OluwaseunOjeleye

Oluwaseun Ojeleye's repositories

Image-Processing-App

This repository contains the implementation of an Object Detection and Classification & Line and Circle Detection Application

Language:C++MIT18 1 1

2D-Multilayer-Perceptron-Simulator

This is the implementation of a simulator that classifies 2D data into two classes or multi-classes using a multi-layer perceptron (MLP). It was developed using Visual C++ and it can be used for learning and understanding Artificial Neural Network concepts.

Language:C++MIT4 10

Eye-Tracking

The objective of the project is to learn how to analyze and visualize eye-tracking data.

Language:Jupyter Notebook4 20

Machine-Learning-and-Deep-Learning-Projects

This repository contains a collection of machine learning and deep learning algorithms I implemented from scratch using Python and C++.

Language:C++4 20

SMS

A movie recommendation web-based application that recommends movies (using a content-based filtering algorithm) to a user.

Language:Python4 10

Spectral_and_Color_Reconstruction

This project involves spectral and color reconstruction of Hyperspectral Images based on Principal Component Analysis (PCA)

Language:Jupyter Notebook4 10

Monocular-3D-Object-Detection

Using knowledge distillation technique to create a student model for MonoFlex

Language:Python3 2 1

School-Projects

This repository contains my solutions to some of the assignments of the courses I took during my undergraduate first and second year

Language:C++3 10

Image-Annotator

This repository contains the development of a graphical image labeling tool that makes use of VS for its graphical interface. It allows users to create, import, view, correct and generate annotations based on formats such as YOLO Format.

Language:C++GPL-3.02 10

AR-App-Camera-Calibration-using-a-Rectangular-Marker

AR App: Camera Calibration using a Rectangular Marker

Language:C++1 10

Real-Time-Human-Gender-Detection

This repository contains the implementation of a real-time human gender detection application using an optimized Darknet Library. For accurate detection of faces closer or farther from the camera, YOLOv3 architecture is used. It was trained on a modified FDDB dataset

Language:C1 10