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Building Missing Maps with Machine Learning
By Humanity & Inclusion
about 2 years ago
We have experimented a bit more with our codebase and managed to improve the score significantly and got to 0.934 which would have gotten us top1 in stage1.
As of now there is no option to make late submitions so we could only validate on the local validation set. It seems however that the score on validation is pretty much exactly what you get on test.
Anyhow things that made a difference:
use resize at training and inference instead of padding.
For me this is very surprising and I still don’t understand what is going on with that.
use Renset101 as the encoder
1. train with larger learning rate with Adam on a subset of 50k images from train (to benefit from lr adjustments)
2. train for a few epochs on the entire 230k dataset
3. decrease the learning by a factor of 10 and train for another few epochs
4. increase the weight on soft dice loss 10x and train for a few more epochs
We will have a clean write-up of our training procedure on the repo soon
This gets you to 0.926 (0.928 with TTA)
Anyhow the best model is still training so it could improve on that still.
We are working on cleaning up the repo and dockerization and should have it ready soon.
The second big topic is how well can this model generalize. We made some quick tests and it seems that the competition dataset consist on images so similar that generalization will can be a really big problem.
Our strategy to combat those problems will probably involve retraining the model on strongly augmented images (scale, color, blur) combined with multiscale/multcolor TTA.
For those that don’t already know all the code, issues, feature request, tickets and more is freely available in our repo https://github.com/minerva-ml/open-solution-mapping-challenge .
Good luck all!