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license: cc-by-4.0 |
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pipeline_tag: image-to-image |
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tags: |
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- pytorch |
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- super-resolution |
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xLSDIRCompactC) |
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# 4xLSDIRCompactC |
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Name: 4xLSDIRCompactC |
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Author: Philip Hofmann |
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Release Date: 17.03.2023 |
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License: CC BY 4.0 |
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Network: SRVGGNetCompact |
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Scale: 4 |
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Purpose: 4x photo upscaler that handler jpg compression |
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Iterations: 190000 |
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batch_size: Variable(1-5) |
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HR_size: 256 |
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Dataset: LSDIR |
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Dataset_size: 84991 |
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OTF Training No |
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Pretrained_Model_G: 4xLSDIRCompact.pth |
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Description: Trying to extend my previous model to be able to handle compression (JPG 100-30) by manually altering the training dataset, since 4xLSDIRCompact cant handle compression. Use this instead of 4xLSDIRCompact if your photo has compression (like an image from the web). |
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Here is a comparison to show that 4xLSDIRCompact cannot handle compression artifacts, and that these two models will produce better output for that specific scenario. These models are not ‘better’ than the previous one, they are just meant to handle a different use case: https://imgsli.com/MTYyODY3 |
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