Add eval notebook.

README.md

@@ -56,7 +56,12 @@ Low Resolution (LR) images are created by using bicubic interpolation as the res
 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. 
 
-The following code provides some helper functions to get the data and preprocess/augment the data.
+We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
+```bash
+pip install datasets
+```
+The following code gets the data and preprocesses/augments the data.
+
 ```python
 from datasets import load_dataset
 
@@ -117,6 +122,10 @@ The results columns below are represented below as `PSNR/SSIM`. They are compare
 
 ![Comparing Bicubic upscaling against x2 upscaling on Set5 Image 2](images/Set5_2_compare.png "Comparing Bicubic upscaling against x2 upscaling on Set5 Image 2")
 
+You can find a notebook to easily run evaluation on pretrained models below:
+
+[![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/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
+
 ## BibTeX entry and citation info
 ```bibtex
 @InProceedings{Lim_2017_CVPR_Workshops,