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Artificial Intelligence · Major Project
E-Waste Classification with YOLOv11
Detects phones, batteries and circuit boards and sorts them into bins
3-4 weeks build
Overview
An Ultralytics YOLOv11 detector is fine-tuned on a public e-waste image dataset to find items like mobile phones, batteries, cables, keyboards and PCBs in a single frame. Each detection is mapped to a recycling category — hazardous, recoverable metal, plastic — and a React dashboard logs counts per category over a session.
What makes it stand up
- 01YOLOv11 fine-tuned for multi-object e-waste detection
- 02Mapping from detected item to recycling and hazard category
- 03Works on uploaded images, video files and a live webcam
- 04Session dashboard with counts per category
- 05mAP, precision and recall curves in the report
Module breakdown
- 01
Dataset
Public e-waste images in YOLO format with a cleaned label map.
- 02
Training
YOLOv11 fine-tuning with augmentation and early stopping.
- 03
Sorting rules
Config that maps each class to a disposal category.
- 04
Inference API
FastAPI endpoint returning boxes, labels and categories.
- 05
Dashboard
React view of live detections and category totals.
After this, you will be able to
- Explain how a single-stage detector predicts boxes and classes together
- Interpret mAP at different IoU thresholds
- Demo live detection on real devices placed under the webcam
Technology stack
- Python
- YOLOv11
- OpenCV
- FastAPI
- React
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support