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Artificial Intelligence · Major Project
Clothing Detection with YOLO
Detects garments in photos and tags their category and colour
3-4 weeks build
Overview
Trains a YOLOv8 detector on a public fashion dataset such as DeepFashion2 to find shirts, trousers, dresses, jackets and other garments in a photo or live webcam feed. Each detected item is cropped and passed to attribute steps that estimate its dominant colour and sleeve length. Results come back as labelled boxes plus structured tags through a FastAPI endpoint.
What makes it stand up
- 01YOLOv8 fine-tuned for multi-class garment detection
- 02Dataset conversion from source annotations to YOLO format
- 03Dominant colour extraction with k-means on each crop
- 04Attribute classifier for sleeve length
- 05mAP and per-class precision and recall in the report
Module breakdown
- 01
Dataset
Converts, splits and balances annotated fashion images.
- 02
Detector training
Fine-tunes YOLOv8 with augmentation and early stopping.
- 03
Attributes
Colour clustering and sleeve-length classification.
- 04
Inference
Image, video and webcam detection with overlays.
- 05
API
FastAPI endpoint returning boxes and garment tags.
After this, you will be able to
- Explain how YOLO predicts boxes in a single pass
- Defend your mAP results and the weakest classes
- Demo live detection on clothes worn in the viva room
Technology stack
- Python
- YOLOv8
- PyTorch
- OpenCV
- FastAPI
- 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