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README.md
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The BHI SISR Dataset's purpose is for training single image super-resolution models and is a result of tests on my BHI filtering method, which I made [a huggingface community blogpost about](https://huggingface.co/blog/Phips/bhi-filtering), which can be extremely summarized by that removing (by filtering) only the worst quality tiles from a training set has a way bigger positive effect on training metrics than keeping only the best quality training tiles.
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It consists of 390'241 images, which are all 512x512px dimensions and in the webp format.
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The advantage of such a big dataset is when applying degradations in a randomized manner to create a corresponding LR, the distribution of degradations and strenghts should be sufficient because of the quantity of training tiles. I will create some corresponding x4 LR datasets to this one and publish them aswell.
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Though if an on-the-fly degradation pipeline is used during training, such a high quantity of training tiles would probably generally not be needed since longer training iterations make sure of distribution.
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The BHI SISR Dataset's purpose is for training single image super-resolution models and is a result of tests on my BHI filtering method, which I made [a huggingface community blogpost about](https://huggingface.co/blog/Phips/bhi-filtering), which can be extremely summarized by that removing (by filtering) only the worst quality tiles from a training set has a way bigger positive effect on training metrics than keeping only the best quality training tiles.
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It consists of 390'241 images, which are all 512x512px dimensions and in the webp format.
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<figure>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/634e9aa407e669188d3912f9/bV0oaFKJzdsEqRme_lqU8.png" alt="48 first training tiles">
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<figcaption>Visual example - the first 48 training tiles</figcaption>
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</figure>
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The advantage of such a big dataset is when applying degradations in a randomized manner to create a corresponding LR, the distribution of degradations and strenghts should be sufficient because of the quantity of training tiles. I will create some corresponding x4 LR datasets to this one and publish them aswell.
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Though if an on-the-fly degradation pipeline is used during training, such a high quantity of training tiles would probably generally not be needed since longer training iterations make sure of distribution.
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