Bio Sortify is a machine learning model trained to classify waste as either biodegradable or non-biodegradable. The model boasts an impressive accuracy of 98.8%. This project was developed for Philly Codefest 2024, where the theme was "AI for Common Good".
A user-friendly website was built using HTML, CSS, and JavaScriptto interact with the model. The website supports both image classification and real-time classification. We used FastAPI to connect the model to the website, ensuring a seamless and efficient interaction between the user interface and the machine learning model.
The model was trained on a dataset of 250,000 images sourced from Kaggle. We used AWS Sagemaker for training, leveraging transfer learning on ImageNet V2, where we got a test accuracy of 98.8%. Initially, we attempted to train the model using a Convolutional Neural Network (CNN), but the training process was lengthy and the accuracy was only 86.54%. Therefore, we decided to use AWS Sagemaker, which also happened to be a sponsor for Philly Codefest.
Bio Sortify was brought to life by a team of six hardworking and enthusiastic individuals:
- Meghna Rajbhandari
- Saksham Rajbhandari
- Krithi Hari
- Evan Toomey
- Vruj Patel
- Uditi Shah
We would like to express our gratitude to Philly Codefest 2024 for providing us with the opportunity to work on this project. We would also like to thank AWS Sagemaker for their support.
We are continuously working on improving the model's accuracy and the website's user experience.