LAB Report Template (Deep Learning)
Tutorial Title goes here
Date:
Author:
Github: repository link
Demo Video: Youtube link
I. Introduction
This lab is about ~
Write a short abstract of the project with necessary diagram.
II. Problem Statement
1. Project Objectives
Explain the objectives of the project
This project aims to develop an AI-powered vision system for a waste recycling system that sort the waste type with a robot arm. Specifically, this project needs to
Develop a model that can detect and classify a recyclable waste product
4 classes of PET, PVC, Metal, Glass
Locate and display the waste with a boundary box
Sort the waste product with a robot arm
Display the statistics of each waste product
2. Expected Outcomes
Explain what outcome and evaluation index you are going to achieve.
A classification deep learning model that can recognize waste type
A robot arm and conveyor belt system that can sort the waste automatically
GUI that display the statistics of the fault rate
3. Evaluation Index
1. Accuracy of object detection
>90%
Test image of 500 frames
2. F1-score of Anomaly Classification
>00
3. Estimate accuracy of the detection model
>00
4. Inference Time (FPS)
>10 fps
Tested on GTX1080 TI
III. Requirements
Write a list of HW/SW requirements.
1. Hardware List
Jetson Nano
Webcam
2. Software List
CUDA 10.1
cudatoolkit 10.1
Python 3.8.5
Pytorch 1.6.0
Torchvision==0.7.0
YOLO v5
3. Dataset
A brief description of dataset goes here.
Dataset link: download here
For open dataset, just include the download link.
For custom dataset, include the download link. Also, need to submit to TA
IV. Installation and Procedure
Explain the whole procedure step by step with proper headings and images.
This section is a tutorial that helps the reader to follow the whole procedure
1. Hardware Setup
A simple overview how to install the hardware setup. You may skip this if you do not have any hardware.
2. Software Installation
Do need to include installation of {Python, OpenCV, NumPy, PyTorch} , which were covered in class.
But, you should include CLI for installing libraries(Python etc), which versions are different from the ones used in class.
3. Data Preparation
Explain how to download the datasets, and how to partition train/test sets
4. Train model
Explain how to train the model. Don't need to show the whole source code.
5. Test model
Explain how to test the model. Don't need to show the whole source code.
V. Method
1. Overview
Explain the overview of your algorithm with proper diagrams and flow chart.
2. Preprocessing
Explain how you have done preprocessing on the datasets.
Also include other image processing you have done
3. Deep Learning Model
Briefly explain which the deep learning model you have used, with proper citations
4. Postprocessing
Algorithm #1: 0000
Briefly explain other algorithms you have created
Algorithm #2: 0000
Briefly explain other algorithms you have created
5. Experiment Method
Explain how you have tested for evaluation
Also include which evaluation Index were used
VI. Results and Analysis
Show the final results visually (images, graph, table etc)
Analyze the results in terms of accuracy/precision/recall etc..
Explain whether you have achieved the project objectives
VII. Conclusion
Do not write your personal comments.
This is to summarize the overall project that include the main objectives, the methods, and the final results.
Also, include what should be the further work to improve the project.
Reference
Complete list of all references used (github, blog, paper, etc)
Appendix
1. Team Contribution
2. Debugging
3. Others
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