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---
license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
---
[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_dat2)
# 4xNomos2_hq_dat2
Scale: 4
Architecture: [DAT](https://github.com/zhengchen1999/dat)
Architecture Option: [dat2](https://github.com/muslll/neosr/blob/5fba7f162d36052010169e6517dec3b406c569ab/neosr/archs/dat_arch.py#L1111)
Author: Philip Hofmann
License: CC-BY-0.4
Purpose: Upscaler
Subject: Photography
Input Type: Images
Release Date: 29.08.2024
Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)
Dataset Size: 6000
OTF (on the fly augmentations): No
Pretrained Model: DAT_2_x4
Iterations: 140'000
Batch Size: 4
Patch Size: 48
Description:
A dat2 4x upscaling model, similiar to the [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) model, trained and for usage on non-degraded input to give good quality output.
I scored 7 validation outputs of each of the 21 checkpoints (10k-210k) of this model training with 68 metrics.
[The metric scores can be found in this google sheet](https://docs.google.com/spreadsheets/d/1NL-by7WvZyDMHj5XN8UeDALVSSwH70IKvwV65ATWqrA/edit?usp=sharing).
The corresponding image files for this scoring can be [found here](https://drive.google.com/file/d/1ZTp9fBMeawftNqzg4RN9_zIvHtul5jVc/view?usp=sharing)
Screenshot of the google sheet:
Release checkpoint has been selected by looking at the scores, manually inspecting, and then getting responses on discord which chose B to this quick visual test, A B or C, which denote different checkpoints: https://slow.pics/c/8Akzj6rR
Checkpoint B is 140k which is 4xNomos2_hq_dat2. But I added checkpoint A (4xNomos2_hq_dat2_150000) and checkpoint C (4xNomos2_hq_dat2_10000) model files additionally here if people want to try them out).
## Model Showcase:
[Slowpics](https://slow.pics/c/yuue9WpF)
(Click on image for better view)
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