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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

Is GPU available?

@shivam: Yes, please lets start allocating GPUs for evaluations on this challenge.

View post 0

External datasets

@joao_schapke: Yes you are allowed to use external datasets, pre-trained models, etc.

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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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 - Predict Access To Resources for Employees 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

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

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

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

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

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

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Please send me evaluation error log on gitlab

Can you please reach out to help@aicrowd.com for the same ?

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A post in "Using open-sourced networks" requires staff attention

Thanks for letting us know. We agree there is an issue and we’ve removed the post.

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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 NeurIPS 2020: Procgen Competition 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 PTEMP - Predict Min Temperature category

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About the YOUTB - Predict Transcoding Time category

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About the WAVEC - Predict Wave Energy category

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About the SPRTS - Sports Articles Complexity category

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About the QUERY - Predict Average Beat category

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About the PWRPL - Predict Electrical Energy category

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About the POSTN - Predict The Latitude category

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About the NOISE - Predict Sound Pressure category

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About the MUSIC - Predict Music Origin category

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About the CRIME - Crime Rate Prediction category

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About the WINEQ category

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About the TRVLR - Travel Rating Prediction category

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About the STDEV category

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About the STATL - Statlog Image Segmentation category

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About the SKILL- Predict Gamers League category

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About the SDRVD - Sensorless Drive Diagnosis category

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About the PRTNS - Predict Protein Structure category

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About the POWER category

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About the CHESS category

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About the BLOGF - Predict Blog Comments category

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About the AGEPR - Predict Abalone Age category

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About the tret13 category

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About the CRDIO - Classify Fetal State category

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About the GSTTE - Classify Confusable Digits category

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About the THROD - Detect Thyroid Stages category

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About the SHTTL - Decide Control Type category

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About the MDLON - Classify Clusters category

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About the CARGO - Transport Leg Number category

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About the KDNEY - Predict Kidney Disease category

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About the CONCT - Predict Game Winner category

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About the INTAD - Classify Advertisement Images category

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About the Sensr - Predict State Drive category

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About the FORTP - Predict Fastest Heuristics category

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About the Discourse Category Name That Exists category

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

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About the Short Challenge Name category

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About the BUMPS - Seismic Hazard Prediction category

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About the GAMMA - Gamma Or Hadron category

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About the QSARB - Classify Chemical Degradability category

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About the MSHRM - Mushroom Classification category

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About the WILTS - Tree Wilt Detection category

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About the FRSTT - Recognize Forest Cover Type category

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About the ONSPI - Online Shopping Intention category

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About the PHISH - Identify Phishing Websites category

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About the SPFDS - Recognize Steel Plates Faults category

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About the BNOTE category

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About the CNTMC - Contraceptive Method Used category

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About the BHPOB - Burst Header Packet category

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About the WILDS - Find The Room category

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About the SEZUR - Seizure Detection 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-Social Media Shares Prediction category

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About the SemTab 2020 Columns-Property Annotation (CPA) Chal category

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About the SemTab 2020 Cell-Entity Annotation (CEA) Challenge category

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About the SemTab 2020 Column-Type Annotation (CTA) Challenge category

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About the TartanAir Visual SLAM - Mono Track category

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About the CYD Campus Aircraft Localization Competition category

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About the REAL Robots 2020 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

Note :

This forum is currently in private mode.
The permissions have to be manually changed for this to be visible to the final users.

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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 - Occupancy Detection category

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About the EMSPM - Email Spam Prediction category

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About the DBSRA - Diabetes Readmission Prediction category

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About the DOTAW - Dota 2 Prediction category

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About the DCRCL - Default of Credit Card Clients category

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About the INCPR - Income Prediction category

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About the YPMSD - Music year prediction category

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About the SPCRT - Superconductor critical temperature category

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About the OLNWP category

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About the BKMKT category

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About the PKHND category

Lousy at poker faces? To all the wannabe players who failed at poker, we bring you redemption! Just let your model play your hand and you will master an old foe. We will put 5 cards on your table. Predict the Hand it makes !!

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

Challenge Page : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/pkhnd-poker-hand-recognition

Discussion Forum : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/pkhnd-poker-hand-recognition/discussion

leaderboard : https://www.aicrowd.com/challenges/aicrowd-blitz-may-2020/problems/pkhnd-poker-hand-recognition/leaderboards

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About the MNIST category

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About the QM energy challenge category

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About the Poker AI Challenge category

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About the MICCAI 2020: HECKTOR category

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About the droneRL category

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About the AWS DeepRacer Sim2Real Challenge #2 category

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About the ImageCLEF 2020 VQA-Med - VQG category

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Can I have an example of a code which is working to make a submission on gitlab?

@amapic: If you built the conda env drom the initial environment.yml file, then conda env export --no-build will export the updated state of the environment.

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Can I have an example of a code which is working to make a submission on gitlab?

Dear @ValAn,

Our sincere apologies for the inconveniences faced by you.

Regarding the slow evaluation speeds, given that we have to execute your code (and models etc) on a large number of test images, the evaluations are indeed slow. Your model has to make predictions for a large number of images. We are trying to improve this experience by providing better feedback in terms of progress etc, and will definitely address this in the upcoming version of the challenges.

Regarding the competition, we are providing all updates on this forum here, and we would be happy to answer any and all questions you have here. We are also working on better notification systems so that you get relevant updates from the challenge over emails and other notification channels on the platform that you subscribe to.

In the meantime, we really appreciate your feedback. Your feedback helps us make the platform much better for thousands of other users on the platform, and under no circumstances we take it as an offense.

Thank You,
Mohanty
(on behalf of the organizing team)

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About the ImageCLEF 2020 Caption - Concept Detection category

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About the ImageCLEF 2020 VQA-Med - VQA category

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About the ImageCLEF 2020 DrawnUI category

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About the ImageCLEF 2020 Coral - Pixel-wise parsing category

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About the ImageCLEF 2020 Coral - Annotation and Localisation category

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About the ImageCLEF 2020 Lifelog - SPLL category

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About the ImageCLEF 2020 Lifelog - LMRT category

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About the LifeCLEF 2020 Snake category

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About the LifeCLEF 2020 Plant category

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About the LifeCLEF 2020 Geo category

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About the LifeCLEF 2020 Bird - Monophone category

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About the LifeCLEF 2020 Bird - Stereo category

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Submission issue

@eli_meirom: The git remote add step should be :

git remote add aicrowd git@gitlab.aicrowd.com:<YOUR_AICROWD_USER_NAME>/flatland-challenge-starter-kit.git

Note the lack of / after the git@gitlab.aicrowd.com

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Submissions counting

@vitaly_bondar: Yes, failed submissions are indeed counted towards the daily submission limit.
And the submission limits are enforced in the “last 24 hours” period ( moving window of 24 hours), and hence are agnostic to the timezone you are in.

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Evaluation server hardware specifications

@sungbinchoi: Agree. We would be happy to provide GPUs if you make a case for the same. Please reach out to us over email, and share any code which requires GPU for evaluation, and then we should be able to make GPUs available for valid requests.

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[ANNOUNCEMENT] Start Round 2

Update

We are currently working on a few more bug fixes, and performance and stability related issues with flatland. These fixes will be made available as another patch to the current latest release of 2.1.6.

Much of the delay in reliably accepting submissions has been due to tuning the complexity of the test environments on which your submissions will be evaluated. Please expect a further update from us soon.

But please be assured that no key changes in the features or the environment interfaces will be introduced at this stage, hence you can reliably continue experimenting with the flatland library at your end before we start accepting submissions again.

And our apologies for not being more communicative about the updates and announcements related to the competition. But rest assured, the whole team is working really hard to ensure you all can have a great experience taking part in the competition.

Thanks,
Mohanty
(on behalf of the organizing team)

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Local evaluator on Windows not working

I have a feeling that the pickle files you are using are corrupt somehow Or the dataset directory paths are not properly set.

Are you correctly specifying the /path/to/test-env-data/ ?

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Test case 532 evaluation problem

@ryznefil: Look at your score on the leaderboard, looks like you managed to get past that test case ! Anything you would want to share with the rest of the participants ? !

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How is F1 score calculated?

@carlos.cortes: Yes, F1 Score computation works around a 0.5 threshold of the probability of approval.

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How to use conda-forge or CRAN for packages in evaluation?

@bjoern.holzhauer: We are on it. We will send you a working version with tidyverse soon. Thanks for your patience.

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Test Post Please Ignore

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Test Post Please Ignore

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Test Post Please Ignore

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Test Post Please Ignore

Test Post. Please Ignore. Test Post. Please Ignore.

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About the ImageCLEF 2020 Security - Forged File Discovery category

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About the ImageCLEF 2020 Tuberculosis - Severity Scoring category

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About the AMLD 2020 - Transfer Learning for International Crisis Response

AMLD 2020 - Transfer Learning for International Crisis Response

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About the AMLD 2020 - D'Avatar Challenge category

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Environment variables

@kashin: That is an artifact left from the transition from crowdAI to AIcrowd. And on all evaluations on AIcrowd, we set both the variables CROWDAI_IS_GRADING and AICROWD_IS_GRADING to True.

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Test Topic for demo

Test Topic

This is a test topic.
Please have a look at example_evaluator.py for a sample implementation.

Steps

• Installation

git clone https://github.com/AIcrowd/aicrowd-example-evaluator
cd aicrowd-example-evaluator
pip install -r requirements.txt

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Novartis Pharma Data Science Hackathon category

A discussion forum for the Novartis Hackathon Participants.

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About the EPFL ML Recommender System 2019 category

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About the EPFL ML Road Segmentation 2019 category

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About the EPFL ML Text Classification 2019 category

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About the EPFL Machine Learning Higgs 2019 category

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[Announcement] Start of Round-2

@amirabdi: The current computation actually chooses the best submission for you automatically. I havent closely looked at how well they agree with the nominated ones, but if they do not match, your rank would only go down

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[Announcement] Start of Round-2

@amirabdi: Yes, the ranking algorithm used is the same one as discussed here

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[Announcement] Start of Round-2

Dear @all,

Confirming that we are accepting submissions now for Round-2.
The leaderboard for the new submissions is available here

Cheers,
Mohanty

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The competition is halted?

@jozerozero @amirabdi: Sorry for the delay. Please refer to this post by Stefan for more details : [Announcement] Start of Round-2

The summary is that we are starting to accept submissions for Round-2 starting 20:00 CET today (September 2nd)

Best of Luck.

Cheers,
Mohanty

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