update Benchmark.md
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## Photo
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## Photo
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Note: waifu2x's photo models was trained on [kou's photo collection](http://photosku.com/photo/category/%E6%92%AE%E5%BD%B1%E8%80%85/kou/).
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Note: waifu2x's photo models was trained on [kou's photo collection](http://photosku.com/photo/category/%E6%92%AE%E5%BD%B1%E8%80%85/kou/).
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Note: PSNR in this benchmark uses a MATLAB's rgb2ycbcr 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.
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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.
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command:
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command:
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`th tools/benchmark.lua -dir <dataset_dir> -model1_dir <model_dir> -method scale -filter Catrom -color y -range_bug 1 -tta <0|1> -force_cudnn 1`
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`th tools/benchmark.lua -dir <dataset_dir> -model1_dir <model_dir> -method scale -filter Catrom -color y -range_bug 1 -tta <0|1> -force_cudnn 1`
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### 2x with TTA - PSNR
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### 2x with TTA - PSNR
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Note: TTA is an ensemble technique that is supported by waifu2x. This method is 8x slower than non TTA method but it improves PSNR (~+0.1 on photo, ~+0.4 on art).
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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).
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| Dataset/Model | Bicubic | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo |
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| Dataset/Model | Bicubic | vgg\_7/photo | upconv\_7/photo | upconv\_7l/photo |
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|---------------|---------------|---------------|------------------|------------------|
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|---------------|---------------|---------------|------------------|------------------|
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