Primary competition visual

UNIDO AfricaRice Quality Assessment Challenge

Helping Ghana
$5 000 USD
Completed (~1 month ago)
Computer Vision
Object Detection
487 joined
203 active
Starti
Dec 24, 25
Closei
Feb 01, 26
Reveali
Feb 02, 26
User avatar
Koleshjr
Multimedia university of kenya
Clarification
Help · 23 Jan 2026, 13:24 · 17

Hello @Ajoel and @meganomaly

From the info page

"This challenge invites you to build a computer vision model that can assess rice quality directly from images. The goal is to enable a field-ready, mobile-friendly solution where a user can take a photo of a rice sample and instantly receive meaningful quality indicators to support real-time decision-making."

Given there are 15 targets, can we train 15 specialized models for each target or we are only supposed to train one model that can predict all 15 targets. Kindly let us know so that we may not be disqualified during the evaluation stage

Discussion 17 answers
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crossentropy
Federal university of Technology, Akure

I personally do not think training personalized models should be an issue but let us hear from them.

23 Jan 2026, 13:27
Upvotes 0
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Koleshjr
Multimedia university of kenya

It is better to know than assume, so let's hear from them :)

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CodeJoe

Mobile Friendly, oh my God 🙆‍♂️

23 Jan 2026, 13:29
Upvotes 1
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Koleshjr
Multimedia university of kenya

That is my main fear. 😅

15 models won't be mobile friendly, so we would rather hear from them than assume

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crossentropy
Federal university of Technology, Akure

Wait, have you been training specialized models?

Fair enough

🤔

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nymfree

Or an ensemble of 15 models. Other than the upcoming minor shake-up, the decision to this question will separate cash prize winners from the other winners.

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CodeJoe

Even with a heavy gpu it takes 10s to 2 mins if you are infering. I should have seen this from the start. That changes everything to be honest😭

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CodeJoe
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Koleshjr
Multimedia university of kenya

True @nymfree . It would be so bad training specialized models , then during evaluation you get disqualified due to the mobile friendliness. We would rather ask than assume.

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crossentropy
Federal university of Technology, Akure

Wow!

Never even thought of that until i saw this discussion

User avatar
meganomaly
Zindi

Hi everyone

Thanks for the great question - it’s an important one.

Because this challenge is designed around a field-ready, mobile-friendly use case, the intended and recommended approach is a single multi-output model that predicts all 15 rice quality targets in one inference.

Why this matters:

  • Mobile deployment: Running many separate models on a phone is slow, memory-heavy, and fragile. A single model is far more practical for real-world use.
  • End-to-end experience: The goal is that a user can take one photo and immediately (or close to) receive all quality indicators.

While evaluation on the leaderboard is based on your submitted predictions, not your internal training process, we expect final solutions to be based on a single model that outputs 15 targets to stay aligned with the spirit and real-world objective of the challenge. We have made this clearer on the Info page.

Thanks again for raising this, and good luck!

26 Jan 2026, 14:03
Upvotes 4
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crossentropy
Federal university of Technology, Akure

Thank You for this

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nymfree

would be great if you could post it as a new message so that those not following this thread are also informed. It is highly likely that top scorers are using ensembles.

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Muhamed_Tuo
Inveniam

@meganomaly I think this is something that would have helped to clarify much earlier. I (and many others) read the rules and description, and since this wasn’t mentioned, it seemed fair to assume there were no restrictions.

Sharing this so close to the end is likely (and rightfully going) to create some tension.

User avatar
meganomaly
Zindi

We hear the concerns. We want to be fair and allow what will give the best solution - on a mobile app. While 1 may be too restrictive, 15 is also not feasible.

As a compromise we would suggest aiming for 3 or 4 models aligned with the variable characteristics.

User avatar
CodeJoe

@meganomaly, I think it will be best to post this recent update on a new thread just as @nymfree suggested.

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meganomaly
Zindi

Will do that as soon as I can 👍