Built a waste classification model using CNN and YOLO, achieving 92% accuracy on test data. Deployed a real-time detection demo with Streamlit.
As part of the Edunet Foundation (AICTE & SHELL) internship, I developed a deep learning model for waste classification. The model, built using Convolutional Neural Networks (CNN) and YOLO, can accurately distinguish between different types of waste.
The project ranked in the top 40 out of over 5,000 submissions by industry experts. We enhanced the model's inference speed by 25%, enabling efficient real-time usage in a Streamlit-based demo application.