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.gitignore Created a basic train loop + changed a bit loss and utils 2017-08-17 21:16:19 +02:00
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data_vis.py Created a basic train loop + changed a bit loss and utils 2017-08-17 21:16:19 +02:00
eval.py Added simple predict + submit script 2017-08-21 18:00:07 +02:00
load.py Added simple predict + submit script 2017-08-21 18:00:07 +02:00
main.py Added simple eval and test CRF 2017-08-19 10:59:51 +02:00
MODEL.pth.REMOVED.git-id Added simple trained model 2017-08-23 17:38:55 +02:00
myloss.py Created a basic train loop + changed a bit loss and utils 2017-08-17 21:16:19 +02:00
predict.py Added simple predict + submit script 2017-08-21 18:00:07 +02:00
README.md Updated README.md 2017-11-30 03:44:29 +01:00
submit.py Final tweaks 2017-09-26 21:00:51 +02:00
train.py Added simple trained model 2017-08-23 17:38:55 +02:00
unet_model.py Created a basic train loop + changed a bit loss and utils 2017-08-17 21:16:19 +02:00
unet_parts.py Added simple eval and test CRF 2017-08-19 10:59:51 +02:00
utils.py Final tweaks 2017-09-26 21:00:51 +02:00

README.md

Pytorch-UNet

Customized implementation of the U-Net in Pytorch for Kaggle's Carvana Image Masking Challenge.

This model scored a dice coefficient of 0.988423 (511 out of 735), which is bad but could be improved with more training, data augmentation, fine tuning, and playing with CRF post-processing.

The model used for the last submission is stored in the MODEL.pth file, if you wish to play with it. The data is available on the Kaggle website.

Note

The code and the overall project architecture is a big mess for now, as I left it abandoned when the challenge finished. I will clean it SoonTM.