README add datasets download links (XPixelGroup#318)

* add dataset download

* update readme

* update readme

* update readme

Author: xinntao

README.md

@@ -3,8 +3,9 @@
 [English](README.md) **|** [简体中文](README_CN.md)   [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)
 
 <a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
-: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)
+:m: [Model Zoo](docs/ModelZoo.md) :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)
 :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) <br>
+:file_folder: [Datasets](docs/DatasetPreparation.md) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/baidutodo) (提取码:wdix)<br>
 :chart_with_upwards_trend: [Training curves in wandb](https://app.wandb.ai/xintao/basicsr) <br>
 :computer: [Commands for training and testing](docs/TrainTest.md) <br>
 :zap: [HOWTOs](#zap-howtos)

README_CN.md

@@ -3,8 +3,9 @@
 [English](README.md) **|** [简体中文](README_CN.md)   [GitHub](https://github.com/xinntao/BasicSR) **|** [Gitee码云](https://gitee.com/xinntao/BasicSR)
 
 <a href="https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" height="18" alt="google colab logo"></a> Google Colab: [GitHub Link](colab) **|** [Google Drive Link](https://drive.google.com/drive/folders/1G_qcpvkT5ixmw5XoN6MupkOzcK1km625?usp=sharing) <br>
-: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)
+:m: [模型库](docs/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) <br>
+:file_folder: [数据](docs/DatasetPreparation_CN.md) :arrow_double_down: [百度网盘](https://pan.baidu.com/s/baidutodo) (提取码:wdix) :arrow_double_down: [Google Drive](https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing) <br>
 :chart_with_upwards_trend: [wandb的训练曲线](https://app.wandb.ai/xintao/basicsr) <br>
 :computer: [训练和测试的命令](docs/TrainTest_CN.md) <br>
 :zap: [HOWTOs](#zap-howtos)

docs/DatasetPreparation.md

@@ -182,7 +182,7 @@ We provide a list of common image super-resolution datasets.
     <td rowspan="3">Classical SR Training</td>
     <td>T91</td>
     <td><sub>91 images for training</sub></td>
-    <td rowspan="9"><a href="https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU?usp=sharing">Google Drive</a> / <a href="https://pan.baidu.com/s/1q_1ERCMqALH0xFwjLM0pTg">Baidu Drive</a></td>
+    <td rowspan="9"><a href="https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing">Google Drive</a> / <a href="https://pan.baidu.com/s/1q_1ERCMqALH0xFwjLM0pTg">Baidu Drive</a></td>
   </tr>
  <tr>
     <td><a href="https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">BSDS200</a></td>

docs/DatasetPreparation_CN.md

@@ -182,7 +182,7 @@ DIV2K 数据集被广泛使用在图像复原的任务中.
     <td rowspan="3">Classical SR Training</td>
     <td>T91</td>
     <td><sub>91 images for training</sub></td>
-    <td rowspan="9"><a href="https://drive.google.com/drive/folders/1pRmhEmmY-tPF7uH8DuVthfHoApZWJ1QU?usp=sharing">Google Drive</a> / <a href="https://pan.baidu.com/s/1q_1ERCMqALH0xFwjLM0pTg">Baidu Drive</a></td>
+    <td rowspan="9"><a href="https://drive.google.com/drive/folders/1gt5eT293esqY0yr1Anbm36EdnxWW_5oH?usp=sharing">Google Drive</a> / <a href="https://pan.baidu.com/s/1q_1ERCMqALH0xFwjLM0pTg">Baidu Drive</a></td>
   </tr>
  <tr>
     <td><a href="https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/">BSDS200</a></td>

docs/ModelZoo.md

@@ -2,14 +2,19 @@
 
 [English](ModelZoo.md) **|** [简体中文](ModelZoo_CN.md)
 
+: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)
+: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)
+
+---
+
 We provide:
 
 1. Official models converted directly from official released models
 1. Reproduced models with `BasicSR`. Pre-trained models and log examples are provided
 
 You can put the downloaded models in the `experiments/pretrained_models` folder.
 
-**[Download official pre-trained models]** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g))(https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing))
+**[Download official pre-trained models]** ([Google Drive](https://drive.google.com/drive/folders/15DgDtfaLASQ3iAPJEVHQF49g9msexECG?usp=sharing), [百度网盘](https://pan.baidu.com/s/1R6Nc4v3cl79XPAiK0Toe7g))
 
 You can use the scrip to download pre-trained models from Google Drive.
 
@@ -93,7 +98,6 @@ EDVR\_(training dataset)\_(track name)\_(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.
 
-[Download Models from Google Drive](https://drive.google.com/open?id=1WfROVUqKOBS5gGvQzBfU1DNZ4XwPA3LD)
 
 | Model name |[Test Set] PSNR/SSIM |
 |:----------:|:----------:|
@@ -107,7 +111,6 @@ EDVR\_(training dataset)\_(track name)\_(model complexity)
 <sup>1</sup> Y or RGB denotes the evaluation on Y (luminance) or RGB channels.
 
 #### Stage 2 models for the NTIRE19 Competition
-[Download Models from Google Drive](https://drive.google.com/drive/folders/1PMoy1cKlIYWly6zY0tG2Q4YAH7V_HZns?usp=sharing)
 
 | Model name |[Test Set] PSNR/SSIM |
 |:----------:|:----------:|
@@ -119,7 +122,6 @@ EDVR\_(training dataset)\_(track name)\_(model complexity)
 
 ## DUF
 The models are converted from the [officially released models](https://github.com/yhjo09/VSR-DUF). <br/>
-[Download Models from Google Drive](https://drive.google.com/open?id=1seY9nclMuwk_SpqKQhx1ItTcQShM5R50)
 
 | Model name | [Test Set] PSNR/SSIM<sup>1</sup> | Official Results<sup>2</sup> |
 |:----------:|:----------:|:----------:|
@@ -136,7 +138,6 @@ The models are converted from the [officially released models](https://github.co
 
 ## TOF
 The models are converted from the [officially released models](https://github.com/anchen1011/toflow).<br/>
-[Download Models from Google Drive](https://drive.google.com/open?id=18kJcxPLeNK8e0kYEiwmsnu9wVmhdMFFG)
 
 | Model name | [Test Set] PSNR/SSIM | Official Results<sup>1</sup> |
 |:----------:|:----------:|:----------:|

docs/ModelZoo_CN.md

@@ -2,6 +2,11 @@
 
 [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
@@ -92,8 +97,6 @@ EDVR\_(training dataset)\_(track name)\_(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.
 
-[Download Models from Google Drive](https://drive.google.com/open?id=1WfROVUqKOBS5gGvQzBfU1DNZ4XwPA3LD)
-
 | Model name |[Test Set] PSNR/SSIM |
 |:----------:|:----------:|
 | EDVR_Vimeo90K_SR_L | [Vid4] (Y<sup>1</sup>) 27.35/0.8264 [[↓Results]](https://drive.google.com/open?id=14nozpSfe9kC12dVuJ9mspQH5ZqE4mT9K)<br/> (RGB) 25.83/0.8077|
@@ -106,7 +109,6 @@ EDVR\_(training dataset)\_(track name)\_(model complexity)
 <sup>1</sup> Y or RGB denotes the evaluation on Y (luminance) or RGB channels.
 
 #### Stage 2 models for the NTIRE19 Competition
-[Download Models from Google Drive](https://drive.google.com/drive/folders/1PMoy1cKlIYWly6zY0tG2Q4YAH7V_HZns?usp=sharing)
 
 | Model name |[Test Set] PSNR/SSIM |
 |:----------:|:----------:|
@@ -118,7 +120,6 @@ EDVR\_(training dataset)\_(track name)\_(model complexity)
 
 ## DUF
 The models are converted from the [officially released models](https://github.com/yhjo09/VSR-DUF). <br/>
-[Download Models from Google Drive](https://drive.google.com/open?id=1seY9nclMuwk_SpqKQhx1ItTcQShM5R50)
 
 | Model name | [Test Set] PSNR/SSIM<sup>1</sup> | Official Results<sup>2</sup> |
 |:----------:|:----------:|:----------:|
@@ -135,7 +136,6 @@ The models are converted from the [officially released models](https://github.co
 
 ## TOF
 The models are converted from the [officially released models](https://github.com/anchen1011/toflow).<br/>
-[Download Models from Google Drive](https://drive.google.com/open?id=18kJcxPLeNK8e0kYEiwmsnu9wVmhdMFFG)
 
 | Model name | [Test Set] PSNR/SSIM | Official Results<sup>1</sup> |
 |:----------:|:----------:|:----------:|