📝 Announcing Community Contribution Prizes 🚁🥽

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🧑‍💻 Baselines Published!

Dear Participants,

The Leaderboards for all 3 tasks are seriously heating up As we gear up for code-based submissions, we’re excited to share that the baselines for all 3 tasks are now published!

Get Started With The Baselines Here

Key Details

• For Task 1, we fine-tuned 3 models one for each query_locale.
  • For us locacale we fine-tuned MS MARCO Cross-Encoders. For es and jp locales multilingual MPNet. We used the query and title of the product as input for these models.
• For Task 2, we trained a Multilayer perceptron (MLP) classifier whose input is the concatenation of the representations provided by BERT multilingual base for the query and title of the product.
• For Task 3, we followed the same approach as in task 2.

The following table shows the baseline results obtained through the different public tests of the three tasks.

Remember, you can get all creative with the baselines now, and share your own versions with the community - To win Drones, VR Sets, and bragging rights!

Community Contribution Prize1920×1005 80.5 KB

Not just that, you can share explainers/videos/articles/contribute to discussions - anything that will help the community!

All the best!
Team AIcrowd

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Downloading data via AIcrowd CLI

Hi @HAGE2000,

Thank you so much for sharing this. We have updated the commands on the resources page with your feedback.

Regards

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📝 Announcing Community Contribution Prizes 🚁🥽

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👋 Welcome to Review Classification Puzzle!

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End of Round 2⏱️

@lapp09, the source codes are now public: 💡 Solutions are Public

Do share your insights on their approaches with the community!

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💡 Solutions are Public

Dear Participants!

The solutions from the three leaderboard winners are now public .

Check them out below

	Lab_3I	Solution
	gaurav_singhal	Solution
	Camaro	Solution

Did you find some interesting approaches in their solutions? Please share them with the community below!

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🎉 Winners of Round 2

Dear Participants,

As the Round 2 of the Food Recognition Benchmark comes to a close, we are excited to reveal the final winners!

Frame 21920×768 53.9 KB

This is a tough benchmark. With each successive iteration, you all have pushed the limits of what’s possible in solving this problem. We thank each one of you for your continued participation.

Check out the Final leaderboard

Stay tuned for what’s next for the benchmark!

Cheers,
Team AIcrowd

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End of Round 2⏱️

Hi, yes.

As per the challenge rules:

We look forward to the top solutions being shared with the community - you should hear more on this in further winner announcements!

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⏰ Round 1 Extended to 28th Feb

Dear Participants,

The deadline for Round 1 of Food Recognition Benchmark is now extended to 28th February, 2022, 23:59 UTC

We introduced some exciting new resources for the benchmark (1 | 2), and have posted an update on the timeout conditions as well!

Please let us know if you have any feedback.

All the best!
Team AIcrowd

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💡 Winners' Solutions Available Now

Dear Participants,

We have some exciting news! The solutions from the three leaderboard winners are now public.

Check them out below

	xiaozhou_wang	Solution
	sergey_zlobin	Solution
	ArtemVoronov	Solution

Did you find some interesting approaches in their solutions? Please share them with the community below!

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🎉 Community Contribution Prize Winners

Award Post1920×1005 81.2 KB

Congratulations @santiactis @gaurav_singhal @leocd @sagar_rathod @moto!

Your contributions throughout the duration of the challenge have been truly helpful to the community, and inspiring for everyone!

Here are some quick links to the top contributions by the winners:

[Analysis + Utils] Labels co-occurence & Image Similarity	by @santiactis
[Utils] Round 2 Final Model compilation	by @santiactis
[Explainer + Baseline] Get your Baseline right! (+0.84 LB)	by @santiactis
Create your baseline with 0.4+ on LB (Git Repo and Video)	by @gaurav_singhal
[First Baseline + Explainer] Getting Started With ROUND 2	by @gaurav_singhal
WANDB - Build better models faster with experiment tracking	by @gaurav_singhal
Apply the right Normalisation transformation for your data	by @gaurav_singhal
A New Baseline With 0.71 accuracy on LB	by @gaurav_singhal
Simple Way to Detect Noisy Label with opencv	by @leocd
[LB 0.880] My Experiment Results + Baseline too I guess	by @leocd
Ways to Select Which Data to Purchase - Episode 1	by @leocd
Exploration, Body Paint Color Dominance, Image Transforms	by @leocd
Sneak Peek into the image samples from Round 2 dataset	by @sagar_rathod
Purchase with anomaly detection	by @moto
Best baseline in round 1 - score 0.885	by @moto
Color normalization	by @moto
Baseline + Exploration: random purchase vs full purchase	by @moto

Cheers,
Team AIcrowd!

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About the CVPR 2022 CLEAR Challenge category

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👋 Welcome to the Lip Reading Puzzle

Hey there,

This forum is your one-stop solution for troubleshooting any query related to the Lip Reading Puzzle!

Get started with the puzzle
Explore the dataset
Hints and resources

Learn along with the community!

Share your approaches to the puzzle in the thread below.

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🎉 Congratulations to the Winners!

Dear All,

The Learn to Race Challenge saw trailblazing solutions from the AIcrowd community. More than 400 Participants from 58+ Countries competed in the challenge and raced their agents!

We thank every one of you for making the challenge a success.

We have completed the due diligence on the winning submissions and are excited to announce the final winners of the challenge!

Award Post Final1920×1005 87.9 KB

@lachlan_mares @fheinrich @shivansh_beohar @Werner_Duvaud @matthew_howe @any_name!

Quick explainer on the ranking
Some participants submitted to both the Single camera and Multi camera leaderboards. Participants cannot be awarded for multiple leaderboards, so, in the event the participant ranks at the top of both leaderboards, we skip them in the rankings for their lower-ranking leaderboard and move on to the next participant for that leaderboard.

Congratulate the winners, and share your insights and thoughts on the challenge in the thread below!

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[Round 2] Launch - Expected Date

Dear all,

Thank you for your continued participation in the Learn to Race Challenge.

As the Top 10 teams from Round 1 move to Round 2, we wanted to share an update on the challenge timeline and prize details.

• We are readying the setup for Round 2, where your agents will be evaluated on an unseen track, and expect to have it ready before the end of this week.
• The end date of the second round will also be updated based on the launch date.
• The teams/participants that move to Round 2 will also receive training credits. We expect to deliver these by tomorrow.

Cheers!

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Phishing email from aicrowd?

Hi @test_2023,

I can confirm that the mail is official and not a phishing email. We are in the process of dispatching your AWS credits and you should receive your coupon codes for that shortly. (Round 2 of the challenge will also go live soon!)

As for the Passport requirement, tagging @ashivani who can clarify it.

Though, this is good feedback for us to explain the document requirements in a better way, and not be considered phishing mail.

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About the Multi Agent Behavior Challenge 2022 category

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How do I check the Audioshake submission on my own files?

Hi @vitaly_shukela, welcome to AIcrowd!

You are correct - the Audioshake team chose to keep their solution closed-source.

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About the AIcrowd Blitz ⚡ category

(Replace this first paragraph with a brief description of your new category. This guidance will appear in the category selection area, so try to keep it below 200 characters.)

Use the following paragraphs for a longer description, or to establish category guidelines or rules:

• Why should people use this category? What is it for?

• How exactly is this different than the other categories we already have?

• What should topics in this category generally contain?

• Do we need this category? Can we merge with another category, or subcategory?

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