Update README.md

README.md

@@ -12,8 +12,7 @@ It consists of 390'035 images, which are all 512x512px dimensions and in the web
   <figcaption>Visual example - the first 48 training tiles</figcaption>
 </figure> 
 
-The advantage of such a big dataset is when applying degradations in a randomized manner to create a corresponding LR, the distribution of degradations and strenghts should be sufficient because of the quantity of training tiles. I will create some corresponding x4 LR datasets to this one and publish them aswell.  
-Though if an on-the-fly degradation pipeline is used during training, such a high quantity of training tiles would probably generally not be needed since longer training iterations make sure of distribution.  
+The advantage of such a big dataset is when applying degradations in a randomized manner to create a corresponding LR for paired sisr training, the distribution of degradations and strenghts should be sufficient because of the quantity of training tiles. I will create some corresponding x4 LR datasets to this one and publish them aswell.  
 
 Size on disc: 
 ```
@@ -21,7 +20,14 @@ du BHI_HR
 131148100	BHI_HR/  
 ```
 
-Also for the future, I am releasing the full dataset here. But there can of course be attempts in the future to make distilled versions of this dataset that perform better since I might find additional metrics or filtering methods in the future that might help reduce dataset size while achieving better training validation metric performance.  
+Also for the future, I am releasing the full dataset here. But there can of course be (community?) attempts in the future to make distilled versions of this dataset that perform better since I might find additional metrics or filtering methods in the future that might help reduce dataset size while achieving better training validation metric performance.  
+
+In Summary:
+
+Advantage of this dataset is large quantity of normalized (512x512) tiles
+- When applying degradations to create a corresponding LR, the distribution of degradation strengths should be sufficient, even when using multiple degradations.
+- Big arch options in general can profit from the amount of learning content in this dataset.
+- Since it takes a while to reach a new epoch, higher training iters is advised for big arch options to profit thereof. The filtering method used here made sure that metrics would not worsen during training.
 
 ## Used Datasets