origin

Inspiration

The global pandemic has prompted an rise in online shopping. As we bought more clothes, we began to think: What exactly are consumers paying for? In a world where 93% of brands fail to pay their workers a living wage, it's up to the consumers to make the first effort in supporting ethical businesses. We realized that it's difficult for consumers to find information on the multitude of different brands out there. Companies obscure their data through unclear supply chains, and consumers often don't have the time to research. It is important to come up with a project that allows this data to be accessible to the consumer, to create a sustainable, ethical, and environmentally friendly fashion industry.

What it does

Our app, Origin, allows consumers to scan their garments. The garments will then be recognized by our machine learning software tool, which will find information on the manufacturer of the garment and details about their ethicality. The tool will use be used to create a ethical number scale that will rate the garment in terms of how ethically it was produced, making it easier for the consumer to compare different garments. The app overall is aimed at making information more accessible to the everyday consumer.

How we built it

We constructed the app using XCode and coded in Swift. We used the ResNet50 machine learning software tool from Apple to recognize objects.

Challenges we ran into

The coding within this project was all very new to us, so we had some difficulties debugging and truly understanding the code. We are glad that we decided to be rather ambitious in this project though, because it allows us to become more familiar with multiple facets of XCode and Swift. It was a challenge to figure out how to create different view controllers, but we were able to figure it out eventually, which allowed us to organize our project better.

Accomplishments that we're proud of

We are really proud of how we implemented the machine learning software tool to recognize different objects at high accuracy rates. Previously we thought applications of machine learning were out of reach for us because we aren't very familiar with the tools. When the tool worked, we were elated. We are also proud of all the other new tools we incorporated into our project, like the collection view, uploads from photo library, and the use of the camera.

What we learned

We learned how beneficial it is to travel out your comfortable zone. This project contained many components that were difficult to code and that we originally thought we wouldn't be able to do. However, we learned so much throughout this hackathon, and we are looking forward to utilizing the new skills and techniques we learned in later projects.

What's next for origin

In the future, we want to create more accurate and enhanced database systems for the app. We want to improve precision in the calculation of the ethical scores and improve the current record keeping system. We could also add scoreboards among individuals, which also people to see the progress they have made in their purchases and ranks them with friends and others on the app. This will encourage individuals to keep making ethical choices, and show them how much of a difference they are making.