INFT2060: Applied AI Assessment Item 2: You Only Look Once (YOLO)
INFT2060: Applied AI Assessment Item 2: AI Project Trimester 1, 2024
INFT2060: Applied AI
Assessment Item 2
AI Project
Specification
Overview
Assessment Type Weighting Involvement Due Date Deliverables
Description
Written Report
30%
Group
Friday, 5 April 2024 (23:59)
- Report in pdf format
- Completed cover sheet
Student groups research a specific AI model and document their findings in form of a written report. The report is 20-25 pages long (excluding reference list and appendices) and includes:
• Title Page
• Table of Contents
• Executive Summary
• Introduction
• Background and Description
• Applications and Impact
• Experimental Evaluation
• Advantages and Limitations
• Future Directions and Conclusion
• List of references *
• Appendix A - Python Code *
• Appendix B - Supplemental Material *
Note: Sections marked * are excluded from page count
See the next pages for the topic and marking criteria. The marking criteria can also be used as a guide for the content and length of each section.
Submission
-
The report and any attachments should be compiled to pdf format and submitted by one member of the group via Canvas, which also includes a Turnitin screen.
-
Please attach a completed cover sheet on which all contributing group members have signed off the agreed group report.
-
Optionally, a document that states the contributions of each group member can also be attached. In cases where member contributions within the group are very different and/or a conflict arises, the marks may be weighted according to this document.
Topic
Your group’s topic for this report is the You Only Look Once (YOLO) computer vision model. YOLO is a fast object detection approach (Redmon et al., 2016). Since its original publication, it has seen a steady stream of new and improved versions released by the open-source community.
YOLOv5 applied to image by vwalakte on Freepik
As part of Applications and Impact, you will identify several industries on which this model may have an impact. Your Experimental Evaluation task is to experimentally evaluate the feasibility of using YOLO in at least two of the identified industries. For each industry, you will need to
-
Define a hypothetical scenario of how YOLO may be used in this industry
-
Describe a methodology and performance metric(s) that experimentally determine how well the
model performs in the hypothetical scenario. For example, you may decide to
-
– Collect a small dataset of sample images or videos that may be used in this industry to detect or track objects of interest
-
– Definelabelsthatrepresentwhattheoutputshouldlooklike
-
– Choosemetric(s)toevaluatehowwellthemodeldoesatthisparticulartask
-
-
Get access to a YOLO model for running inference
– YouwillfindavarietyofYOLOmodelsonGithuborHuggingFace– Toimproveperformance,youshouldalsolookintofinetuningYOLO
-
Run inference on your data with the YOLO model and calculate metric(s) using your labels
-
Present the experimental results according to your methodology and performance metric(s)
