# Benchmarks ## Photo Note: waifu2x's photo models was trained on the blending dataset of [kou's photo collection](http://photosku.com/photo/category/%E6%92%AE%E5%BD%B1%E8%80%85/kou/) and [ukbench](http://vis.uky.edu/~stewe/ukbench/). Note: PSNR in this benchmark uses a [MATLAB's rgb2ycbcr](https://jp.mathworks.com/help/images/ref/rgb2ycbcr.html?lang=en) compatible function (dynamic range [16 235], not [0 255]) for converting grayscale image. I think it's not correct PSNR. But many paper used this metric. command: `th tools/benchmark.lua -dir -model1_dir -method scale -filter Catrom -color y -range_bug 1 -tta <0|1> -force_cudnn 1` ### Datasets BSD100: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/ (100 test images in BSDS300) Urban100: https://github.com/jbhuang0604/SelfExSR ### 2x - PSNR | Dataset/Model | Bicubic | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo | resnet_14l/photo | |---------------|---------------|---------------|------------------|------------------|--------------------| | BSD100 | 29.558 | 31.427 | 31.640 | 31.749 | 31.847 | | Urban100 | 26.852 | 30.057 | 30.477 | 30.759 | 31.016 | ### 2x - benchmark time (sec) | Dataset/Model | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo | resnet_14l/photo | |---------------|---------------|------------------|------------------|--------------------| | BSD100 | 4.057 | 2.509 | 4.947 | 6.86 | | Urban100 | 16.349 | 7.083 | 14.178 | 27.87 | ### 2x with TTA - PSNR Note: TTA is an ensemble technique that is supported by waifu2x. TTA method is 8x slower than non TTA method but it improves PSNR (~+0.1 on photo, ~+0.4 on art). | Dataset/Model | Bicubic | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo | resnet_14l/photo | |---------------|---------------|---------------|------------------|------------------|--------------------| | BSD100 | 29.558 | 31.474 | 31.705 | 31.812 | 31.915 | | Urban100 | 26.852 | 30.140 | 30.599 | 30.868 | 31.162 | ### 2x with TTA - benchmark time (sec) | Dataset/Model | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo | resnet_14l/photo | |---------------|---------------|------------------|------------------|--------------------| | BSD100 | 36.611 | 20.219 | 42.486 | 60.38 | | Urban100 | 132.416 | 65.125 | 129.916 | 255.20 | ## Art command: See `appendix/benchmark.sh` ### Dataset art_test: This dataset contains 84 various fan-arts. Sorry, This dataset is private. ### 2x - PSNR | Filter/Model | Bicubic | vgg\_7/art | upconv\_7/art | cunet/art | |----------------|---------------|-------------|----------------|----------------| | Lanczos | 31.022 | 37.495 | 38.330 | 39.886 | | Sinc | 30.947 | 37.722 | 38.538 | 40.312 | | Catrom(Bicubic)| 30.663 | 37.278 | 37.189 | 40.184 | | Box | 30.891 | 37.709 | 38.410 | 39.672 | ### 2x - benchmark time (sec) | Dataset/Model | vgg\_7/art | upconv\_7/art | cunet/art | |---------------|-------------|----------------|----------------| | art_test | 24.153 | 10.794 | 24.222 | ### 2x with TTA - PSNR | Filter/Model | Bicubic | vgg\_7/art | upconv\_7/art | cunet/art | |----------------|---------------|-------------|----------------|----------------| | Lanczos | 31.022 | 37.777 | 38.677 | 40.289 | | Sinc | 30.947 | 38.005 | 38.883 | 40.707 | | Catrom(Bicubic)| 30.663 | 37.498 | 37.417 | 40.592 | | Box | 30.891 | 38.032 | 38.768 | 40.032 | ### 2x with TTA - benchmark time (sec) | Dataset/Model | vgg\_7/art | upconv\_7/art | cunet/art | |---------------|-------------|----------------|----------------| | art_test | 207.217 | 99.151 | 211.520 |