Update README.md

README.md

@@ -5,7 +5,7 @@ license: cc-by-4.0
 
 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.    
 
-It consists of 390'241 images, which are all 512x512px dimensions and in the webp format. 
+It consists of 390'035 images, which are all 512x512px dimensions and in the webp format. 
 
  <figure>
   <img src="https://cdn-uploads.huggingface.co/production/uploads/634e9aa407e669188d3912f9/bV0oaFKJzdsEqRme_lqU8.png" alt="48 first training tiles">
@@ -18,7 +18,7 @@ Though if an on-the-fly degradation pipeline is used during training, such a hig
 Size on disc: 
 ```
 du BHI_HR  
-131199816	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.  
@@ -95,7 +95,7 @@ Well, JPEG XL would be the absolute best modern option for lossless compression,
 
 TODO put paper stuff etc in here about webp / jpeg xl being superior concerning lossless compression
 
-(Note to myself: Tiles 'inaturalist_2019_65228.png','inaturalist_2019_54615.png','inaturalist_2019_22816.png' removed because of PNG error when checking with [pngcheck](http://www.libpng.org/pub/png/apps/pngcheck.html))
+See file list for all the files in the dataset
 
 ## Upload