README

Description

Welcome to the README for my thesis project. This repository serves as a comprehensive guide to understand the purpose, goals, and technical aspects of my thesis research. Whether you're a fellow researcher, a student, or someone interested in the topic, this README will provide you with valuable insights.

Table of Contents

• Introduction
• Code Reference
• Installation
• Docker Images
• Usage
• Contributing
• License

Introduction

In this thesis, I explore "the ability of capsule network to inspect defects of PCB". The primary objective is "making it possible to defect inspection on images of printed circuit boards using a capsule network". Throughout this project, I aim to "the final accuracy, precision, recall rate, and F-score all reached 99.22%". You may download my thesis here.

Code Reference

The codebase associated with this thesis is available in the code directory. You can find detailed information about the code structure, modules, and usage instructions in the Code Reference document.

Installation

To set up the development environment and run the code, follow these steps:

1. Clone this repository:

   git clone https://github.com/x1y2z3456/capsule-network-pcb-defect-inspection.git

2. Navigate to the project directory:

   cd capsule-network-pcb-defect-inspection

3. Install the required dependencies ( Based on Ubuntu ) :

   NVIDIA DRIVER:

   sudo apt install nvidia-driver-510
   # verify nvidia driver
   nvidia-smi

   Docker:

   sudo apt-get update
   sudo apt-get install ca-certificates curl gnupg
   sudo install -m 0755 -d /etc/apt/keyrings
   curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
   sudo chmod a+r /etc/apt/keyrings/docker.gpg
   echo \
   "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
   "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
   sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
   sudo apt-get update
   sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

   You may take a look at the Docker documentation.

   Tmux (one of my favorite) :

   sudo apt install tmux

   You may refer to my tmux configuration, rename the file name to .tmux.conf and place it under the home folder.

   # set <prefix> key to C-a
   set-option -g prefix C-a
   
   # use C-a again to send ctrl-a to inner session
   bind-key C-a send-prefix
   
   # vi-style controls for copy mode
   setw -g mode-keys vi
   
   # select and copy like vi in vi-mode
   bind-key -T copy-mode-vi v send -X begin-selection
   bind-key -T copy-mode-vi y send -X copy-selection
   
   # paste text in vi-mode
   bind p paste-buffer
   
   # reload settings
   bind-key R source-file ~/.tmux.conf
   
   # enable mouse draging in sessions
   set -g mouse on

   After that, you will be able to:

   1. use tmux to split your command window vertically or horizontally, for example, Ctrl + A + % or Ctrl + A + ".
   2. change the window by mouse click.

Docker Images

This project offers two versions of Docker images, which provide a consistent and isolated environment for running the code. The images can be found on Docker Hub:

• Tensorflow 1.0
• Tensorflow 2.0

To use Docker for this project, you can pull the desired image and run a container:

sudo docker pull 105552010/keras:v2.4.0
sudo docker run -it 105552010/keras:v2.4.0 /bin/bash

Usage

Once you have completed the installation, you can run model training script in container.

sudo docker run --name=keras -v /home/user/capsule-network-pcb-defect-inspection/:/tmp --network=host -it 105552010/keras:v2.4.0 /bin/bash
cd /tmp
cd 200x200
python3 train_capsnet_latest15-200-full-size-da-densenet121-r8-reduce_lr-r6-r2.py

Contributing

I welcome contributions to this thesis project. If you're interested in improving the code, adding new features, or fixing issues, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the terms of the license.

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For any questions or collaborations, feel free to contact me at andy345694@gmail.com.