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Pytorch implementation of the U-Net for image semantic segmentation, with dense CRF post-processing
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milesial Fixed major data loading bug : #46 from pierrezeb/patch-1
Fix split_ids (returned only (id, 0))

The network is now training on both squares and not only the left part of the image
Latest commit e2e46ce Feb 7, 2019


input and output for a random image in the test dataset

Customized implementation of the U-Net in Pytorch for Kaggle's Carvana Image Masking Challenge from a high definition image. This was used with only one output class but it can be scaled easily.

This model was trained from scratch with 5000 images (no data augmentation) and scored a dice coefficient of 0.988423 (511 out of 735) on over 100k test images. This score is not quite good but could be improved with more training, data augmentation, fine tuning, playing with CRF post-processing, and applying more weights on the edges of the masks.

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 : Use Python 3


You can easily test the output masks on your images via the CLI.

To see all options: python -h

To predict a single image and save it:

python -i image.jpg -o output.jpg

To predict a multiple images and show them without saving them:

python -i image1.jpg image2.jpg --viz --no-save

You can use the cpu-only version with --cpu.

You can specify which model file to use with --model MODEL.pth.


python -h should get you started. A proper CLI is yet to be added.


In order to process the image, it is split into two squares (a left on and a right one), and each square is passed into the net. The two square masks are then merged again to produce the final image. As a consequence, the height of the image must be strictly superior than half the width. Make sure the width is even too.


This package depends on pydensecrf, available via pip install.

Notes on memory

The model has be trained from scratch on a GTX970M 3GB. Predicting images of 1918*1280 takes 1.5GB of memory. Training takes approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays. This assumes you use bilinear up-sampling, and not transposed convolution in the model.

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