Skip to content

Latest commit

 

History

History
112 lines (86 loc) · 4.17 KB

README.md

File metadata and controls

112 lines (86 loc) · 4.17 KB
Raw

UNet3plus_pth

UNet3+/UNet++/UNet, used in Deep Automatic Portrait Matting in Pytorth

Dependencies

  • Python 3.6
  • PyTorch >= 1.1.0
  • Torchvision >= 0.3.0
  • future 0.18.2
  • matplotlib 3.1.3
  • numpy 1.16.0
  • Pillow 6.2.0
  • protobuf 3.11.3
  • tensorboard 1.14.0
  • tqdm==4.42.1

Data

This model was trained from scratch with 18000 images (data augmentation by 2000images) Training dataset was from Deep Automatic Portrait Matting. Your can download in baidu cloud http://pan.baidu.com/s/1dE14537. Password: ndg8 For academic communication only, if there is a quote, please inform the original author!

We augment the number of images by perturbing them withrotation and scaling. Four rotation angles{−45◦,−22◦,22◦,45◦}and four scales{0.6,0.8,1.2,1.5}are used. We also apply four different Gamma transforms toincrease color variation. The Gamma values are{0.5,0.8,1.2,1.5}. After thesetransforms, we have 18K training images.

Run locally

Note : Use Python 3

Training

> python train.py -h
usage: train.py [-h] [-g G] [-u U] [-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
  -g G, --gpu_id        Number of gpu
  -u U, --unet\_type    UNet type is unet/unet2/unet3
  -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: 10.0)

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 should be in the data/imgs and data/masks folders respectively.

Notes on memory

$ python train.py -g 0 -u v3 -e 200 -b 1 -l 0.1 -s 0.5 -v 15.0

Prediction

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] [--gpu_id 0]  [--unet\_type unet/unet2/unet3] [--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
  -g G, --gpu_id        Number of gpu
  --unet\_type, -u U    UNet type is unet/unet2/unet3
  --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)

Reference

[2015] U-Net: Convolutional Networks for Biomedical Image Segmentation (MICCAI)

[2018] UNet++: A Nested U-Net Architecture for Medical Image Segmentation (MICCAI)

[2020] UNET 3+: A Full-Scale Connected UNet for Medical Image Segmentation (ICASSP 2020)