You are simply more likely to recycle if you have to think less to do so
A huge problem with the recyclying industry is that when one piece of trash is in a recycling bin, the entire bin must be thrown out
These two problems lead to the current failure of our recycling system and solving for this can spur true environmental progress
Inspiration
The feeling of uncertainty when you are faced with a bag of chips and an intimidating abundance of cans to decide from is why we created this app
Rather than being forced to make an impulsive, and often incorrect, decision, we'd like to make the right decision for you.
What it does
User takes a picture of a disposal good and the application displays the most likely categories of recyclable or trash along with confidence values for each category
How I built it
Uses a Convolutional Neural Network to classify different types of waste based on a Kaggle dataset of thousands of images
Hosted this web app on a Heroku server and connected this with an interactive UI
Challenges I ran into
Implementing Heroku and connecting our frontend to our AI backend were, without a doubt, the most difficult parts of this projects
Accomplishments that I'm proud of
We are proud of my neural network's ability to classify images into 6 different groups with such a high accuracy
We are also very proud in our Heroku server and its ability to dynamically hold and conduct our AI operations to each of our images
What I learned
We learned about the many different ways to maximize the accuracy of a neural network
We also learned how to host and upload images to a Heroku server
What's next for RecycleML
Integrating our software in hardware that can be installed near recyclying bins to make it extremely convinient to recycle
Creating a mobile app so more people can actively classify different types of waste regardless of their vicinity to a recycle bin
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