Train an Object Detector for an Autonomous-Forklift Robotics Startup
Overview
What this challenge is about.
Train an object detector on warehouse images to hit high pedestrian recall, then profile latency and write a safety-case appendix. Earn a verifiable certificate.
The scenario
The startup (around 80 staff, around 40 forklifts deployed in Indian warehouses) cannot expand without the safety officer's sign-off on the new perception model.
The Brief
What you'll do, and what you'll demonstrate.
Train a real-time pallet-and-pedestrian detector with pedestrian recall above 0.99 at the chosen operating point and on-device latency under 35 ms per frame.
Earning criteria — what you'll demonstrate
- Train modern object detectors on domain-specific data
- Select operating points under hard safety constraints
- Profile and budget on-device inference latency
- Communicate model behavior to a safety-officer audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning for Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Deep Learning for 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.
- Yolo
Apply yolo to solve real industry problems and demonstrate production-level capability.
- Edge Deployment
Apply edge deployment to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Safety Evaluation
Apply safety evaluation to solve real industry problems and demonstrate production-level capability.
- Operating Point Selection
Apply operating point selection 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:
Computer Vision Engineer
Shipping a safety-critical detector with on-device latency budgets is exactly the work CV engineers do at robotics companies.
This challenge sharpens
- object-detection
- yolo
- edge-deployment
Machine Learning Engineer
Profiling on-device inference and selecting operating points is core MLE work on edge-AI teams.
This challenge sharpens
- edge-deployment
- operating-point-selection
- pytorch
AI Safety Researcher
Writing the safety-case appendix and documenting pedestrian failure modes is a stepping stone into AI safety research roles for safety-critical systems.
This challenge sharpens
- safety-evaluation
- operating-point-selection
- object-detection