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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]() |
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# 4xNomos2_hq_atd |
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Scale: 4 |
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Architecture: [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary) |
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Architecture Option: [atd](https://github.com/muslll/neosr/blob/dc4e3742132bae2c2aa8e8d16de3a9fcec6b1a74/neosr/archs/atd_arch.py#L891) |
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Author: Philip Hofmann |
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License: CC-BY-0.4 |
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Purpose: Upscaler |
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Subject: Photography |
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Input Type: Images |
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Release Date: 05.09.2024 |
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Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets) |
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Dataset Size: 6000 |
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OTF (on the fly augmentations): No |
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Pretrained Model: 003_ATD_SRx4_finetune |
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Iterations: 180'000 |
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Batch Size: 2 |
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Patch Size: 48 |
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Norm: true |
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Description: |
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An atd 4x upscaling model, similiar to the [4xNomos2_hq_dat2](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_dat2) or [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) models, trained and for usage on non-degraded input to give good quality output. |
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Training checkpoints metric scoring on val images |
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Showcase of the top 3 checkpoints from this model training, where 180k has been selected as the main release model: https://slow.pics/c/ZEnoG0Ou |
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I added the other checkpoints (135k and 205k) as additional model files in the assets of this release. |
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## Model Showcase: |
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[Slowpics](https://slow.pics/c/ttYvxmJq) |
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(Click on image for better view) |
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