Kaggle

Photo Credit Overview Preamble I started doing this competition (iMet Collection 2019 - FGVC6) seriously after hitting a wall doing the Freesound competition. It was really late (only about one week until the competition ends), but by re-using a lot of code from the Freesound competition and using Kaggle Kernels to train models, I managed to get a decent submission with F2 score of 0.622 on the private leaderboard (the top 1 solution got 0.672, but used a hell lot more resources to train). ...

Photo Credit Introduction Jigsaw toxic comment classification challenge features a multi-label text classification problem with a highly imbalanced dataset. The test set used originally was revealed to be already public on the Internet, so a new dataset was released mid-competition, and the evaluation metric was changed from Log Loss to AUC. I tried a few ideas after building up my PyTorch pipeline but did not find any innovative approach that looks promising. Text normalization is the only strategy I had found to give solid improvements, but it is very time consuming. The final result (105th place/about top 3%) was quite fitting IMO given the time I spent on this competition(not a lot). ...