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]

This example trains a super-resolution network on the BSD300 dataset, dataset also increse by "crop" function. 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

Check

For check you can use server.py which also request flask