Hey Kagglers,
\nWe’re happy to announce the conclusion of the BirdCLEF 2021 - Birdcall Identification competition! This is our second year working with the team (click here for last year’s competition), and we’re pleased to see how your expertise has grown since then.
\nThis year’s competition ended with 1,004 participants on 816 teams! We had 9,307 submissions from 70 countries! For 111 users (including 4 users on top 100 teams!), this was their first competition. We also had 1 new Grandmaster and 11 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nAs said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums, and are incredibly knowledgeable about the state-of-the-art in the industry, while also soaking in all of the great content you have generated in this competition. We're glad they continue to choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future.
\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.
\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact compliance. Please fill in all the fields honestly.
\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.
\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.
\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
Hey Kagglers,
\nWe’re happy to announce the conclusion of the BirdCLEF 2021 - Birdcall Identification competition! This is our second year working with the team (click here for last year’s competition), and we’re pleased to see how your expertise has grown since then.
\nThis year’s competition ended with 1,004 participants on 816 teams! We had 9,307 submissions from 70 countries! For 111 users (including 4 users on top 100 teams!), this was their first competition. We also had 1 new Grandmaster and 11 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nAs said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums, and are incredibly knowledgeable about the state-of-the-art in the industry, while also soaking in all of the great content you have generated in this competition. We're glad they continue to choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future.
\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.
\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact compliance. Please fill in all the fields honestly.
\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.
\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.
\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
Nice Work!!! Thanks for the effort!!!
", "rawMarkdown": "Nice Work!!! Thanks for the effort!!!", "votes": null }, { "id": "1335592", "postDate": "06/04/2021 09:49:36", "content": "\n\nAs said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums
\n
This is true, actually. Many orgs forget about ongoing competitions, but in this case we had great notebooks (for example, with EDA) and responses on the forum. Wish more orgs were like this 💪
", "rawMarkdown": "> As said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums\n\nThis is true, actually. Many orgs forget about ongoing competitions, but in this case we had great notebooks (for example, with EDA) and responses on the forum. Wish more orgs were like this 💪", "votes": null } ], "comments": [ { "id": 1333949, "author_name": "wubinbai", "author_url": "", "post_date": "06/03/2021 07:07:26", "content": "Nice Work!!! Thanks for the effort!!!
", "votes": null, "replies": [] }, { "id": 1335592, "author_name": "ivanpan", "author_url": "", "post_date": "06/04/2021 09:49:36", "content": "\n\nAs said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums
\n
This is true, actually. Many orgs forget about ongoing competitions, but in this case we had great notebooks (for example, with EDA) and responses on the forum. Wish more orgs were like this 💪
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1333923": "Hey Kagglers,\n\nWe’re happy to announce the conclusion of the BirdCLEF 2021 - Birdcall Identification competition! This is our second year working with the team (click [here](https://www.kaggle.com/c/birdsong-recognition) for last year’s competition), and we’re pleased to see how your expertise has grown since then.\n\nThis year’s competition ended with 1,004 participants on 816 teams! We had 9,307 submissions from 70 countries! For 111 users (including 4 users on top 100 teams!), this was their first competition. We also had 1 new Grandmaster and 11 new Masters at the conclusion of the competition! Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking! \n\nAs said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google ([Holger](https://www.kaggle.com/holgerklinck), [Stefan](https://www.kaggle.com/stefankahl), [Tom](https://www.kaggle.com/tomdenton)) for this competition. They’ve been great at engaging with you Kagglers in the forums, and are incredibly knowledgeable about the state-of-the-art in the industry, while also soaking in all of the great content you have generated in this competition. We're glad they continue to choose Kaggle to be a platform to bring these types of problems to the broader data science community. They've been a pleasure to work with, and we hope to work with them again in the future. \n\nWe're also continually pleased to see how Kaggle Competitions serve as a great medium and vehicle through which the greater data science community can learn and develop their machine learning abilities and skills. Beginners and experts can come together to start, grow, and succeed. Further, in this competition, many can use their machine learning skills to help a great cause like this one.\n\nWe've cleaned the leaderboard and disqualified some teams that have violated the rules. If you think you were removed by mistake, or believe you have evidence that suggests another team cheated, please contact [compliance](https://www.kaggle.com/compliance). Please fill in all the fields honestly.\n\nThe top potential winning teams have been contacted via email to provide their winning solutions for host review. Should we need to move down the leaderboard for any reason, we will do so and continue to make contact with subsequent teams.\n\nYou should have already seen your points and medals awarded, however it may take a few hours to propagate through if yours are still missing.\n\nIn the meantime, I've created this thread to be a curated list of your work. As you post your solutions, top kernels, and general discussion posts, I'll update this thread to keep track. If you write a paper, thesis, or present at a conference, and would like to share your work, please let us know so we can share with the Kaggle community! Additionally, if you performed any data manipulation, or used a new technique in this competition that you'd like to share to further the industry, please let us know and we'll post it here! That way, should you be looking for takeaways in the future, or if you've just now stumbled across the competition, you have a place to see what surfaced from the work performed.\n\nHappy Modeling!\nKaggle Team\n\n###Top Solution Writeups\n\n- [1st Place](https://www.kaggle.com/c/birdclef-2021/discussion/243304)\n- [2nd Place](https://www.kaggle.com/c/birdclef-2021/discussion/243463)\n- [3rd Place](https://www.kaggle.com/c/birdclef-2021/discussion/245708)\n- [4th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243293)\n- [5th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243351)\n- [9th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243324)\n- [11th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243360)\n- [18th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243343)\n- [22nd Place](https://www.kaggle.com/c/birdclef-2021/discussion/243349)\n- [39th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243356)\n- [42nd Place](https://www.kaggle.com/c/birdclef-2021/discussion/243312)\n- [47th Place](https://www.kaggle.com/c/birdclef-2021/discussion/243332)\n\n\n###Top Notebooks\n- [Clean Fast & Simple Bird Identifier (inference)](https://www.kaggle.com/kneroma/clean-fast-simple-bird-identifier-inference)\n- [[Pytorch, Training] BirdCLEF 2021 Starter](https://www.kaggle.com/hidehisaarai1213/pytorch-training-birdclef2021-starter)\n- [BirdCLEF 2021 Starter](https://www.kaggle.com/drcapa/birdclef-2021-starter)\n- [BirdCLEF Mels Computer [Public]](https://www.kaggle.com/kneroma/birdclef-mels-computer-public)\n- [BirdCLEF 2021 Exploring the Data](https://www.kaggle.com/stefankahl/birdclef2021-exploring-the-data)\n\n###Other Top Discussions\n- [Where to Start? A Collection of Resources](https://www.kaggle.com/c/birdclef-2021/discussion/230000)\n- [Read and study past competition solutions](https://www.kaggle.com/c/birdclef-2021/discussion/234464)\n- [Fast training and precomputed melspec images](https://www.kaggle.com/c/birdclef-2021/discussion/234154)\n- [Starter and some thoughts](https://www.kaggle.com/c/birdclef-2021/discussion/230539)", "1333949": "Nice Work!!! Thanks for the effort!!!", "1335592": "> As said before, we were once again very pleased to work with the teams at Cornell and the Bioacoustics team at Google (Holger, Stefan, Tom) for this competition. They’ve been great at engaging with you Kagglers in the forums\n\nThis is true, actually. Many orgs forget about ongoing competitions, but in this case we had great notebooks (for example, with EDA) and responses on the forum. Wish more orgs were like this 💪" }, "source": "meta" }