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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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- pretrain |
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--- |
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[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xmssim_realplksr_dysample_pretrain) |
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# 4xmssim_realplksr_dysample_pretrain |
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Scale: 4 |
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Architecture: [RealPLKSR with Dysample](https://github.com/muslll/neosr/?tab=readme-ov-file#supported-archs) |
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Architecture Option: [realplksr](https://github.com/muslll/neosr/blob/master/neosr/archs/realplksr_arch.py) |
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Author: Philip Hofmann |
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License: CC-BY-0.4 |
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Purpose: Pretrained |
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Subject: Photography |
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Input Type: Images |
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Release Date: 27.06.2024 |
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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: None (=From Scratch) |
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Iterations: 200'000 |
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Batch Size: 8 |
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GT Size: 192, 512 |
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Description: [Dysample](https://arxiv.org/pdf/2308.15085) had been recently added to RealPLKSR, which from what I had seen can resolve or help avoid the checkerboard / grid pattern on inference outputs. So with the [commits from three days ago, the 24.06.24, on neosr](https://github.com/muslll/neosr/commits/master/?since=2024-06-24&until=2024-06-24), I wanted to create a 4x photo pretrain I can then use to train more realplksr models with dysample specifically to stabilize training at the beginning. |
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Showcase: |
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[Imgsli](https://imgsli.com/Mjc0OTA1) |
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[Slowpics](https://slow.pics/c/I9grkcqM) |
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