Waste Sorter Assistant

A powerful tool for simplifying waste management. With real-time object recognition, this app identifies and classifies items, guiding users to properly sort them into compost, recycling, or landfill.

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Inspiration

I was inspired to create the Waste Sorter Assistant by my commitment to environmental conservation. I wanted to develop a solution that simplifies waste sorting and encourages responsible disposal of items.

What it does

The Waste Sorter Assistant is a web application that leverages computer vision and machine learning to classify waste items in real time. Users can point their cameras at items, and the app provides instant guidance on whether to place the item in the recycling or landfill bin.

How I built it

I built the Waste Sorter Assistant using a combination of technologies. I trained a ResNet deep learning classification model using PyTorch and trained on a dataset from Kaggle. My server-side logic is powered by Python and the Flask framework. I integrated OpenCV for computer vision and camera access.

Challenges I ran into

  • Integrating real-time camera feed with the web application.
  • Training and fine-tuning the deep learning model for accurate waste classification.

Accomplishments that I'm proud of

  • Successfully implementing real-time waste classification.
  • Creating a working web-app with amazing performance.
  • Overcoming technical challenges related to computer vision and model integration.

What I learned

Throughout this project, I learned valuable lessons in computer vision, deep learning, web development, and user interface design. I gained hands-on experience in managing real-time video feeds, training and integrating machine learning models, and making a complex technology accessible to users.

What's next for Waste Sorter Assistant

In the future, I plan to enhance the Waste Sorter Assistant by:

  • Adding food detection to determine whether an item should go in the compost bin.
  • Improving the accuracy of waste classification through additional training and model optimization.
  • Extending the app's features to provide educational resources on responsible waste disposal.
  • Exploring the possibility of mobile app development for increased accessibility.

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