Study Guide

Objective

This course provides an understanding of the basic mechanisms of deep learning models related with image processing. The students will learn how to develop the VGG-16 model for image recognition and the YOLO v1 algorithm for object detection from scratch. These algorithms use Convolutional Neural Network (CNN), which provides excellent performance in the field of image processing, and the main theme of this course is to learn its mechanism and application methods. This course also provides an understanding of how to use PyTorch, one the most popular framework in deep learning, including how to work with both open datasets and original datasets.

Content

Day 1: Introduction to PyTorch (popular neural network framework)
- Reviews on Neural Network Basics
- Building and training simple neural networks with PyTorch
- Image recognition using Convolutional Neural Network (CNN) and some tricks

Day 2: Image recognition with deep learning
- Image recognition using VGG-16 network model
- Using pretrained models and transfer learning
- How to use original dataset for image recognition

Day 3: Object detection (1)
- An Overview of object detection problems
- Overview of YOLO v1 algorithm
- Building YOLO v1 from scratch (1)

Day 4: Object detection (2)
- Using YOLO v5
- Fine tuning for YOLO v5 with Pascal VOC dataset

Day 5: Mini-project
- Introducing various open datasets
- Experiment with open deep learning models

Evaluation scale

0-5

Prerequisites

It is desirable to have completed the contents of Introduction to Machine Learning course. Otherwise, object-oriented programming experience is required, especially Python programming experience is desirable. Basic knowledge of neural networks and machine learning algorithms is also desirable. In this lecture, some basic mathematical expressions such as linear algebra, calculus and statistics are used.