Carbocycle

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  Google Teachable Machine

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  Google Teachable Machine

Inspiration

Climate change is big and we wanted to help. However, we knew that if we wanted to see a change, we must incorporate it into our daily lives and encourage as many people as possible. Thus, we came up with an app that gives rewards to users for being environmentally friendly. Users can redeem these rewards later. The app contains a "match footprint" feature that encourages users to view similar products with lower footprints. It encourages human contribution towards fighting climate change as it lets people make conscious choices about products and also incentivizes manufacturers to invest interest in this issue since the information is publicized.

What it does

Detects various day to day garbage items and suggests which bin or landfill sector it belongs in. It also detects multiple barcodes on products and tells the carbon footprint score on it.

How we built it

We were using a pre-trained image recognition model to feed it data with various garbage items.

Challenges we ran into

Sometimes the trained model suggests the wrong bin, partly because of the lack of enough data to maximize accuracy.

Accomplishments that we're proud of

Creating a working model and app interface that conveys the idea of our project.

What we learned

App development, Working with the teachable machine, and a little bit of coding

What's next for stream hackathon code

We are trying to understand how our models can be more accurate and useful.

Built With

• javascript