# 模型库和基准 [English](ModelZoo.md) **|** [简体中文](ModelZoo_CN.md) :arrow_double_down: 百度网盘: [预训练模型](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g) **|** [复现实验](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ) :arrow_double_down: Google Drive: [Pretrained Models](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing) **|** [Reproduced Experiments](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing) --- 我们提供了: 1. 官方的模型, 它们是从官方release的models直接转化过来的 1. 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](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g)) 你可以使用以下脚本从Google Drive下载预训练模型. ```python 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](https://drive.google.com/drive/folders/1XN4WXKJ53KQ0Cu0Yv-uCt8DZWq6uufaP?usp=sharing), [百度网盘](https://pan.baidu.com/s/1UElD6q8sVAgn_cxeBDOlvQ)) 此外, 我们在 [wandb](https://www.wandb.com/) 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](https://app.wandb.ai/xintao/basicsr)**

#### 目录 1. [图像超分辨率](#图像超分辨率) 1. [图像超分官方模型](#图像超分官方模型) 1. [图像超分复现模型](#图像超分复现模型) 1. [视频超分辨率](#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](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]](https://drive.google.com/open?id=14nozpSfe9kC12dVuJ9mspQH5ZqE4mT9K)
(RGB) 25.83/0.8077| | EDVR_REDS_SR_M | [REDS] (RGB) 30.53/0.8699 [[↓Results]](https://drive.google.com/open?id=1Mek3JIxkjJWjhZhH4qVwTXnRZutKUtC-)| | EDVR_REDS_SR_L | [REDS] (RGB) 31.09/0.8800 [[↓Results]](https://drive.google.com/open?id=1h6E0QVZyJ5SBkcnYaT1puxYYPVbPsTLt)| | EDVR_REDS_SRblur_L | [REDS] (RGB) 28.88/0.8361 [[↓Results]](https://drive.google.com/open?id=1-8MNkQuMVMz30UilB9m_d0SXicwFEPZH)| | EDVR_REDS_deblur_L | [REDS] (RGB) 34.80/0.9487 [[↓Results]](https://drive.google.com/open?id=133wCHTwiiRzenOEoStNbFuZlCX8Jn2at)| | EDVR_REDS_deblurcomp_L | [REDS] (RGB) 30.24/0.8567 [[↓Results]](https://drive.google.com/open?id=1VjC4fXBXy0uxI8Kwxh-ijj4PZkfsLuTX) | 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](https://github.com/yhjo09/VSR-DUF).
| Model name | [Test Set] PSNR/SSIM1 | Official Results2 | |:----------:|:----------:|:----------:| | DUF_x4_52L_official3 | [Vid4] (Y4) 27.33/0.8319 [[↓Results]](https://drive.google.com/open?id=1U9xGhlDSpPPQvKN0BAzXfjUCvaFxwsQf)
(RGB) 25.80/0.8138 | (Y) 27.33/0.8318 [[↓Results]](https://drive.google.com/open?id=1HUmf__cSL7td7J4cXo2wvbVb14Y8YG2j)
(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](https://github.com/anchen1011/toflow).
| Model name | [Test Set] PSNR/SSIM | Official Results1 | |:----------:|:----------:|:----------:| | TOF_official2 | [Vid4] (Y3) 25.86/0.7626 [[↓Results]](https://drive.google.com/open?id=1Xp5U6uZeM44ShzawfuW_E-NmQ30hk-Be)
(RGB) 24.38/0.7403 | (Y) 25.89/0.7651 [[↓Results]](https://drive.google.com/open?id=1WY3CcdzbRhpvDi3aGc1jAhIbeC6GUrM8)
(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.