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Your First Video Dataset

The capstone project for Video Annotation for AI: From Your First Bounding Box to Paid Work — a free AI & Emerging Tech course. Pass it and you earn a certificate anyone can verify.

Start the course free Download the project workbook

The project unlocks once you complete the lessons. The workbook is free to download now so you can see exactly what is expected.

What you will submit

One document (Google Doc with view access, or an uploaded Word or PDF file) with a headed section for each of the five parts, the screenshots embedded, and links to the clip and the export file. Everything must be your own annotation of your own clip.

What to do, step by step

  1. Film a 10 to 20 second clip of a busy everyday Nigerian scene you have the right to record (a street, a market, a motor park, a football pitch), or use any clip you own. Do not film inside private property without permission, and do not include anyone's face close up; distant people and vehicles are fine. Keep it under 30 MB (lower the resolution if needed).
  2. Part 1. Project setup: in CVAT (free tier or self-hosted) or Label Studio, create a project with at least three classes that appear in your clip (for example person, car, keke, okada, bus, dog) and at least two attributes (for example occluded, moving). Take a screenshot of the label configuration.
  3. Part 2. Tracking: annotate at least five objects as tracks using keyframes and interpolation, following each object for as long as it is visible. At least one object must go behind something or leave the frame and come back, and you must handle it with the occluded and outside flags as Lesson 5 and Lesson 7 teach. Include screenshots of three different frames showing your boxes.
  4. Part 3. Mini-guideline: write 6 to 10 numbered rules another annotator could follow to reproduce your labels: class definitions, how tight the boxes are, minimum object size, what to do with reflections, occlusion and partial objects, and at least two edge-case rules specific to your clip.
  5. Part 4. Export: export the project as CVAT for video XML or COCO JSON, open the file, and paste a short excerpt (10 to 20 lines) with two sentences explaining what the excerpt shows (which object, which frames, which fields). Share the full export file by link.
  6. Part 5. Self-QA: review your own work and list at least five errors you found and fixed (box jitter, a drifted interpolation, a missed re-entry, a wrong class, a loose box), quoting the frame number for each, plus two errors you deliberately looked for and did not find.

Files to work with

Project workbook (fill in, then submit)The whole brief, an evidence checklist, the grading rubric as a self-check and the link-sharing steps in one file. Opens in Word, Google Docs or WPS.Word · 7 KB

How it is graded

CriterionWeight
The project is set up with sensible classes and attributes that match the clip14%
Tracks use keyframes and interpolation correctly and keep the same identity across frames24%
Occlusion and objects leaving and re-entering the frame are handled with the right flags16%
The mini-guideline is clear, complete and includes genuine edge-case rules18%
The export is correct and the excerpt is explained accurately12%
The self-QA shows honest, specific error-finding with frame numbers16%

You need 65% overall to pass. A failed submission comes back with feedback and can be revised and resubmitted.

Before you submit: make your link public

If your work lives in Google Drive or Google Docs, open Share → General access and change “Restricted” to “Anyone with the link” (Viewer). Then paste the link into a private browser window: if it opens without sign-in, you are ready. A private link cannot be graded — it is the single most common reason a good project fails.

Frequently asked questions

Do I have to finish Video Annotation for AI: From Your First Bounding Box to Paid Work before submitting the project?

Yes. The submission screen opens once every lesson in Video Annotation for AI: From Your First Bounding Box to Paid Work is marked complete. The lessons are where the methods, the Nigerian context and the worked examples the project depends on are taught.

How is the project graded?

An examiner scores each rubric criterion from 0 to 100 and weights them as shown on this page; you need 65% overall to pass. Most submissions are graded within minutes and you get written feedback on what was strong and what to improve.

Can I resubmit if I fail?

Yes. A failed project comes back with feedback; revise the weak parts and resubmit. A passed project is final — the certificate is issued and cannot be re-rolled.

What do I actually submit?

A write-up of what you did (under 5,000 characters) plus a public link to your work — a Google Drive folder, Google Doc, spreadsheet, GitHub repository or video. The link must open without sign-in; a private link cannot be graded. The free project workbook on this page walks you through all 6 steps.

Is the certificate real?

Yes. Passing this project issues a certificate with a unique verification code. Anyone — an employer, a client, a school — can open the verification page and see that the certificate is genuine and which project earned it.

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Ready to earn this certificate?

Video Annotation for AI: From Your First Bounding Box to Paid Work is free and self-paced. Finish the lessons, complete this project, and the certificate is yours to share.

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