In the likes of competitiveness, it will be good to give more insight into how to improve the model for those below 0.4 scores on the leaderboard. The major challenges is you need to iterate fast and try different methods, and if you are you are using colab, then you and I know that colab is slow.
https://github.com/steveoni/ICLR_models contain some models I used to experiment on some basic stuff.
And to see more tricks that you can try before using ensembles check out this post : https://medium.com/@steveoni/tricks-for-improving-your-image-classification-model-cd1f588602ba
Following this trick, I was able to have a single model that gave me my highest score of 0.24 on the leaderboard, using Mixup data augmentation.
And to see how to use zindi dataset directly on colab, check out the discussion forum for the topic on that.
You are a champion. Thanks
thanks for this, but it's think this is coming late 5 days to goto share such score might not be really that fair. please always make it quicker on next competition
anyone who found it's notebook useful should give credit by star the notebook
Likely your loss figures are impacted by duplicates in the train set that you have in your valid set.
yeah, i wasn't sure of the best way, i wanted to use scikit learn version before.
Thanks. tho am not submitting again
also set the random seed so you get the same examples in validation set across experiments
Yeah that's true
Please if some of these tricks works, Please do well to let me know, especially that of using Mixup for data augmentation, and is Test Time Augmentation, actually increasing the score
Thank you for sharing.However i think sharing such a huge score towards the end of a competition is evil to the community.Make them quicker in future competitions.cheers!