py - opt options / swinir / train_swinir_sr_realworld_psnr. launch - nproc_per_node = 8 - master_port = 1234 main_train_psnr. json - dist True # 003 Real-World Image SR (middle size) python - m torch. py - opt options / swinir / train_swinir_sr_lightweight. json - dist True # 002 Lightweight Image SR (small size) python - m torch. py - opt options / swinir / train_swinir_sr_classical. # 001 Classical Image SR (middle size) python - m torch. You may need to change the dataroot_H, dataroot_L, scale factor, noisel level, JPEG level, G_optimizer_lr, G_scheduler_milestones, etc.
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To train SwinIR, run the following commands.
#Download ost master sun download#
* We use the first practical degradation model BSRGAN, ICCV2021 for real-world image SRĭIV2K (800 training images) + Flickr2K (2650 images) + BSD500 (400 training&testing images) + WED(4744 images)Ĭolor: CBSD68 + Kodak24 + McMaster + Urban100 download all SwinIR-L (large size): DIV2K + Flickr2K + OST + WED(4744 images) + FFHQ (first 2000 images, face) + Manga109 (manga) + SCUT-CTW1500 (first 100 training images, texts) SwinIR-M (middle size): DIV2K (800 training images) + Flickr2K (2650 images) + OST (10324 images, sky,water,grass,mountain,building,plant,animal) Set5 + Set14 + BSD100 + Urban100 + Manga109 download all TaskĭIV2K (800 training images) or DIV2K + Flickr2K (2650 images) Please put them in trainsets and testsets respectively. Training and testing sets can be downloaded as follows. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.
![download ost master sun download ost master sun](https://i1.sndcdn.com/artworks-000059276455-22dlaw-large.jpg)
We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images).
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Paper | supplementary | visual results | original project page | online Colab demo SwinIR: Image Restoration Using Shifted Window Transformer