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license: cc-by-4.0
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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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![image](https://github.com/user-attachments/assets/2ae3b750-adbd-4ceb-8ff3-cafee2ee97d1)
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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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![Example1](https://github.com/user-attachments/assets/b4ec00fb-464c-4039-b50f-656c8a49a2b4)
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![Example2](https://github.com/user-attachments/assets/3cd16733-f14d-424a-94e5-ef812aa9a1dd)
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![Example3](https://github.com/user-attachments/assets/4d19f3e5-5844-437f-9805-9a1eb50d79db)
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![Example4](https://github.com/user-attachments/assets/c7246289-faed-4f26-a987-da1f88eb6f33)
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![Example5](https://github.com/user-attachments/assets/09286ce5-6f64-467a-a9db-a6ddbc803b32)
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