Round 2 of Food Recognition Benchmark has ended !!
This round saw a lot more activity and a lot more rank swaps on the leaderboard.
Thanks to you all for making it a success.
While a few of submissions are being re-evaluated at the moment, the public leaderboard looks like this right now:
The number of participants who crossed the official baseline has increased from 12 to 15.
At the same time, participants having score >= 0.200 AP is increased from 6 to 11 as well!
We can’t wait to complete the due diligence before making the winners’ announcement.
Stay tuned for it!
Why Food Recognition Benchmark matters?
It’s a good time to re-share about exciting updates on how the community collaboration has worked so far and what it’s enabling! [Blog Post]
The Food Recognition Benchmark is not a competition, but an ongoing collaboration with a community of inspiring individuals like yourself, to deliver the future of diet tracking in research cohorts around the world.
What can you do meanwhile?
Start some interesting discussions with other challenge participants’ here on the Forums or on Discord Chat.
Share what worked and what didn’t during the competition. And maybe some interesting approaches you may want to try out in future?
Share any feedbacks you have for future Rounds either in this thread of here.
Guess the winners in this thread
Great work guys.
Thank you @mohanty, @shivam for making this challenge happen.
Thanks everyone on the leaderboard, it was really fun this time. In the previous week it felt like a roller coaster ride with all the score shuffling. I expect the final leaderboard will see a very small variance in the score and the results don’t shuffle by a huge gap.
Congratulations to @team_zi, and great work @saidinesh_pola, @nivedita_rufus and @unnikrishnan.r
Thanks for hosting this challenge. Will you be releasing the winners source code? Would love to learn how I can improve my model.
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!
Thanks to Aicrowd for hosting this yearly competition and benchmark. It was a lot of fun working on it, exploring and learning models for instance segmentation for solving the task on food recognition.
Thanks for my teammates for this work.
Thanks @shivam for a lot of helping us with aicrowd infrastructure, @gaurav_singhal for your paper on your previous year best approach and unbelievable race on score other participants for good time within this competition!
Also, congratulations to @gaurav_singhal and @Camaro!!!
Thanks, @Mykola_Lavreniuk. I hope our techniques are different from each other to broaden our knowledge horizons. I would love to collaborate with you and your team if I will be invited to co-author.
Congratulation to @Lab_zi and @Camaro
Thanks, looking forward to the source code being posted!