Hey Kagglers,
\n\nThe Peking University/Baidu Autonomous Driving competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this interesting problem. Last year’s CVPR WAD Challenge (focused on video segmentation) had 141 teams. This year had over 1,100 participants on 866 teams. We had 10,000 submissions from 62 countries! For 247 users (including 6 in the top 10!), this was their first competition. We also had 1 new Grandmaster and 3 new Masters at the conclusion of this 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\nWe were excited to work with all the folks at Peking University and Baidu for this competition once again, like Ruigang, Dingfu an Zhuo. We're glad they 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 further research in a burgeoning industry 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. 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!
\n\nKaggle Team
\n\nHey Kagglers,
\n\nThe Peking University/Baidu Autonomous Driving competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this interesting problem. Last year’s CVPR WAD Challenge (focused on video segmentation) had 141 teams. This year had over 1,100 participants on 866 teams. We had 10,000 submissions from 62 countries! For 247 users (including 6 in the top 10!), this was their first competition. We also had 1 new Grandmaster and 3 new Masters at the conclusion of this 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\nWe were excited to work with all the folks at Peking University and Baidu for this competition once again, like Ruigang, Dingfu an Zhuo. We're glad they 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 further research in a burgeoning industry 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. 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!
\n\nKaggle Team
\n\nAwesome first competition for me, learned a lot! Thanks for hosting it!
", "rawMarkdown": "Awesome first competition for me, learned a lot! Thanks for hosting it!", "votes": null }, { "id": "726234", "postDate": "01/23/2020 00:08:30", "content": "It's my first DL project. I've learned so much from other competitors, many thanks!
", "rawMarkdown": "It's my first DL project. I've learned so much from other competitors, many thanks!", "votes": null }, { "id": "748314", "postDate": "02/17/2020 11:20:19", "content": "me too. I learned so much from those competitors, thanks a lot
", "rawMarkdown": "me too. I learned so much from those competitors, thanks a lot", "votes": null } ], "comments": [ { "id": 726182, "author_name": "greatgamedota", "author_url": "", "post_date": "01/22/2020 23:00:08", "content": "Awesome first competition for me, learned a lot! Thanks for hosting it!
", "votes": null, "replies": [] }, { "id": 726234, "author_name": "yuanlin08", "author_url": "", "post_date": "01/23/2020 00:08:30", "content": "It's my first DL project. I've learned so much from other competitors, many thanks!
", "votes": null, "replies": [] }, { "id": 748314, "author_name": "superming", "author_url": "", "post_date": "02/17/2020 11:20:19", "content": "me too. I learned so much from those competitors, thanks a lot
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "726180": "Hey Kagglers,\n\nThe Peking University/Baidu Autonomous Driving competition has closed! We hope you were able to learn a lot, and use your machine learning skills on this interesting problem. Last year’s [CVPR WAD Challenge](https://www.kaggle.com/c/cvpr-2018-autonomous-driving) (focused on video segmentation) had 141 teams. This year had over 1,100 participants on 866 teams. We had 10,000 submissions from 62 countries! For 247 users (including 6 in the top 10!), this was their first competition. We also had 1 new Grandmaster and 3 new Masters at the conclusion of this 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\nWe were excited to work with all the folks at Peking University and Baidu for this competition once again, like Ruigang, Dingfu an Zhuo. We're glad they 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 further research in a burgeoning industry 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!\n\nKaggle Team\n\n###Top Solutions\n- [1st Place Solution](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127037)\n- [2nd Place Solution](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127099)\n- [5th Place Solution part 1](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127065)\n- [5th Place Solution part 2](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127145)\n- [7th Place Solution part 1](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127034)\n- [7th Place Solution part 2](https://www.kaggle.com/c/pku-autonomous-driving/discussion/127056)\n###Top Notebooks\n- [CenterNet Baseline ](https://www.kaggle.com/hocop1/centernet-baseline)\n- [A Quick and Simple EDA](https://www.kaggle.com/phunghieu/a-quick-simple-eda)\n- [Introduction and Data Review](https://www.kaggle.com/robikscube/autonomous-driving-introduction-data-review)\n- [Augmented Data Reality](https://www.kaggle.com/ebouteillon/augmented-reality)\n- [CenterNet Objects as Points](https://www.kaggle.com/diegojohnson/centernet-objects-as-points)\n\n\n\n###Other Top Discussions\n- [Useful Papers, Blogs, References](https://www.kaggle.com/c/pku-autonomous-driving/discussion/113895)\n- [Algorithm Selection for Beginners](https://www.kaggle.com/c/pku-autonomous-driving/discussion/120015)\n- [Understand Camera Intrinsic Parameters](https://www.kaggle.com/c/pku-autonomous-driving/discussion/120083)\n- [Baidu Apollo](https://www.kaggle.com/c/pku-autonomous-driving/discussion/120076)", "726182": "Awesome first competition for me, learned a lot! Thanks for hosting it!", "726234": "It's my first DL project. I've learned so much from other competitors, many thanks!", "748314": "me too. I learned so much from those competitors, thanks a lot" }, "source": "meta" }