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
I personally do not think training personalized models should be an issue but let us hear from them.
It is better to know than assume, so let's hear from them :)
Mobile Friendly, oh my God 🙆♂️
That is my main fear. 😅
15 models won't be mobile friendly, so we would rather hear from them than assume
Wait, have you been training specialized models?
Fair enough
🤔
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.
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😭
Yes @crossentropy
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.
Wow!
Never even thought of that until i saw this discussion
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:
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!
Thank You for this
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.
@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.
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.
@meganomaly, I think it will be best to post this recent update on a new thread just as @nymfree suggested.
Will do that as soon as I can 👍