SwinIR

Reimplementation of 4x SwinIR https://arxiv.org/abs/2108.10257

4x Result

PSNR

Dataset	Bicubic	SwinIR
Set5	28.648	32.666 (32.920)
Set14	26.406	29.020 (29.090)
Urban100	23.220	27.133 (27.450)

(number) comes from the paper.

[2023.05.04: Set14 result update]

Now gray-scale test image result is included. Please be aware that still this code does not use gray-scale test images.

Set14

Bicubic	SwinIR	GT

Train Setting

Item	Setting
Train Data	DIV2K, Flickr2K
Preprocess	[-1,1] Normalization
Random Transforms	Crop {DIV2K(48x48), Flickr2K(64x64)}, Rotation {90 degree}
Validation Data	DIV2K
Test Data	Set5, Set14, Urban100
Scale	4x
Optimizer	Adam
Learning Rate	2e-4
Scheduler	Reduce LR to half at 50%, 80%, 90%, 95% of 5e5 iterations
Actual Trained Iterations	Around 4.3e5
Loss	L1
Batch	8 {2 for each GPU, total 4 GPUs are utilized}