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--- |
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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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--- |
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos8kHAT-L_bokeh_jpg) |
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# 4xNomos8kHAT-L_bokeh_jpg |
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Name: 4xNomos8kHAT-L_bokeh_jpg |
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Author: Philip Hofmann |
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Release: 05.10.2023 |
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License: CC BY 4.0 |
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Network: HAT |
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Scale: 4 |
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Purpose: 4x photo upscaler (handles bokeh effect and jpg compression) |
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Iterations: 145000 |
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epoch: 66 |
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batch_size: 4 |
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HR_size: 128 |
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Dataset: nomos8k |
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Number of train images: 8492 |
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OTF Training: No |
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Pretrained_Model_G: HAT-L_SRx4_ImageNet-pretrain |
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Description: |
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4x photo upscaler, made to specifically handle bokeh effect and jpg compression. Basically a HAT-L variant of the already released 4xNomosUniDAT_bokeh_jpg model, but specifically trained for photos on the nomos8k dataset (and hopefully without the smoothing effect). |
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The three strengths of this model (design purpose): |
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Specifically for photos / photography |
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Handles bokeh effect |
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Handles jpg compression |
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This model will not attempt to: |
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Denoise |
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Deblur |
