mohanty

Suggestion to switch from spot to on-demand

@jurgisp: This is a valid suggestion. We will discuss internally early tomorrow, and adjust the submission quotas for the last week and move to on-demand instances.

We should be able to manage 2 submissions a day until the end of the competitions.

We will post an update on this thread after a decision has been made.

Cheers,
Mohanty

View post 2

Anyone else having trouble with Plunder rollout timeout?

Hi everyone,

We are acknowledging this issue. And after discussing internally, we will post an update later today.

But overall, we would be okay with relaxing (a little bit) the timeouts for the rollouts in this and subsequent rounds.

Cheers,
Mohanty

View post 2

FAQ: Round 1 evaluations configuration

@scitator: We will unfortunately have to stick to the ray+rllib starter kit that we have been using since the beginning of the competition.

We will however be open to suggestions around how the starter kit should look like for the future iterations of this competition.

View post 0

Is the private environment score on public leaderboard

Hi Dipam,

Thanks for pointing this out. And yes we are aware the releasing the private score will lead to metric hacking.

But the metric hacking in this round will not help in the subsequent rounds where we will have different private environments. And finally at the end of the competitions we will have a final exhaustive evaluation where the code submissions will be evaluated on all the available public environments and all the private environments to measure both sample efficiency and generalisation.

Hence any metric hacking attempts in these rounds will not bear fruits.

The private scores in this round are made available to the participants to also prime the community of participants to get used to being evaluated on completely unseen environments.

Hope this clarifies your question.
Cheers,
Mohanty

View post 4

Selecting seeds during training

Hi @Leckofunny,

So in the warm-up round and round-1, we will not be evaluating generalization. So during the training phase your code will have access to all the levels.

In round-2, we will be restricting access to only 200 levels during the training phase by enforcing num_levels=200 during all env instantiation.

Does this answer your question ?.

Cheers,
Mohanty

View post 0

Impala Pytorch Baseline Bug(s)

You mean low throughput on the evaluation server or also locally ?

In any case, @jyotish: do you want to share the throughput spreadsheet you prepared to tune the num_workers etc for the baselines ?
It might be helpful for participants to get a rough baseline of what throughput to expect in different scenarios.

View post 0

Several questions about the competition

Hi @the_raven_chaser,

When you are using num_levels=0 during training, the env samples from all the possible levels for the said env. So it would make sense to use num_levels=0 during your local evaluation too.

And yes, we use the difficult as easy throughout the competition.

View post 0

Several questions about the competition

@the_raven_chaser: the num_levels=200 setting would only be used in the final round when we are evaluating the generalization.

For the warm_up round and Round 1 of the competition, there are no restrictions on num_levels, and hence technically we are only measuring sample efficiency.

View post 1

Multi-Task Challenge?

@Leckofunny: while there are multiple environments that your submitted code will be evaluated against, there is no expectation to jointly learn across all the environments.

Your code will be evaluated independently across each of the N environments (depending on the round), and then the performances across all aggregated.

So in practice, if your submission is being evaluated against N different environments, then at the end of the N parallal training phases, we will have N different agents, one for each of the environments.

Hope this is clarifying,
Cheers,
Mohanty

View post 0

About the NeurIPS 2020: Procgen Competition category

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View post 0

📅 Deadline Extension

View post 0

📅 Deadline Extension

The deadline for Round-1 submissions will be extended until, 27.10.2020, 12:00 UTC.

The following round timelines are adjusted as follows :

Round-1 : September 14th, 2020 - October 27th, 2020
Round-2 : October 28th, 2020 - November 30th, 2020

The community contribution prize deadline is also extended until 23rd October, 2020, 12:00 UTC.

View post 1

About the number of submission

@AmaurySu: I acknowledge the argument, but the low per-day submission count is by design.

If we provided a much larger number of submissions, then in the process of trying to fix the divergence in the validation set scores and training set scores, you will end up overfitting to the test set.

In this case, you should try ensuring that multiple training-validation splits at your end have consistent scores, and then ideally a similar score should translate as the score on the test set.

Cheers,
Mohanty

View post 0

Announcement : Dataset Update!

View post 0

Announcement : Dataset Update!

We noticed a data alignment issue in the released dataset. The said issue has been fixed at 16 Sep 2020 19:14:27 CEST and all the dataset files have been updated.
If you have downloaded the data before that, we request you to redownload the corrected datasets from the Resources section.
Apologies for any inconvenience caused.

Cheers,
Mohanty

View post 2

NoSuchKey when downloading data

@cfernhout: You should be able to download the data now ! Cheers, mohanty !

View post 0

NoSuchKey when downloading data

@cfernhout: We are updating all the datasets at the moment, and the datasets should be back online in about 20 minutes from now. Sorry for the inconvenience.

View post 2

Clarification : Submission Count

@rohitmidha23: the number of submissions is 5 per day, and we will be updating the same on the challenge overview.

Thanks,
Mohanty

View post 0

Rule Clarification: Code release

Hi @NotAlphaGo,

An express code release is not required for this competition. However the participants will have to share their (reproducible) code with the organizers for a manual verification before any prizes can be claimed.

That said, the nature of the Community Contribution Prizes require the associated code to be released publicly, however you have complete freedom to choose a license of your choice to release the code under.

Cheers,
Mohanty

PS: the site wide Terms impose an MIT license if you do not attach a license already. In case if you attach a license of your choice, then that overrides the default MIT license.

View post 1

📅 Round-1 Deadline Extension

View post 0

📅 Round-1 Deadline Extension

The deadline for Round-1 submissions will be extended until, 27.10.2020, 12:00 UTC.

The following round timelines are adjusted as follows :

Round-1 : September 8th, 2020 - October 27th, 2020
Round-2 : October 28th, 2020 - November 29th, 2020
Round-3 : December 1st, 2020 - January 10th, 2021.

The community contribution prize deadline is also extended until 23rd October, 2020, 12:00 UTC.

View post 0

What is the meaning of top2 in the new leaderboard?

The final ranks are determined by the top-5 score. In the unlikely event of an exact tie in top-5 score, top-2 will be used as a tie breaker.

View post 3

"AssertionError: Unknown smell words provided in the predictions file"

@cedric_bouysset @mtrofimov : Confirming that we have updated the evaluator to be more kind towards repeated words in the same sentence. This was also a bug only in the sentence validation. During the score computation, the list of words was always type casted into a set which ensured that duplicate iterations of the same word would not affect the final score.

View post 2

"AssertionError: Unknown smell words provided in the predictions file"

@guillaumegodin: I think @mtrofimov 's point was more around a helpful error message so people are not left confused.

We are debugging this particular issue, and will get back on this thread with an update soon.

Cheers,

View post 0

Question about rounds

@robert_allaway: That is a good question, and happy to clarify. Please find my responses inline.

Will we need to create Docker and/or Singularity containers?

The submissions will be code repositories with a Dockerfile in it, and we will be building docker images (and subsequently orchestrating them as docker containers)

Will the submissions have access to GPU (and if so, what kind)?

Yes. The submissions can request a GPU (by using a friendly config file), and at the moment we can confirm that all submissions will have access to atleast a NVIDIA K80, but we might be able to also allot NVIDIA V100s (to be announced over the next few weeks).

What is the maximum runtime, CPU cores, memory available to the submissions?

All submissions will have access to 4 CPU cores and 16GB of memory. The maximum runtime has not been decided yet, and will be decided at the end of the first round. A tentative number will be of the order of a max timelimit of 3s / data-point.

Will the containers have networks access?

The containers will not have any network access to prevent any accidental or malicious data leak.

Cheers,
Mohanty

View post 5

Question about rounds

@young: No, there are no qualification criterias across the rounds. So all participants will be able to make submissions across all the rounds independently.

View post 1

Why do invalid submissions count against quota?

@robert_allaway: Thanks for pointing this out, and we have fixed the same. Now upto 5 failed submissions will not count towards your submission quota. And we have also increased the submission quota to 10 per day.

Best of Luck,
Mohanty

View post 2

External solution using using Graph Neural Networks (GNNs)

@hengck23: we do have some broad ontology of how all the labels are related to each other, and we will be releasing the same data in the subsequent round. But until then, it is indeed a clever idea to try and build an ontology of the odor words just by using pre existing language models. Would love to see how they compare with the ontology we will finally release.

If you come up with some exploratory notebooks in that direction, we will strongly encourage you to share some colab links for others to jump on exploring the idea !

Cheers !

View post 2

Interpretation of repeated "sentence" values

@young @guillaumegodin: Also confirming that, the datasets in the resources section have been updated to remove the duplicate items.

Cheers

View post 1

Interpretation of repeated "sentence" values

@robert_allaway: Thanks for pointing it out. The reason for the repetitions of the odor words in some of the sentences is because many low-frequency odor words were replaced with their parent categories in the first version of the dataset. Hence, in this case, all the repetitions can be safely treated as a single instance of the odor word in the same sentence.

In the example above, the sentence for C=CCS can be treated simply as alliaceous,cooked,roasted,burnt,meat.

In the future rounds of the competitions we will be releasing the data without the low-frequency odor word replacement.

In any case, given that each of the sentences represent a set of the odor words, the representation do not affect the problem, or the evaluation metric computation. We will however soon update the current dataset to remove the above mentioned replications.

Cheers,
Mohanty

View post 3

Question about rounds

Hi @denis_vorotyntsev,

Across the different rounds we will be introducing more data, and also we will transition slowly from a simple submission system where we evaluate just the submitted predictions to a submission system where we evaluate the code submitted by participants on some internal test data which cannot be released due to legal constraints.

Hope that clarifies your question.

Cheers,
Mohanty

View post 3

RE: What is the cause of ambiguity of structure and odor label

Hi @guillaumegodin,

Given you are an active organizer on the platform, it might be great if you can update a profile picture for your account. You can do so at :

• Go to aicrowd.com
• Click on your profile image on the top-righ
• Edit Profile

Thanks,
Mohanty

View post 0

Mailing list

Hi Martin,

I am looping in Shivam to respond on the emails approval.

Cheers,
Mohanty

View post 0

Mailing list

View post 0

RE: Computational approach

More concretely, I remember your tweet, and it actually resonated well with something I had planned for the Blitz. Would you be interested in doing a screencast (imagine a joint video call with screen share with 2-3 people) who collaboratively try to solve each of the problems one after the other Writing code on the go (with all the mess and mistakes :’)), with a beginner-friendly target audience in mind

I will be doing that myself. But I thought it would be cooler if 2-3 people are doing it together where everyone thinks out loud. Disclaimer : It can get messy even in case of really simple problems like these

View post 0

https://discourse.aicrowd.com/t/

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View post 0

Explained by the Community | 100 CHF Prize contest 🎉

@ashwin_ramesh: You should consider including a Google Colab link for easier replication by the participants. Great job nevertheless !

View post 1

Can we use pre-trained pytorch models?

Yes, the broader sentiment is that as long as the pretrained models are generally available for all participants you can very well use them.

It would also be important to announce all such pretrained models that you are using, on the forums.

Cheers,
Mohanty

View post 1

Affiliation of richardalligier

Hi everyone,

I appreciate discussing the topic in the open, and very much appreciate the clarifications provided by @richardalligier and @masorx.

I am also particularly disappointed by the tone of the messages by @UserSerb.
As someone who has been working quite hard to help shape the AIcrowd Community, I am convinced this behavior is not welcome here.

Coming back to the actual topic of this thread. Here is what we know :

• @richardalligier has been working with the raw ADS-B data by applying to get access to the data via the OpenSky Network webpage. As any other researcher interested in the topic could very well get.
• As confirmed by @masorx, Richard has no affiliation with OpenSky network, AIcrowd, and has no privileged access to the test sets, or any private decisions made when designing the competition.
• As confirmed by @masorx, the raw OpenSky data that Richard had access to does not have the timinig and the RSS data that are key to this competition.

I acknowledge the concerns of @sergei_markochev, but they have been very well cleared by @masorx.
If we host a competition around a dataset derived from ImageNet, it will be really hard to dis-qualify all the participants who have had experience working on the ImageNet before the competition began (again by requesting access to the dataset through the website like any other researcher).

Conditional on the fact that @richardalligier and @masorx have established their positions correctly, my personal assessment on this topic is that there has been no violation of rules.

@UberSerb: The tone of your messages are not in the spirit of the community that we are trying to build here. We acknowledge your threats to sue everyone involved, but we cannot also sit silently while you harass members of our community publicly. We welcome you and “your friend in zurich” to send us a legal notice, if you can even actually make a case. You can reach out to us at hello@aicrowd.com.
If we receive another incident report on harassment related to your account, we will have to suspend your account.

To avoid situations like these, at AIcrowd, we will work urgently to put together a Code of Conduct which can help set guidelines on what behaviors are acceptable in the community and what are not. I apologize for the oversight on our end to not have a mature Code of Conduct in place already.

I finally would like to thank @vitaly_bondar for speaking up. We need more community members like you to help make this is safe space that our members enjoy devoting their time to.

Best of luck to all the competitors.

Sharada Mohanty,
CEO / Co-Founder,
AIcrowd

View post 12

About the CYD Campus Aircraft Localization Competition category

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Quick-Start Model

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About the ECCV 2020 Commands 4 Autonomous Vehicles category

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About the ECCV 2020 Commands 4 Autonomous Vehicles category

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About the ECCV 2020 Commands 4 Autonomous Vehicles category

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About the ECCV 2020 Commands 4 Autonomous Vehicles category

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About the ECCV 2020 Commands 4 Autonomous Vehicles category

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View post 0

Round 2 Finished

Hi @simon_mezgec,

Thank you for your participation in the competition !
Pulling off code based submissions like these are actually quite hard because of the many moving parts, but the patience and feedback from participants like you help us get better one little step at a time. So thank you again !!

And this competition is based on top of this idea of trying to truly do Open Research, where we share our data and the results with the whole community even before they are mature, and hopefully engage the community to get much more mature and much better results to begin with ! We hope to see your continued engagement in the competition ! The official announcement for the food competition should come out soon, and we hope to gather all the top and active participants on a few community calls to register your voices in the future directions of this research initiative !

Looking forward to talk to you soon,
Mohanty

View post 4

New tag does not create an issue or evaluation

Another hypothesis I have, is that in your git history you have large models checked in.
Even if you use git-lfs from now, your git client will try to push the large models in your git history to our github endpoint, which would reject the request because of it breaching the file limit.

A solution could be to scrub your repository history off of large file blobs.

Cheers,
Mohanty

View post 1

New tag does not create an issue or evaluation

Weird. I am pretty sure, I have checked in much larger models via LFS.

@shivam: do let us know if you figure out the reason.

View post 0

New tag does not create an issue or evaluation

@frgfm: I suspect you are actually not using GIT-LFS to check in your models ?

@shivam: Maybe we should reduce the file size limits of file blobs that can be checked in directly via git. That helps ensure if checkpoints are by chance being checked into the repository directly via git, it should throw up a meaningful error much before.

Cheers,
Mohanty

View post 0

About the Mask Detection Challenge category

Interested in detecting masks in the wild? Train object detection models
capable of identifying the location of masked faces in an image as well
as the location of unmasked faces in the image.

Challenge Page: https://www.aicrowd.com/challenges/mask-detection-challenge

Discussion Forum: https://discourse.aicrowd.com/c/maskd-mask-detection-challenge/261

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About the dfsdsfdsfds category

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Use of external data

Hi @O_O,

Thank you for the thoughtful comment, and I personally understand where it comes from.
And I was expecting something along these lines when I wrote my previous comment.

Much of your comment is based around the idea of “releasing the test set”.

In context of DIBRD, the actual test set (with labels) is one google search away. When I included the link, it was also an attempt at being more transparent. Security through obscurity is a myth ! A handful of people who already were abusing the dataset had an unfair advantage which many other had no clue about. Even in a completely competitive setup, I would be a lot more comfortable knowing that everyone had equal access to all information.

Coming to ORIENTME. Great suggestion about actually not releasing the test set. Infact, a lot of the research challenge we run do not actually release the test set at all. But given that those competitions expect code submissions (imagine elaborate code repositories with exotic software runtimes), they happen to have a huge barrier to entry for participants, especially the ones quite new to the field.
And more so, in context of ORIENTME, have you considered that maybe the actual insight on how you can use the test data cleverly was the takeaway we hoped the participants would arrive independently at ? Historically speaking, many hard problems, and many amazing results have had a key moment like that : a simple solution hugely outperforms the obvious more sophisticated solution. As mentioned in this response, one of my most favorite examples of this phenomenon is the results detailed in this paper : https://arxiv.org/pdf/1505.04467.pdf

And thank you for highlighting our faults, we happily acknowledge that this is a huge learning experience for us, with every new challenge that we run. And we will continue to try and improve the experience we bring for the participants. And thank you for all the feedback here, we will weigh them in in the design process for the next iteration of AIcrowd Blitz.

Thanks,
Mohanty

View post 1

Use of external data

@gaetan_ramet: Algorithmic solutions are acceptable. Infact, we wish computationally feasible Algorithmic solutions existed for all ML problems

100% reproducibility is ideal, but we understand if some variation in the result is present. The internal team will use best judgement to deal with any arbitration related to the same. In some future competitions we will also collect code as a part of the evaluation process (like numerous of research challenges we run on AIcrowd).

We are finalizing the guidelines for publications of the solutions, but they will all be aggregated in a single repository with proper credit attribution (via the git authorship attribution). We will release the same soon.

Cheers,
Mohanty

View post 0

Use of external data

@gaetan_ramet : That does indeed qualify as “unfair activity”.

To help put this in perspective, AIcrowd Blitz is an educational initiative. The goal here is to encourage community members who are interested in learning about ML by using these problems as short term goals. Some of the problems in AIcrowd Blitz (and in the broader educational initiatives of AIcrowd) will be based on text book toy problems, publicly available datasets, etc. Hence, someone willing to try and cheat on a problem in these educational initiatives might very well cheat. That is definitely against the spirit of this event.

Also, at the end of the competition before claiming any of the token prizes, the participants are expected to publicly share their code/notebooks that they used to generate the final predictions. If that is not the case, then we will unfortunately have to disqualify the said submission and also cite the reason for the same (something which stays a public record forever).

I like to think of these educational problems as a set of weekly/monthly milestones that we would share with all of our community, especially the young members of the community who are very new to the field. These milestones will hopefully help everyone to be consistent while they are trying to learn various aspects of AI and ML. (Something similar to daily health goals many people like to have : drink N glasses of water, take M steps, etc).

The existence of the centralized leaderboard is to help everyone see the broad progress of everyone else who are in the same boat as them. Now, if some participants can gain the satisfaction of solving a problem simply by cheating, then it is mostly a loss for them !

That said, as soon as the competition ends on May 17th, we will internally be coordinating with all the top participants in the competition to help create a shared set of resources that others can learn from. I believe, that will also help us remove and disqualify submissions which have categorically cheated in the competition.

Cheers,
Mohanty

PS: For participants with the only objective to cheat, here is the original source of the dataset : And for participants , here is the original source of the dateset : https://archive.ics.uci.edu/ml/datasets/Diabetic+Retinopathy+Debrecen+Data+Set
(Also cited in the actual challenge references section)

View post 2

About the DIBRD category

Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world. Deep Learning has given us tremendous power in the field of computer vision. In some fields of vision ,computers can now see and perceive beyond human capabilities. But with great power comes responsibility. The problem we have for you is to classify the patient’s retina as being diabetic or not diabetic taking into consideration the available image features in the dataset. To know more about diabetic retinopathy click here.

Understand with code! Here is getting started code for you.

Challenge Page : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/

Discussion Forum : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/discussion

leaderboard : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/dibrd-predict-diabetic-retinopathy/leaderboards

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About the NeurIPS 2020: Flatland Challenge category

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About the AZACS category

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About the ADCLK - Predict If An Ad Will Be Clicked category

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View post 0

Column names in the training and testing set

@nishanth_s: Are you using the updated version of the dataset ? It should have just two columns in the training set : filename and xRot. Where filename is the filename of the associated image, and xRot is explained here : https://gitlab.aicrowd.com/aicrowd/practice-challenges/aicrowd_ORIENTME_challenge#-dataset

Cheers,
Mohanty

View post 0

Computational approach

Hi @Johnowhitaker ,

Thats a great observation, and we are very aware of that.
And infact, you could argue that the imputation based approach will work equally well even if you had to predict the rotation angles across three axes.

In any case, this still requires you to understand the notion of a dense “representation” of each of the data points (images of rubiks cube in this case), to be able to interpolate from the training dataset. And we choose a rubiks cube here for a reason : so that participants who are not experienced in AI/ML space can still intuitively try and come up with good representations based on a set of simple rules. The future versions of the task (for the 2nd AIcrowd Blitz scheduled to be launch a week after this one ends) will have arbitrary models which will make that representation learning task a bit more difficult.

But, if you are really into a research-grade-hard-variation () of this problem, you should definitely check the NeurIPS 2019 Disentanglement Challenge that we put together to push the state of art in Representation Learning.

But in any case, the goal of these problems in AIcrowd Blitz are not to come up with the best possible solution which solves the said problem, but actually to introduce a series of problems of varying difficulty levels which encourage a lot more people to engage in this whole space of AI/ML.

While problems like these might seem trivial (after cleverly connecting the initial dots in the problem statement), the simple and key ideas here still continue to be major barriers to entry for hundreds and thousands of potential “research collaborators” who never make the leap to try and understand the basic primitives in our domain, because it always looked so daunting and intimidating. Infact, as soon as the competition ends on May 17th, we have a dedicated 1 week of dissecting all the problems mentioned here as a community - but theres a catch - here the goal would be to try and explain the solutions to all these problems to an absolute beginner, who has no experience in Machine Learning, but happens to be a exceptionally great programmer.

Also, its interesting to highlight the story of this paper : https://arxiv.org/pdf/1505.04467.pdf around Image Captioning. This paper came out a year or so later after the First Image Captioning model made waves, in media, in academic circles, on twitter, everywhere !! This was written by the (almost) same authors, but highlighted a very key idea that being able to impute efficiently on your training set, using ridiculously simple ideas, might help you solve rather (seemingly) complex tasks with just simple ideas !

My personal vision here is to build a community here around AIcrowd Blitz, which acts as a safe space for anyone who is willing to learn about AI/ML while using these problems as encouraging milestones. And I am hoping to gather a LOT MORE experienced researchers to come and patiently mentor the next generation of AI Researchers, irrespective of where they come from, their academic pedigree, their age, or their domain of expertise.

And given your thoughtful inputs and also the participation in this competition, I really think you should definitely come help us make many of these ideas accessible to the many who are still daunted by them

View post 4

[Important] Dataset + Problem Update

Sorry for the confusion. It is indeed a single axis. That is consistently used to generate the whole dataset.

View post 0

Query regarding ORIENTME data

@bobthebuilder: I have posted an update on this problem here : [Important] Dataset + Problem Update

Thanks for all your inputs.
We also took the time to simplify the problem a bit more.
We could have temporarily solved the problem by ensuring that Gimbal Locks are not represented in the training and test sets. But on some initial exploration, we realised that most models would still struggle to learn properly in that loss landscape. Our whole goal to use Euler Angles was to make the notion of orientation intuitive for the participants. And trying to bring in Quaternions now would solve the problems here, but also increase the complexity of the task from a ML pov. So we would leave that for a future version of AIcrowd Blitz. In the meantime, we come up with a simplified problem statement, where the goal is to predict the orientation around an arbitrary axis that we used to generate the training and test set.

Cheers,
Mohanty

View post 1

[Important] Dataset + Problem Update

Hi everyone,

Based on the numerous feedback we received on this problem, we have now updated the problem statement, and the associated dataset to address the said feedback. In this post, we outline the key changes that have been made and the broad reasoning behind the same.

Key Changes

• The new dataset is generated by rotating the Rubik’s cube around an arbitrarily chosen axes. The said axis is consistent throughout the whole dataset.
• In view of the previous change, the yRot, zRot parameters from the training set have been dropped, as the updated problem statement is to just estimate the xRot (rotation in 0-360 degrees) across an arbitrarily chosen axis.
• The new dataset has 5,000 images in the training set, and 5,000 images in the test set.

NOTE: Please download the whole dataset again before making further submissions to this problem.

Both these changes, also simplify the problem statement a lot more, as the final prediction has to be made in a continuous space of [0, 360] instead of [0, 360] x [0, 360] x [0, 360].

The updated problem formulation also deals with the Gimbal Lock issue in the previous training set. The initial idea was to ensure no state close to Gimbal Lock states are represented in the training set and the test set, but clearly, due to an oversight at my end, the error crept up into the final production dataset. Thanks @bobthebuilder for pointing it out!

We hope you guys have fun trying to solve the updated problem statement. And please feel free to reach out to us in case you have any questions or concerns.

Best of Luck,
Mohanty

View post 3

Query regarding ORIENTME data

@bobthebuilder: No, we use the exact same model. In the dataset they are just rendered in openGL, and in the screenshots above, I quickly rendered it in Blender.

And also, the cube doesnt have a “cyan” face, its probably the blue face looking that way while taking into account the lighting and the material.

In any case, we are currently preparing a new version of the dataset. where we also simplify the problem a bit. So please expect an update from us within the next few hours.

View post 2

Query regarding ORIENTME data

@bobthebuilder: I am not sure, if I correctly understand the images you have attached. But cant the L-R positioning of the Blue and White be affected if you are looking at it “from the top” or “from the bottom” of the cube ? Does that explain the images you mention here ?

Also, for generating the data, we use a simple 3d model of a Rubik’s cube. And here are some screenshots by manipulating the camera angles of the rubiks cube to help you get a proper view of the cube from all angles.

Screenshot 2020-05-07 at 12.30.25872×832 402 KB

Screenshot 2020-05-07 at 12.30.411144×1016 213 KB

Screenshot 2020-05-07 at 12.31.011070×962 188 KB

Screenshot 2020-05-07 at 12.31.14886×976 448 KB

Screenshot 2020-05-07 at 12.31.29824×764 367 KB

View post 0

Query regarding ORIENTME data

@bobthebuilder: That is indeed a valid concern raised especially in context of Gimbal lock.

We will post a fix to the data, and an update soon. Thanks for pointing this out, and for your patience.

View post 1

About the ORIENTME category

Solving a rubik’s cube is now too mainstream they say. But what if you have 100,000 pictures of the rubicks cube and you are asked to stitch it all together? This is not a problem we need, this is a problem we deserve!

For input you will be given a large number of images, for about half of them, we have measured the orientation of the cube. But to be able to stitch all those images together, you have to figure out how to predict the orientation of the Rubik's Cube for the rest of the images.

Understand with code! Here is some code to get you started right away !

Challenge Page : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/orientme-estimate-the-orientation-of-a-rubiks-cube

Discussion Forum : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/orientme-estimate-the-orientation-of-a-rubiks-cube/discussion

leaderboard : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/orientme-estimate-the-orientation-of-a-rubiks-cube/leaderboards

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About the PRKNS - Predict Parkinsons Disease-780601 category

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About the PRKNS - Predict Parkinsons Disease-e9fc04 category

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About the PRKNS - Predict Parkinsons Disease category

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About the TartanAir Visual SLAM - Stereo Track category

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About the Temp Challenge category

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About the AIcrowd Blitz - May 2020 category

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About the MINILEAVES category

Can you detect plant diseases just by examining the images of the plant’s leaves? Sounds Tough…… Can you do the same task if the images are at a really low resolution? …Welcome to your new challenge!

In this task participants have to correctly identify a disease-class from low-resolution images of plant-leaves.

Challenge Page: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/

Discussion Forum: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/discussion

Leaderboard: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/minileaves/leaderboards

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About the FOODC category

Maintaining a healthy diet is difficult. As the saying goes, the best way to escape a problem is to solve it. So why not leverage the power of deep learning and computer vision to build the foundation of a semi-automated food tracking application?

With over 9300 hand-annotated images with 61 classes, the challenge is to train accurate models that can look at images of food items and detect the food items present in the image. It’s time to unleash the food (data)scientist in you! Given any image , identify the food item present in it.

Understand with code! Here is getting started code for you. 😄

Challenge Page: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/foodc-recognise-food-items

Discussion Forum: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/foodc-recognise-food-items/discussion

Leaderboard: https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/foodc/leaderboards

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About the Way To Long Chalenge Name - aaaaaaaaaaaaaaaaaaa... category

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About the BHPOB category

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About the WILDS category

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About the SEZUR category

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About the HTSPC- Hate Speech Classification category

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About the TJEDC - Tom and Jerry Emotion Detection Challenge category

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About the COVID - Drug Discovery for COVID19 category

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About the ONPDS category

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About the Columns-Property Annotation (CPA) Challenge category

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About the Cell-Entity Annotation (CEA) Challenge category

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About the Column-Type Annotation (CTA) Challenge category

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About the TartanAir Visual SLAM - Mono Track category

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About the REAL 2020 - Robot open-Ended Autonomous Learning category

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About the Spotify Million Playlist Dataset Challenge category

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About the CRDSM category

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About the pandemic-rl category

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About the OCPDT - Occupancy Detection-254310 category

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About the OCPDT - Occupancy Detection-d5215a category

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About the OCPDT category

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