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--- |
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license: cc-by-4.0 |
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tags: |
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- super-resolution |
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pretty_name: DF2K_BHI |
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size_categories: |
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- 1K<n<10K |
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--- |
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# DF2K_BHI SISR Dataset |
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This is a filtered version of the DF2K dataset, specifically aimed for single image super-resolution model training. |
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It is a result, or is one of the testing sets, for [my huggingface community blog post on my BHI-Filtering method](https://huggingface.co/blog/Phips/bhi-filtering). |
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## Dataset Details |
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This is based on the DF2K dataset, which consists of X images. |
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These images then have been tiled into 21'387 512x512px tiles for faster sisr I/O training speed. |
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The BHI filtering has been applied with the following default threshold values: |
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Blockiness < 30 |
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HyperIQA >= 0.2 |
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IC9600 >= 0.4 |
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Which results in this DF2K_BHI dataset with 12'639 512x512 training tiles. |
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## Visualization |
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Here is a visual example of the first few tiles that are in the dataset: |
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## Training |
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As can be seen in the following training validation tensorboard graphs (DIV2K validation dataset), this BHI-filtered version of the DF2K dataset is able to achieve higher or similiar metric scores while seeing a reduction of -40.9% in training tiles quantity. |
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Here is the training validation metrics on the DIV2K validation dataset, using the plksr_tiny architecture option, in comparison to the tiled full DF2K dataset: |
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Tiled DF2K dataset (here called X): 21'387 tiles |
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DF2K_BHI dataset: 12'639 tiles |
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