Welcome to the Multi-Agent Behavior Challenge
Please read below to see how you can claim AWS credits for yourself or for your team
- You (or your team) must have received scores higher than the below-mentioned scores in any of the 3 tasks.
- Each participant (or team) is eligible for a maximum of 2 redeemable codes.
- We will be sharing 50 X $200 = $10,000 worth of AWS Credits. The distribution will happen on a first come first serve basis and will cease once all the 50 codes (each for $200) are distributed.
- The codes are valid till 12/31/2021 so, we would nudge you to use them for this competition itself!
- For teams claiming the code, please nominate one person to represent. The code will be mailed to that team member.
How to redeem?
- Please share the following as a reply to this thread to receive your code.
Team name (if relevant):
Submission id (the one with at least 5% improvement over any of the baselines):
How much did you improve over the relevant baseline score?:
A brief intro about you (Us and the participants would love to know what brought you to this challenge):
We will be sending out the AWS credits every alternative day after verifying the details. After receiving your code, you can go to this website and claim your credits.
If you haven’t gone through the baselines, check them out here
claiming credits for Classification task submission_id=127051 F1score=.814
Team name: xiren_zhou (it’s my username; haven’t formed a team so far)
Submission id: 126983
How much did you improve over the relevant baseline score?: I got F1 score of 0.809 on task1.
A brief intro about you:
I’m a deep learning engineer in industrial field.
Team name: rssfete
Submission id: 126596
F1 score: 0.802
Engineering students. This seemed like an interesting problem, got an email from AIcrowd about it.
Team name: alatau (user: dna1289)
Submission id: ‘submission_id’: 127311
How much did you improve over the relevant baseline score?: Classification F1= 0.806
A brief intro about you: A Data Science Enthusiast currently learning Computer Vision. The competition problem and the data appears to be unique and interesting. The baseline code provided by organizers gives a good and smooth introduction to the problem. The restriction on usage of external data makes the problem manageable within available technical resources (e.g. google colab).
Claiming credits for submission with ID: 127500 F1 score: 0.810
I’m a data scientist working in behavior classification in animals. This is very relevant!
Team name: sungbinchoi
Submission id: 128954
F1 score on Task 1: 0.848
A brief intro about you: PhD. Deep learning enthusiast
Submission id: 131794 [Task1] and 131795 [Task 2]
How much did you improve over the relevant baseline score?: F1 scores 0.888 [Task1] and 0.861 [Task2]
A brief intro about you: PhD student using machine learning to study collective behavior.