Add update README with generalised fixes.

README.md

@@ -24,7 +24,7 @@ EDSR is a model that uses both deeper and wider architecture (32 ResBlocks and 2
 
 This is a base model (~5mb vs ~100mb) that includes just 16 ResBlocks and 64 channels.
 ## Intended uses & limitations
-You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train an EDSR model on your own dataset.
+You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
 ### How to use
 The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
 ```bash
@@ -48,7 +48,7 @@ ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')      # save a
 ```
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
 ## Training data
-The EDSR models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/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). 
+The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/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).
@@ -101,10 +101,10 @@ trainer.train()
 The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm). 
 
 Evaluation datasets include:
-- Set5 - [Bevilacqua et al. (2012)](http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html)
-- Set14 - [Zeyde et al. (2010)](https://sites.google.com/site/romanzeyde/research-interests)
-- BSD100 - [Martin et al. (2001)](https://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)
-- Urban100 - [Huang et al. (2015)](https://sites.google.com/site/jbhuang0604/publications/struct_sr)
+- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
+- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
+- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
+- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
 
 The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.