Add super-image tag.

README.md

@@ -1,6 +1,7 @@
 ---
 license: apache-2.0
 tags:
+- super-image
 - image-super-resolution
 datasets:
 - div2k
@@ -9,7 +10,7 @@ metrics:
 - ssim
 ---
 # Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
-EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch). 
+EDSR model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/abs/1707.02921) by Lim et al. (2017) and first released in [this repository](https://github.com/sanghyun-son/EDSR-PyTorch). 
 
 The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and EDSR upscaling x2.
 
@@ -45,7 +46,7 @@ ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')      # save a
 The EDSR models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of  100 validation images (images numbered 801 to 900). 
 ## Training procedure
 ### Preprocessing
-We follow the pre-processing and training method of [Wang et. al](https://arxiv.org/abs/2104.07566).
+We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
 Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
 During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. 
 Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.