Update README.md

README.md

@@ -3,10 +3,20 @@ license: cc-by-4.0
 ---
 # BHI SISR Dataset
 
-The BHI SISR Dataset's purpose is for training single image super-resolution models and is a result of tests on my BHI filtering method, which I made [a huggingface community blogpost about](https://huggingface.co/blog/Phips/bhi-filtering).  
+The BHI SISR Dataset's purpose is for training single image super-resolution models and is a result of tests on my BHI filtering method, which I made [a huggingface community blogpost about](https://huggingface.co/blog/Phips/bhi-filtering), which can be extremely summarized by that removing (by filtering) only the worst quality tiles from a training set has a way bigger positive effect on training metrics than keeping only the best quality training tiles.    
 
-TODO it consists of X images, which are all 512x512px dimensions and in the png format. 
-TODO visual example of the dataset  
+It consists of 390'241 images, which are all 512x512px dimensions and in the webp format.  
+
+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.  
+
+Size on disc: 
+```
+du BHI_HR  
+131199816	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.  
 
 ## Used Datasets
 
@@ -66,7 +76,7 @@ HFA2K -> 2'280 Tiles
 ModernAnimation1080_v3 -> 4'109 Tiles  
 Nomos_Uni -> 2'466 Tiles  
 Nomosv2 -> 5'226 Tiles  
-inaturalist_2019 -> 131'943 Tiles
+inaturalist_2019 -> 131'943 Tiles  
 
 
 ## Files
@@ -77,4 +87,8 @@ Files have been named with '{dataset_name}_{index}.png' so that if one of these
 
 ## Optimization
 
-Then I used [oxipng](https://github.com/shssoichiro/oxipng) ("oxipng --strip safe --alpha *.png") for optimization.
+Then I used [oxipng](https://github.com/shssoichiro/oxipng) ("oxipng --strip safe --alpha *.png") for optimization.  
+
+## WebP conversion
+
+The files have then been converted to lossless webp simply to save storage space locally and for faster uploading/downloading here on huggingface. This reduced size of the dataset by around 50 GB.