AI/ML Intern at Edunet Foundation

Out of 5000 candidates, only 40 projects were selected for the final presentation at a conference organized by the Government of India, evaluated by industry experts.

Developed a waste classification system using CNN and YOLO for organic/non-organic categorization

Built and deployed an interactive web application using Streamlit for real-time waste classification

During my internship at Edunet Foundation, I worked on an innovative waste classification project organized in collaboration with AICTE and SHELL. My project was among the top 40 selected out of 5000 candidates for the final presentation at a Government of India conference.

Project Highlights

Developed a CNN-based model for classifying waste into organic and non-organic categories
Integrated YOLO pretrained model for enhanced object detection capabilities
Created an interactive web application using Streamlit for real-time waste classification
Project selected among top 40 from 5000+ submissions nationwide

Technical Implementation

Built and trained a Convolutional Neural Network for waste classification
Implemented YOLO object detection for precise waste identification
Developed a user-friendly interface using Streamlit
Deployed the application for public access: Live Demo →

Project Architecture

CNN model with multiple convolutional and pooling layers
Image preprocessing and normalization pipeline
Real-time prediction system with confidence scores
Interactive UI for image upload and classification

Impact & Recognition

The project was recognized for its innovative approach to waste management and environmental sustainability. It was evaluated by industry experts and selected for presentation at a national conference, demonstrating its potential impact on real-world waste management solutions.

View the complete project on GitHub →

Certificate

View Certificate →