Faculty of Engineering and Information Technology School of Software
42028: Deep Learning and Convolutional Neural Networks Autumn 2019
ASSIGNMENT-2 SPECIFICATION
Due date Friday 11:59pm, 31 May 2019
Demonstrations Marks
Submission
Optional, If required.
40% of the total marks for this subject
1. AreportinPDForMSWorddocument(10-pages) 2. GoogleColab/iPythonnotebooks
Submit to
Note: This assignment is individual work.
UTS Online assignment submission
Summary
This assessment requires you to customize the standard CNN architectures for image classification. Standard CNNs such as AlexNet, GoogleNet, ResNet should be used to create customized version of the architectures. Students are also required to implement a custom CNN architecture for object detection and localization. Both the customized CNNs (image classification and object detection) should be trained and tested using the dataset provided.
Students need to provide the code (ipython Notebook) and a final report for the assignment, which will outline a brief assumptions/intuitions considered to create the customized CNNs and discuss the performance.
Assignment Objectives
The purpose of this assignment is to demonstrate competence in the following skills.
To ensure that the student has a firm understanding of CNNs and object detections algorithms. This will facilitate the learning of advanced topics for research and also assist in completing the project.
To ensure that the student can develop custom CNN architectures for different computer vision related tasks.
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Tasks: Description:
1.
2. 3.
Customize AlexNet/GoogleNet/ResNet and reduce/increase the layers. Train and test on image classification.
Implement a custom CNN architecture for object detection and localization.
Train and test the custom architecture on a given dataset for detection of multiple Objects, using Faster RCNN or YOLO object detection methods.
Training, validation and testing datasets will be provided.
Write a short report on the implementation, linking the concepts and methods learned in class, and also provide assumptions/intuitions considered to create the custom CNNs. Provide diagrams for the CNNs architecture where required for better illustrations. Provide the model summary, such as input and output parameters, etc. Discuss the results clearly and explain the different situations/constraints for the better understanding of the results obtained.
Dataset to be used: Provided separately.
Report Structure (suggestion only):
The report may include the following sections:
1. Introduction: Provide a brief outline of the report and also briefly explain
the baseline CNN architectures used to create the custom CNNs for image
classification and object detection.
2. Dataset: Provide a brief description of the dataset used with some sample
images of each class.
3. Proposed CNN architecture for Image classification:
a. Baseline architecture used. b. Customized architecture
c. Assumptions/intuitions
d. Model summary
4. Proposed CNN architecture for Object Detection and localization: a. Baseline architecture used.
b. Customized architecture
c. Assumptions/intuitions
d. Model summary
5. Experimental results and discussion:
a. Experimental settings:
i. Image classification
ii. Object detection
b. Experimental Results:
i. Image classification ii. Object detection
iii. Discussion: Provide your understanding of the performance and accuracy obtained. You may also include some image samples which were wrongly classified.
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6. Conclusion: Provide a short paragraph detailing your understanding of the experiments and results.
Deliverables:
7. Project Report (10 pages max)
8. Google Colab or Ipython notebook, with the code
Additional Information: Assessment Submission
Submission of your assignment is in two parts. You must upload a zip file of the Ipython/Colab notebooks and Report to UTS Online. This must be done by the Due Date. You may submit as many times as you like until the due date. The final submission you make is the one that will be marked. If you have not uploaded your zip file within 7 days of the Due Date, or it cannot be run in the lab, then your assignment will receive a zero mark. Additionally, the result achieved and shown in the ipython/Colab notebooks should match the report. Penalties apply if there are inconsistencies in the experimental results and the report.
PLEASE NOTE 1: It is your responsibility to make sure you have thoroughly tested your program to make sure it is working correctly.
PLEASE NOTE 2: Your final submission to UTS Online is the one that is marked. It does not matter if earlier submissions were working; they will be ignored. Download your submission from UTS Online and test it thoroughly in your assigned laboratory.
Return of Assessed Assignment
It is expected that marks will be made available 2 weeks after the submission via UTS Online. You will be given a copy of the marking sheet showing a breakdown of the marks.
Queries
If you have a problem such as illness which will affect your assignment submission contact the subject coordinator as soon as possible.
Dr. Nabin Sharma
Room: CB11.07.124
Phone: 9514 1835
Email: [email protected]
If you have a question about the assignment, please post it to the UTS Online forum for this subject so that everyone can see the response.
If serious problems are discovered the class will be informed via an announcement on UTS Online. It is your responsibility to make sure you frequently check UTS Online.
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PLEASE NOTE: If the answer to your questions can be found directly in any of the following
Subject outline
Assignment specification
UTS Online FAQ
UTS Online discussion board
You will be directed to these locations rather than given a direct answer.
Extensions and Special Consideration
In alignment with Faculty policies, assignments that are submitted after the Due Date will lose 10% of the received grade for each day, or part thereof, that the assignment is late. Assignments will not be accepted after 5 days after the Due Date.
When, due to extenuating circumstances, you are unable to submit or present an assessment task on time, please contact your subject coordinator before the assessment task is due to discuss an extension. Extensions may be granted up to a maximum of 5 days (120 hours). In all cases, you should have extensions confirmed in writing.
If you believe your performance in an assessment item or exam has been adversely affected by circumstances beyond your control, such as a serious illness, loss or bereavement, hardship, trauma, or exceptional employment demands, you may be eligible to apply for Special Consideration (https://www.uts.edu.au/current- students/managing-your-course/classes-and-assessment/special- circumstances/special).
Academic Standards and Late Penalties
Please refer to subject outline.
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