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] [--resume RESUME] 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 --resume resume from checkpoint Training on BSD300 dataset
Put root_dir = download_bsd300() in data.py file * Put root_dir = document_dataset() in data.py file * Use the following folder structure: dataset | --- document | --- images | --- test | --- train python main.py --upscale_factor 3 --batchSize 4 --testBatchSize 100 --nEpochs 30 --lr 0.001
python super_resolve.py --input_image dataset/BSDS300/images/test/16077.jpg --model model_epoch_500.pth --output_filename out.png
provide path with --resume arguement
Use the script scrape_google_search_images.py to scrape HD images from Google search results
usage: scrape_google_search_images.py [--search SEARCH] [--num_images NUM_IMAGES] [--directory DIRECTORY] Scrape Google images arguments: --search search term --num_images number of images to save --directory directory path to save results Point to Note: Script scrape_google_search_images.py works well with Python 2.x version.