Overview
What this challenge is about.
Fine-tune a YOLOv8n detector on 6 PCB defect classes, evaluate mAP with a strict false-positive budget, and deliver a model card. Earn a verifiable certificate.
The scenario
The manufacturer (around 1,400 staff at the Shenzhen line) ships around 20,000 boards/day; a 5-point recall improvement at the FP budget saves around USD 200k/year in field-returns labor.
The Brief
What you'll do, and what you'll demonstrate.
Train a PCB defect detector that achieves recall above 90 percent at the FP budget across the 6 defect classes.
Earning criteria — what you'll demonstrate
- Fine-tune a modern object detector on a defect-detection task
- Operate under a hard false-positive budget
- Write a model card that supports a real production decision
- Communicate model boundaries to a non-ML QA team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Computer Vision at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Object Detection
Apply object detection to solve real industry problems and demonstrate production-level capability.
- Transfer Learning
Apply transfer learning to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Data Augmentation
Apply data augmentation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Ml Pipelines
Apply ml pipelines to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Defect-detection models with a hard FP budget are the day-one MLE work at any manufacturing-AI or industrial-vision company.
This challenge sharpens
- object-detection
- transfer-learning
- ml-pipelines
Computer Vision Engineer
Defect-detection on real PCBs builds the CV-engineer judgment around class imbalance, augmentation, and per-class evaluation.
This challenge sharpens
- object-detection
- data-augmentation
- model-evaluation
AI Safety Researcher
Operating under explicit FP budgets and writing honest model cards is the safety-aware engineering pattern AI safety researchers practice.
This challenge sharpens
- model-evaluation
- object-detection
- transfer-learning