Wintec on Alex's E-Portfolio

Bachelor of Applied Information Technology

Sun, 03 Jul 2022 00:00:00 +0000

I have now completed my Bachelor of Applied Information Technology. It took me three years to do all the course content. Over this time, I have made many lifelong friends. I had an average grade of A+. My favorite paper was COMP605, Data Structures and Algorithms. I found the content clicked with me.

Over this time, I also took part in the Applied Deep Learning Research Group. As part of this, I helped with the paper Automatic counting of chickens in confined area using the LCFCN algorithm. I also joined the Peer Tutor program. Near the start of my time as a tutor, there was a third-year student in need of tutoring. As they did not have any third-year tutors, I was asked to step in and tutor him despite the fact I was a year behind him. He needed help with programming in the language php. In the course we had not yet covered php but, I had experience from outside of Wintec. I was able to apply my knowledge to successfully help him pass the paper.

LCFCN Paper

Fri, 01 Jul 2022 00:00:00 +0000

Over the last year I have been working on a research paper with some of my tutors at Wintec. For this paper we did research into LCFCN. You can read the paper here.

I started off with an implementation called Deepfish. I added support for Microsoft Windows. Documented and built a consistent installation script to enable reproducible installs. I also implemented a custom loss function for the network. I developed wrappers so that my custom labeling tool could generate predictions and make training data correctly.

I developed a custom point level annotation tool in Python. This enables the user to quickly label all the objects in an image and save it to an extensible text-based format. The tool used OpenCV to display the images. It was put to the test labeling more than 1000 pictures. Due to the annotation format being lightweight, sharing annotations between collaborators was easy.

Over the research we used a variety of open-source resources and on-site photography to make custom data sets. To extend the data sets data augmentation techniques were used. Due to the architecture of the network, it cannot distinguish between an image and a mirrored copy. This enabled us to 8X the data we labeled.

One of the data sets that we used we have published. It is a data set of cows labeled from aerial photography provided by the New Zealand government. All the pictures are 500 pixels square and have a density of 10 centimeters per pixel. The data set is hosted here.

I volunteered my personal computer to provide the compute power necessary. It took multiple days per dataset to train the network. Once trained the network would then be tested against a final holdout validation set. I wrote some scripts to take the final numbers and compute overall loss values. Participation certificate for ISCV2022

The paper was then presented at an international conference. I received a certificate of participation for my contributions to the paper.

Deep Learning

Fri, 02 Oct 2020 00:00:00 +0000

I have joined the Applied Deep Learning Research group. We are working on revolutionizing animal welfare. Our project combines satellite imagery with artificial intelligence to create a cutting edge automated dairy management system.

Danger Dag

Tue, 21 Apr 2020 00:00:00 +0000

I made this game in Games Development (COMP706). It is a 2D platformer. I made the movement code for the main character from scratch. The tiles in the levels were made using a combination of 3D and 2D graphics. I used Godot for the game engine as it has a good mix of community support and ease of use. I ended up with a polished final product and got A++ for the game.

You can play the game for free right now here.