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---
license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
---
[Link to Github Release]()
# 4xNomos2_hq_atd
Scale: 4
Architecture: [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary)
Architecture Option: [atd](https://github.com/muslll/neosr/blob/dc4e3742132bae2c2aa8e8d16de3a9fcec6b1a74/neosr/archs/atd_arch.py#L891)
Author: Philip Hofmann
License: CC-BY-0.4
Purpose: Upscaler
Subject: Photography
Input Type: Images
Release Date: 05.09.2024
Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)
Dataset Size: 6000
OTF (on the fly augmentations): No
Pretrained Model: 003_ATD_SRx4_finetune
Iterations: 180'000
Batch Size: 2
Patch Size: 48
Norm: true
Description:
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.
Training checkpoints metric scoring on val images
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
I added the other checkpoints (135k and 205k) as additional model files in the assets of this release.
## Model Showcase:
[Slowpics](https://slow.pics/c/ttYvxmJq)
(Click on image for better view)
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