图像超分辨率¶
概览¶
预训练权重个数: 52
配置文件个数: 0
论文个数: 11
ALGORITHM: 11
SwinIR (ICCVW’2021)¶
任务: 图像超分辨率, 图像去噪, JPEG压缩伪影移除
SwinIR (ICCVW'2021)
@inproceedings{liang2021swinir,
title={Swinir: Image restoration using swin transformer},
author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={1833--1844},
year={2021}
}
Classical Image Super-Resolution¶
在 Y 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU 信息 | 下载 |
|---|---|---|---|---|---|---|---|---|
| swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k | 38.3240 | 34.1174 | 37.8921 | 0.9626 | 0.9230 | 0.9481 | 8 | model | log |
| swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k | 34.8640 | 30.7669 | 34.1397 | 0.9317 | 0.8508 | 0.8917 | 8 | model | log |
| swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k | 32.7315 | 28.9065 | 32.0953 | 0.9029 | 0.7915 | 0.8418 | 8 | model | log |
| swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k | 38.3971 | 34.4149 | 37.9473 | 0.9629 | 0.9252 | 0.9488 | 8 | model | log |
| swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k | 34.9335 | 30.9258 | 34.2830 | 0.9323 | 0.8540 | 0.8939 | 8 | model | log |
| swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k | 32.9214 | 29.0792 | 32.3021 | 0.9053 | 0.7953 | 0.8451 | 8 | model | log |
Lightweight Image Super-Resolution¶
在 Y 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU 信息 | 下载 |
|---|---|---|---|---|---|---|---|---|
| swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k | 38.1289 | 33.8404 | 37.5844 | 0.9617 | 0.9207 | 0.9459 | 8 | model | log |
| swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k | 34.6037 | 30.5340 | 33.8394 | 0.9293 | 0.8468 | 0.8867 | 8 | model | log |
| swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k | 32.4343 | 28.7441 | 31.8636 | 0.8984 | 0.7861 | 0.8353 | 8 | model | log |
Real-World Image Super-Resolution¶
在 Y 通道上进行评估。 我们使用 NIQE 作为指标。
| 算法 | RealSRSet+5images NIQE | GPU 信息 | 下载 |
|---|---|---|---|
| swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost | 5.7975 | 8 | model | log |
| swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost | 7.2738 | 8 | model | log |
| swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost | 5.2329 | 8 | model | log |
| swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost | 7.7460 | 8 | model | log |
| swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost | 5.1464 | 8 | model | log |
| swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost | 7.6378 | 8 | model | log |
Grayscale Image Deoising¶
在灰度图上进行评估。 我们使用 PSNR 作为指标。
| 算法 | Set12 PSNR | BSD68 PSNR | Urban100 PSNR | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15 | 33.9731 | 32.5203 | 34.3424 | 8 | model | log |
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25 | 31.6434 | 30.1377 | 31.9493 | 8 | model | log |
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50 | 28.5651 | 27.3157 | 28.6626 | 8 | model | log |
Color Image Deoising¶
在 RGB 通道上进行评估。 我们使用 PSNR 作为指标。
| 算法 | CBSD68 PSNR | Kodak24 PSNR | McMaster PSNR | Urban100 PSNR | GPU 信息 | 下载 |
|---|---|---|---|---|---|---|
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15 | 34.4136 | 35.3555 | 35.6205 | 35.1836 | 8 | model | log |
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25 | 31.7626 | 32.9003 | 33.3198 | 32.9458 | 8 | model | log |
| swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50 | 28.5346 | 29.8058 | 30.2027 | 29.8832 | 8 | model | log |
JPEG Compression Artifact Reduction (grayscale)¶
在灰度图上进行评估。 我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Classic5 PSNR | Classic5 SSIM | LIVE1 PSNR | LIVE1 SSIM | GPU 信息 | 下载 |
|---|---|---|---|---|---|---|
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10 | 30.2746 | 0.8254 | 29.8611 | 0.8292 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20 | 32.5331 | 0.8753 | 32.2667 | 0.8914 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30 | 33.7504 | 0.8966 | 33.7001 | 0.9179 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40 | 34.5377 | 0.9087 | 34.6846 | 0.9322 | 8 | model | log |
JPEG Compression Artifact Reduction (color)¶
在 RGB 通道上进行评估。 我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Classic5 PSNR | Classic5 SSIM | LIVE1 PSNR | LIVE1 SSIM | GPU 信息 | 下载 |
|---|---|---|---|---|---|---|
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10 | 30.1019 | 0.8217 | 28.0676 | 0.8094 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20 | 32.3489 | 0.8727 | 30.3489 | 0.8745 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30 | 33.6028 | 0.8949 | 31.8235 | 0.9023 | 8 | model | log |
| swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40 | 34.4344 | 0.9076 | 32.7610 | 0.9179 | 8 | model | log |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
## 002 Lightweight Image Super-Resolution (small size)
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
## 003 Real-World Image Super-Resolution
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
## 004 Grayscale Image Deoising (middle size)
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py
## 005 Color Image Deoising (middle size)
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)
## grayscale
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py
## color
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py
## 单个GPU上训练
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
python tools/train.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
python tools/train.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
python tools/train.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
python tools/train.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
python tools/train.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
python tools/train.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py
## 002 Lightweight Image Super-Resolution (small size)
python tools/train.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
python tools/train.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
python tools/train.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py
## 003 Real-World Image Super-Resolution
python tools/train.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
python tools/train.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
python tools/train.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
python tools/train.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py
python tools/train.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
python tools/train.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py
## 004 Grayscale Image Deoising (middle size)
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py
## 005 Color Image Deoising (middle size)
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py
python tools/train.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)
## grayscale
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py
## color
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py
python tools/train.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py
## 多个GPU上训练
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
./tools/dist_train.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py 8
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
./tools/dist_train.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py 8
## 002 Lightweight Image Super-Resolution (small size)
./tools/dist_train.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8
./tools/dist_train.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py 8
## 003 Real-World Image Super-Resolution
./tools/dist_train.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8
./tools/dist_train.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8
./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8
./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py 8
./tools/dist_train.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8
./tools/dist_train.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py 8
## 004 Grayscale Image Deoising (middle size)
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py 8
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py 8
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py 8
## 005 Color Image Deoising (middle size)
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py 8
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py 8
./tools/dist_train.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py 8
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)
## grayscale
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py 8
## color
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py 8
./tools/dist_train.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth
## 002 Lightweight Image Super-Resolution (small size)
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth
## 003 Real-World Image Super-Resolution
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth
## 004 Grayscale Image Deoising (middle size)
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth
## 005 Color Image Deoising (middle size)
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)
## grayscale
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth
## color
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth
## 单个GPU上测试
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
python tools/test.py configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth
python tools/test.py configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth
python tools/test.py configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
python tools/test.py configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth
python tools/test.py configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth
python tools/test.py configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth
## 002 Lightweight Image Super-Resolution (small size)
python tools/test.py configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth
python tools/test.py configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth
python tools/test.py configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth
## 003 Real-World Image Super-Resolution
python tools/test.py configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth
python tools/test.py configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth
python tools/test.py configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth
python tools/test.py configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth
python tools/test.py configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth
python tools/test.py configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth
## 004 Grayscale Image Deoising (middle size)
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth
## 005 Color Image Deoising (middle size)
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth
python tools/test.py configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding usesx8 blocks)
## grayscale
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth
## color
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth
python tools/test.py configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40-5b77a6e6.pth
## 多GPU测试
## 001 Classical Image Super-Resolution (middle size)
## (setting1: when model is trained on DIV2K and with training_patch_size=48)
./tools/dist_test.sh configs/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s48w8d6e180_8xb4-lr2e-4-500k_div2k-ed2d419e.pth
./tools/dist_test.sh configs/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s48w8d6e180_8xb4-lr2e-4-500k_div2k-926950f1.pth
./tools/dist_test.sh configs/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s48w8d6e180_8xb4-lr2e-4-500k_div2k-88e4903d.pth
## (setting2: when model is trained on DIV2K+Flickr2K and with training_patch_size=64)
./tools/dist_test.sh configs/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d6e180_8xb4-lr2e-4-500k_df2k-69e15fb6.pth
./tools/dist_test.sh configs/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d6e180_8xb4-lr2e-4-500k_df2k-d6982f7b.pth
./tools/dist_test.sh configs/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d6e180_8xb4-lr2e-4-500k_df2k-0502d775.pth
## 002 Lightweight Image Super-Resolution (small size)
./tools/dist_test.sh configs/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x2s64w8d4e60_8xb4-lr2e-4-500k_div2k-131d3f64.pth
./tools/dist_test.sh configs/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x3s64w8d4e60_8xb4-lr2e-4-500k_div2k-309cb239.pth
./tools/dist_test.sh configs/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k.py https://download.openmmlab.com/mmediting/swinir/swinir_x4s64w8d4e60_8xb4-lr2e-4-500k_div2k-d6622d03.pth
## 003 Real-World Image Super-Resolution
./tools/dist_test.sh configs/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-c6425057.pth
./tools/dist_test.sh configs/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x2s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-6f0c425f.pth
./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-36960d18.pth
./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d6e180_8xb4-lr1e-4-600k_df2k-os-a016a72f.pth
./tools/dist_test.sh configs/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_gan-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-9f1599b5.pth
./tools/dist_test.sh configs/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-ost.py https://download.openmmlab.com/mmediting/swinir/swinir_psnr-x4s64w8d9e240_8xb4-lr1e-4-600k_df2k-os-25f1722a.pth
## 004 Grayscale Image Deoising (middle size)
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN15-6782691b.pth
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN25-d0d8d4da.pth
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-grayDN50-54c9968a.pth
## 005 Color Image Deoising (middle size)
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN15-c74a2cee.pth
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN25-df2b1c0c.pth
./tools/dist_test.sh configs/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50.py https://download.openmmlab.com/mmediting/swinir/swinir_s128w8d6e180_8xb1-lr2e-4-1600k_dfwb-colorDN50-e369874c.pth
## 006 JPEG Compression Artifact Reduction (middle size, using window_size=7 because JPEG encoding uses 8x8 blocks)
## grayscale
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR10-da93c8e9.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR20-d47367b1.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR30-52c083cf.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-grayCAR40-803e8d9b.pth
## color
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR10-09aafadc.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR20-b8a42b5e.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth
./tools/dist_test.sh configs/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR40.py https://download.openmmlab.com/mmediting/swinir/swinir_s126w7d6e180_8xb1-lr2e-4-1600k_dfwb-colorCAR30-e9fe6859.pth
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
Real-ESRGAN (ICCVW’2021)¶
任务: 图像超分辨率
Real-ESRGAN (ICCVW'2021)
@inproceedings{wang2021real,
title={Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic data},
author={Wang, Xintao and Xie, Liangbin and Dong, Chao and Shan, Ying},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
pages={1905--1914},
year={2021}
}
在 RGB 通道上进行评估，指标为 PSNR/SSIM。
| 算法 | Set5 | GPU 信息 | 下载 |
|---|---|---|---|
| realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost | 28.0297/0.8236 | 4 (Tesla V100-SXM2-32GB) | 模型/日志 |
| realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost | 26.2204/0.7655 | 4 (Tesla V100-SXM2-32GB) | 模型 /日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py
## 单个GPU上训练
python tools/train.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py
## 多个GPU上训练
./tools/dist_train.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth
## 单个GPU上测试
python tools/test.py configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth
## 多个GPU上测试
./tools/dist_test.sh configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
LIIF (CVPR’2021)¶
任务: 图像超分辨率
LIIF (CVPR'2021)
@inproceedings{chen2021learning,
title={Learning continuous image representation with local implicit image function},
author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={8628--8638},
year={2021}
}
| 算法 | scale | Set5 PSNR / SSIM |
Set14 PSNR / SSIM |
DIV2K PSNR / SSIM |
GPU 信息 | 下载 |
|---|---|---|---|---|---|---|
| liif_edsr_norm_c64b16_g1_1000k_div2k | x2 | 35.7131 / 0.9366 | 31.5579 / 0.8889 | 34.6647 / 0.9355 | 1 (TITAN Xp) | 模型 | 日志 |
| △ | x3 | 32.3805 / 0.8915 | 28.4605 / 0.8039 | 30.9808 / 0.8724 | △ | △ |
| △ | x4 | 30.2748 / 0.8509 | 26.8415 / 0.7381 | 29.0245 / 0.8187 | △ | △ |
| △ | x6 | 27.1187 / 0.7774 | 24.7461 / 0.6444 | 26.7770 / 0.7425 | △ | △ |
| △ | x18 | 20.8516 / 0.5406 | 20.0096 / 0.4525 | 22.1987 / 0.5955 | △ | △ |
| △ | x30 | 18.8467 / 0.5010 | 18.1321 / 0.3963 | 20.5050 / 0.5577 | △ | △ |
| liif_rdn_norm_c64b16_g1_1000k_div2k | x2 | 35.7874 / 0.9366 | 31.6866 / 0.8896 | 34.7548 / 0.9356 | 1 (TITAN Xp) | 模型 | 日志 |
| △ | x3 | 32.4992 / 0.8923 | 28.4905 / 0.8037 | 31.0744 / 0.8731 | △ | △ |
| △ | x4 | 30.3835 / 0.8513 | 26.8734 / 0.7373 | 29.1101 / 0.8197 | △ | △ |
| △ | x6 | 27.1914 / 0.7751 | 24.7824 / 0.6434 | 26.8693 / 0.7437 | △ | △ |
| △ | x18 | 20.8913 / 0.5329 | 20.1077 / 0.4537 | 22.2972 / 0.5950 | △ | △ |
| △ | x30 | 18.9354 / 0.4864 | 18.1448 / 0.3942 | 20.5663 / 0.5560 | △ | △ |
注：
△ 指同上。
这两个配置仅在 testing pipeline 上有所不同。 所以他们使用相同的检查点。
数据根据 EDSR 进行正则化。
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的
scale像素。
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py
## 单个GPU上训练
python tools/train.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth
## 单个GPU上测试
python tools/test.py configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth
## 多个GPU上测试
./tools/dist_test.sh configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
GLEAN (CVPR’2021)¶
任务: 图像超分辨率
GLEAN (CVPR'2021)
@InProceedings{chan2021glean,
author = {Chan, Kelvin CK and Wang, Xintao and Xu, Xiangyu and Gu, Jinwei and Loy, Chen Change},
title = {GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
year = {2021}
}
有关训练和测试中使用的元信息，请参阅此处。 结果在 RGB 通道上进行评估。
| 算法 | PSNR | GPU 信息 | 下载 |
|---|---|---|---|
| glean_cat_8x | 23.98 | 2 (Tesla V100-PCIE-32GB) | 模型 | 日志 |
| glean_ffhq_16x | 26.91 | 2 (Tesla V100-PCIE-32GB) | 模型 | 日志 |
| glean_cat_16x | 20.88 | 2 (Tesla V100-PCIE-32GB) | 模型 | 日志 |
| glean_in128out1024_4x2_300k_ffhq_celebahq | 27.94 | 4 (Tesla V100-SXM3-32GB) | 模型 | 日志 |
| glean_fp16_cat_8x | - | - | - |
| glean_fp16_ffhq_16x | - | - | - |
| glean_fp16_in128out1024_4x2_300k_ffhq_celebahq | - | - | - |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/glean/glean_x8_2xb8_cat.py
## 单个GPU上训练
python tools/train.py configs/glean/glean_x8_2xb8_cat.py
## 多个GPU上训练
./tools/dist_train.sh configs/glean/glean_x8_2xb8_cat.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth
## 单个GPU上测试
python tools/test.py configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth
## 多个GPU上测试
./tools/dist_test.sh configs/glean/glean_x8_2xb8_cat.py https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
TTSR (CVPR’2020)¶
任务: 图像超分辨率
TTSR (CVPR'2020)
@inproceedings{yang2020learning,
title={Learning texture transformer network for image super-resolution},
author={Yang, Fuzhi and Yang, Huan and Fu, Jianlong and Lu, Hongtao and Guo, Baining},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5791--5800},
year={2020}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | scale | CUFED | GPU 信息 | 下载 |
|---|---|---|---|---|
| ttsr-rec_x4_c64b16_g1_200k_CUFED | x4 | 25.2433 / 0.7491 | 1 (TITAN Xp) | 模型 | 日志 |
| ttsr-gan_x4_c64b16_g1_500k_CUFED | x4 | 24.6075 / 0.7234 | 1 (TITAN Xp) | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py
## 单个GPU上训练
python tools/train.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py
## 多个GPU上训练
./tools/dist_train.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth
## 单个GPU上测试
python tools/test.py configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth
## 多个GPU上测试
./tools/dist_test.sh configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
DIC (CVPR’2020)¶
任务: 图像超分辨率
DIC (CVPR'2020)
@inproceedings{ma2020deep,
title={Deep face super-resolution with iterative collaboration between attentive recovery and landmark estimation},
author={Ma, Cheng and Jiang, Zhenyu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={5569--5578},
year={2020}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
在 dic_gan_x8c48b6_g4_150k_CelebAHQ 的日志中，DICGAN 在 CelebA-HQ 测试集的前9张图片上进行了验证，因此下表中的 PSNR/SSIM 与日志数据不同。
GPU 信息: 训练过程中的 GPU 信息.
| 算法 | scale | CelebA-HQ | GPU 信息 | 下载 |
|---|---|---|---|---|
| dic_x8c48b6_g4_150k_CelebAHQ | x8 | 25.2319 / 0.7422 | 4 (Tesla PG503-216) | 模型 | 日志 |
| dic_gan_x8c48b6_g4_500k_CelebAHQ | x8 | 23.6241 / 0.6721 | 4 (Tesla PG503-216) | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py
## 单个GPU上训练
python tools/train.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py
## 多个GPU上训练
./tools/dist_train.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth
## 单个GPU上测试
python tools/test.py configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth
## 多个GPU上测试
./tools/dist_test.sh configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
RDN (CVPR’2018)¶
任务: 图像超分辨率
RDN (CVPR'2018)
@inproceedings{zhang2018residual,
title={Residual dense network for image super-resolution},
author={Zhang, Yulun and Tian, Yapeng and Kong, Yu and Zhong, Bineng and Fu, Yun},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={2472--2481},
year={2018}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| rdn_x2c64b16_g1_1000k_div2k | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | 1 (TITAN Xp) | 模型 | 日志 |
| rdn_x3c64b16_g1_1000k_div2k | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | 1 (TITAN Xp) | 模型 | 日志 |
| rdn_x4c64b16_g1_1000k_div2k | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | 1 (TITAN Xp) | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py
## 单个GPU上训练
python tools/train.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth
## 单个GPU上测试
python tools/test.py configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth
## 多个GPU上测试
./tools/dist_test.sh configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
ESRGAN (ECCVW’2018)¶
任务: 图像超分辨率
ESRGAN (ECCVW'2018)
@inproceedings{wang2018esrgan,
title={Esrgan: Enhanced super-resolution generative adversarial networks},
author={Wang, Xintao and Yu, Ke and Wu, Shixiang and Gu, Jinjin and Liu, Yihao and Dong, Chao and Qiao, Yu and Change Loy, Chen},
booktitle={Proceedings of the European Conference on Computer Vision Workshops(ECCVW)},
pages={0--0},
year={2018}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| esrgan_psnr_x4c64b23g32_1x16_1000k_div2k | 30.6428 / 0.8559 | 27.0543 / 0.7447 | 29.3354 / 0.8263 | 1 | 模型 | 日志 |
| esrgan_x4c64b23g32_1x16_400k_div2k | 28.2700 / 0.7778 | 24.6328 / 0.6491 | 26.6531 / 0.7340 | 1 | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py
## 单个GPU上训练
python tools/train.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth
## 单个GPU上测试
python tools/test.py configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth
## 多个GPU上测试
./tools/dist_test.sh configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
EDSR (CVPR’2017)¶
任务: 图像超分辨率
EDSR (CVPR'2017)
@inproceedings{lim2017enhanced,
title={Enhanced deep residual networks for single image super-resolution},
author={Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Mu Lee, Kyoung},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages={136--144},
year={2017}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| edsr_x2c64b16_1x16_300k_div2k | 35.7592 / 0.9372 | 31.4290 / 0.8874 | 34.5896 / 0.9352 | 1 | 模型 | 日志 |
| edsr_x3c64b16_1x16_300k_div2k | 32.3301 / 0.8912 | 28.4125 / 0.8022 | 30.9154 / 0.8711 | 1 | 模型 | 日志 |
| edsr_x4c64b16_1x16_300k_div2k | 30.2223 / 0.8500 | 26.7870 / 0.7366 | 28.9675 / 0.8172 | 1 | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py
## 单个GPU上训练
python tools/train.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth
## 单个GPU上测试
python tools/test.py configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth
## 多个GPU上测试
./tools/dist_test.sh configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
SRGAN (CVPR’2016)¶
任务: 图像超分辨率
SRGAN (CVPR'2016)
@inproceedings{ledig2016photo,
title={Photo-realistic single image super-resolution using a generative adversarial network},
author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
year={2016}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| msrresnet_x4c64b16_1x16_300k_div2k | 30.2252 / 0.8491 | 26.7762 / 0.7369 | 28.9748 / 0.8178 | 1 | 模型 | 日志 |
| srgan_x4c64b16_1x16_1000k_div2k | 27.9499 / 0.7846 | 24.7383 / 0.6491 | 26.5697 / 0.7365 | 1 | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py
## 单个GPU上训练
python tools/train.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth
## 单个GPU上测试
python tools/test.py configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth
## 多个GPU上测试
./tools/dist_test.sh configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。
SRCNN (TPAMI’2015)¶
任务: 图像超分辨率
SRCNN (TPAMI'2015)
@article{dong2015image,
title={Image super-resolution using deep convolutional networks},
author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
journal={IEEE transactions on pattern analysis and machine intelligence},
volume={38},
number={2},
pages={295--307},
year={2015},
publisher={IEEE}
}
在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 scale 像素。
我们使用 PSNR 和 SSIM 作为指标。
| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 |
|---|---|---|---|---|---|
| srcnn_x4k915_1x16_1000k_div2k | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | 1 | 模型 | 日志 |
快速开始¶
训练
训练说明
您可以使用以下命令来训练模型。
## CPU上训练
CUDA_VISIBLE_DEVICES=-1 python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py
## 单个GPU上训练
python tools/train.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py
## 多个GPU上训练
./tools/dist_train.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py 8
更多细节可以参考 train_test.md 中的 Train a model 部分。
测试
测试说明
您可以使用以下命令来测试模型。
## CPU上测试
CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth
## 单个GPU上测试
python tools/test.py configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth
## 多个GPU上测试
./tools/dist_test.sh configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 8
更多细节可以参考 train_test.md 中的 Test a pre-trained model 部分。