模型库和基准
English | 简体中文
⏬ 百度网盘: 预训练模型 | 复现实验
⏬ Google Drive: Pretrained Models | Reproduced Experiments
我们提供了:
- 官方的模型, 它们是从官方release的models直接转化过来的
- 复现的模型, 使用
BasicSR
的框架复现的, 提供模型和log的例子
下载的模型可以放在 experiments/pretrained_models
文件夹.
[下载官方提供的预训练模型] (Google Drive, 百度网盘)
你可以使用以下脚本从Google Drive下载预训练模型.
python scripts/download_pretrained_models.py ESRGAN
# method can be ESRGAN, EDVR, StyleGAN, EDSR, DUF, DFDNet, dlib
[下载复现的模型和log] (Google Drive, 百度网盘)
此外, 我们在 wandb 上更新了模型训练的过程和曲线. 大家可以方便的比较:
wandb训练曲线
目录
- 图像超分辨率
- 图像超分官方模型
- 图像超分复现模型
- 视频超分辨率
图像超分辨率
在计算指标时:
- 所有的图像各条边crop了scale的像素
- 都在RGB通道上测试
图像超分官方模型
Exp Name |
Set5 (PSNR/SSIM) |
Set14 (PSNR/SSIM) |
DIV2K100 (PSNR/SSIM) |
EDSR_Mx2_f64b16_DIV2K_official-3ba7b086 |
35.7768 / 0.9442 |
31.4966 / 0.8939 |
34.6291 / 0.9373 |
EDSR_Mx3_f64b16_DIV2K_official-6908f88a |
32.3597 / 0.903 |
28.3932 / 0.8096 |
30.9438 / 0.8737 |
EDSR_Mx4_f64b16_DIV2K_official-0c287733 |
30.1821 / 0.8641 |
26.7528 / 0.7432 |
28.9679 / 0.8183 |
EDSR_Lx2_f256b32_DIV2K_official-be38e77d |
35.9979 / 0.9454 |
31.8583 / 0.8971 |
35.0495 / 0.9407 |
EDSR_Lx3_f256b32_DIV2K_official-3660f70d |
32.643 / 0.906 |
28.644 / 0.8152 |
31.28 / 0.8798 |
EDSR_Lx4_f256b32_DIV2K_official-76ee1c8f |
30.5499 / 0.8701 |
27.0011 / 0.7509 |
29.277 / 0.8266 |
图像超分复现模型
实验名称的命名规则参见 Config_CN.md.
Exp Name |
Set5 (PSNR/SSIM) |
Set14 (PSNR/SSIM) |
DIV2K100 (PSNR/SSIM) |
001_MSRResNet_x4_f64b16_DIV2K_1000k_B16G1_wandb |
30.2468 / 0.8651 |
26.7817 / 0.7451 |
28.9967 / 0.8195 |
002_MSRResNet_x2_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb |
35.7483 / 0.9442 |
31.5403 / 0.8937 |
34.6699 / 0.9377 |
003_MSRResNet_x3_f64b16_DIV2K_1000k_B16G1_001pretrain_wandb |
32.4038 / 0.9032 |
28.4418 / 0.8106 |
30.9726 / 0.8743 |
004_MSRGAN_x4_f64b16_DIV2K_400k_B16G1_wandb |
28.0158 / 0.8087 |
24.7474 / 0.6623 |
26.6504 / 0.7462 |
|
|
|
|
201_EDSR_Mx2_f64b16_DIV2K_300k_B16G1_wandb |
35.7395 / 0.944 |
31.4348 / 0.8934 |
34.5798 / 0.937 |
202_EDSR_Mx3_f64b16_DIV2K_300k_B16G1_201pretrain_wandb |
32.315 / 0.9026 |
28.3866 / 0.8088 |
30.9095 / 0.8731 |
203_EDSR_Mx4_f64b16_DIV2K_300k_B16G1_201pretrain_wandb |
30.1726 / 0.8641 |
26.721 / 0.743 |
28.9506 / 0.818 |
204_EDSR_Lx2_f256b32_DIV2K_300k_B16G1_wandb |
35.9792 / 0.9453 |
31.7284 / 0.8959 |
34.9544 / 0.9399 |
205_EDSR_Lx3_f256b32_DIV2K_300k_B16G1_204pretrain_wandb |
32.6467 / 0.9057 |
28.6859 / 0.8152 |
31.2664 / 0.8793 |
206_EDSR_Lx4_f256b32_DIV2K_300k_B16G1_204pretrain_wandb |
30.4718 / 0.8695 |
26.9616 / 0.7502 |
29.2621 / 0.8265 |
视频超分辨率
Evaluation
In the evaluation, we include all the input frames and do not crop any border pixels unless otherwise stated.
We do not use the self-ensemble (flip testing) strategy and any other post-processing methods.
EDVR
Name convention
EDVR_(training dataset)_(track name)_(model complexity)
- track name. There are four tracks in the NTIRE 2019 Challenges on Video Restoration and Enhancement:
- SR: super-resolution with a fixed downsampling kernel (MATLAB bicubic downsampling kernel is frequently used). Most of the previous video SR methods focus on this setting.
- SRblur: the inputs are also degraded with motion blur.
- deblur: standard deblurring (motion blur).
- deblurcomp: motion blur + video compression artifacts.
- model complexity
- L (Large): # of channels = 128, # of back residual blocks = 40. This setting is used in our competition submission.
- M (Moderate): # of channels = 64, # of back residual blocks = 10.
Model name |
[Test Set] PSNR/SSIM |
EDVR_Vimeo90K_SR_L |
[Vid4] (Y1) 27.35/0.8264 [↓Results] (RGB) 25.83/0.8077 |
EDVR_REDS_SR_M |
[REDS] (RGB) 30.53/0.8699 [↓Results] |
EDVR_REDS_SR_L |
[REDS] (RGB) 31.09/0.8800 [↓Results] |
EDVR_REDS_SRblur_L |
[REDS] (RGB) 28.88/0.8361 [↓Results] |
EDVR_REDS_deblur_L |
[REDS] (RGB) 34.80/0.9487 [↓Results] |
EDVR_REDS_deblurcomp_L |
[REDS] (RGB) 30.24/0.8567 [↓Results] |
1 Y or RGB denotes the evaluation on Y (luminance) or RGB channels.
Stage 2 models for the NTIRE19 Competition
Model name |
[Test Set] PSNR/SSIM |
EDVR_REDS_SR_Stage2 |
[REDS] (RGB) / [↓Results] |
EDVR_REDS_SRblur_Stage2 |
[REDS] (RGB) / [↓Results] |
EDVR_REDS_deblur_Stage2 |
[REDS] (RGB) / [↓Results] |
EDVR_REDS_deblurcomp_Stage2 |
[REDS] (RGB) / [↓Results] |
DUF
The models are converted from the officially released models.
Model name |
[Test Set] PSNR/SSIM1 |
Official Results2 |
DUF_x4_52L_official3 |
[Vid4] (Y4) 27.33/0.8319 [↓Results] (RGB) 25.80/0.8138 |
(Y) 27.33/0.8318 [↓Results] (RGB) 25.79/0.8136 |
DUF_x4_28L_official |
[Vid4] |
|
DUF_x4_16L_official |
[Vid4] |
|
DUF_x3_16L_official |
[Vid4] |
|
DUF_x2_16L_official |
[Vid4] |
|
1 We crop eight pixels near image boundary for DUF due to its severe boundary effects.
2 The official results are obtained by running the official codes and models.
3 Different from the official codes, where zero padding
is used for border frames, we use new_info
strategy.
4 Y or RGB denotes the evaluation on Y (luminance) or RGB channels.
TOF
The models are converted from the officially released models.
Model name |
[Test Set] PSNR/SSIM |
Official Results1 |
TOF_official2 |
[Vid4] (Y3) 25.86/0.7626 [↓Results] (RGB) 24.38/0.7403 |
(Y) 25.89/0.7651 [↓Results] (RGB) 24.41/0.7428 |
1 The official results are obtained by running the official codes and models. Note that TOFlow does not provide a strategy for border frame recovery and we simply use a replicate
strategy for border frames.
2 The converted model has slightly different results, due to different implementation. And we use new_info
strategy for border frames.
3 Y or RGB denotes the evaluation on Y (luminance) or RGB channels.