developed for ImageNet dataset

An end-to-end CNN Image Classification Model which identifies the food in your image