1.5 KiB
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.
Usage
### Prediction
You can easily test the output masks on your images via the CLI.
To see all options:
python predict.py -h
To predict a single image and save it:
python predict.py -i image.jpg -o ouput.jpg
To predict a multiple images and show them without saving them:
python predict.py -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.
Warning
In order to process the image, it is splitted 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.