Customized implementation of the U-Net in PyTorch for Kaggle's Carvana Image Masking Challenge from high definition images.
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 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 Carvana data is available on the Kaggle website.
Note : Use Python 3.6 or newer
After training your model and saving it to MODEL.pth, you can easily test the output masks on your images via the CLI.
To predict a single image and save it:
python predict.py -i image.jpg -o output.jpg
To predict a multiple images and show them without saving them:
python predict.py -i image1.jpg image2.jpg --viz --no-save
> python predict.py -h
usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...]
[--output INPUT [INPUT ...]] [--viz] [--no-save]
[--mask-threshold MASK_THRESHOLD] [--scale SCALE]
Predict masks from input images
optional arguments:
-h, --help show this help message and exit
--model FILE, -m FILE
Specify the file in which the model is stored
(default: MODEL.pth)
--input INPUT [INPUT ...], -i INPUT [INPUT ...]
filenames of input images (default: None)
--output INPUT [INPUT ...], -o INPUT [INPUT ...]
Filenames of ouput images (default: None)
--viz, -v Visualize the images as they are processed (default:
False)
--no-save, -n Do not save the output masks (default: False)
--mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD
Minimum probability value to consider a mask pixel
white (default: 0.5)
--scale SCALE, -s SCALE
Scale factor for the input images (default: 0.5)You can specify which model file to use with --model MODEL.pth.
> python train.py -h
usage: train.py [-h] [-e E] [-b [B]] [-l [LR]] [-f LOAD] [-s SCALE] [-v VAL]
Train the UNet on images and target masks
optional arguments:
-h, --help show this help message and exit
-e E, --epochs E Number of epochs (default: 5)
-b [B], --batch-size [B]
Batch size (default: 1)
-l [LR], --learning-rate [LR]
Learning rate (default: 0.1)
-f LOAD, --load LOAD Load model from a .pth file (default: False)
-s SCALE, --scale SCALE
Downscaling factor of the images (default: 0.5)
-v VAL, --validation VAL
Percent of the data that is used as validation (0-100)
(default: 15.0)
By default, the scale is 0.5, so if you wish to obtain better results (but use more memory), set it to 1.
The input images and target masks s
687A
hould be in the data/imgs and data/masks folders respectively.
A pretrained model is available for the Carvana dataset. It can also be loaded from torch.hub:
net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana')The training was done with a 100% scale and bilinear upsampling.
You can visualize in real time the train and test losses, the weights and gradients, along with the model predictions with tensorboard:
tensorboard --logdir=runs
You can find a reference training run with the Caravana dataset on TensorBoard.dev (only scalars are shown currently).
The model has be trained from scratch on a GTX970M 3GB. Predicting images of 1918*1280 takes 1.5GB of memory. Training takes much 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.
Personalized support for issues with this repository, or integrating with your own dataset, available on xs:code.
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: https://arxiv.org/abs/1505.04597
