Image Bounding Boxes Certification
Draw tight, class-correct boxes for object detection datasets and defend them under QA review.
What this certification covers
Image Bounding Boxes sits in the Data Annotation & Labeling track (Beginner). You read 3 written modules in order — each one has to be completed before the next opens — then sit a ten-question exam marked on our servers against a fixed answer key. Pass at 80% and the certificate is issued.
01Box geometry and tightness45 min
- Place boxes that hug visible object extent
- Recognise over- and under-fitting
02Class rules, occlusion and truncation60 min
- Apply the label set when classes overlap
- Handle objects cut by the frame edge
03QA, reject reasons and appeals55 min
- Write a reject reason a reviewer can act on
- Appeal a QA decision with evidence
Concepts tested
Tightness
Box edges follow the visible extent of the object without padding or clipping.
Occlusion handling
How much of an object must be visible before it is boxed at all.
Class boundary rule
Which label wins when two classes occupy one region.
Truncation
Flagging an object that runs off the image edge instead of boxing it whole.
Reject policy
The QA conditions that send a task back rather than approving it.
How to get certified
- 1. Create a free account — no card, no payment at signup.
- 2. Read the 3 modules in order and mark each complete.
- 3. Sit the ten-question exam. Retakes are free.
- 4. If you pass, pay $25 once to unlock the signed certificate.