4xNomos2 Hq Atd

--- 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)     

Source context: 0 downloads · 0 likes · Pipeline image-to-image · Repo Phips/4xNomos2_hq_atd