Superresolution using an efficient sub-pixel convolutional neural network

This example illustrates how to use the efficient sub-pixel convolution layer described in "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al. for increasing spatial resolution within your network for tasks such as superresolution.

usage: main.py [-h] --upscale_factor UPSCALE_FACTOR [--batchSize BATCHSIZE]
               [--testBatchSize TESTBATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
               [--cuda] [--threads THREADS] [--seed SEED]

PyTorch Super Res Example

optional arguments:
  -h, --help            show this help message and exit
  --upscale_factor      super resolution upscale factor
  --batchSize           training batch size
  --testBatchSize       testing batch size
  --nEpochs             number of epochs to train for
  --lr                  Learning Rate. Default=0.01
  --cuda                use cuda
  --threads             number of threads for data loader to use Default=4
  --seed                random seed to use. Default=123

This example trains a super-resolution network on the BSD300 dataset, using crops from the 200 training images, and evaluating on crops of the 100 test images. A snapshot of the model after every epoch with filename model_epoch_<epoch_number>.pth

Example Usage:

Train

python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001

Super Resolve

python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png