Update README.md

README.md

@@ -109,7 +109,7 @@ Some tiles have been filtered in a later step, so dont worry if some index numbe
 Also all scores can be found in the [scores folder](https://huggingface.co/datasets/Phips/BHI/tree/main/scores).
 
 I did convert to webp because of file size reduction, because the dataset was originally at around 200GB, when I then used oxipng ("oxipng --strip safe --alpha *.png") for optimization. But lossless webp is just the best option available currently for lossless file size reduction.
-(JPEGXL is not supported by cv2 for training yet. WebP2 is experimental. FLIF was discontinued for JPEGXL.)
+(JPEGXL is not supported by cv2 for training yet. WebP2 is experimental. FLIF was discontinued for JPEGXL.)  
 
  <figure>
   <img src="https://cdn-uploads.huggingface.co/production/uploads/634e9aa407e669188d3912f9/BgkkzkhZQBrXY0qTxR_rm.png" alt="Lossless image formats">
@@ -124,10 +124,21 @@ This should work with lfs file size limit, and i chose zip because its such a co
 
 ## Corresponding LR Sets
 
-In most cases, only the HR part, meaning the part published here, is needed. LR sets, like a bicubic only downsampled counterpart for trainig 2x or 4x models can very simply be generated by the user.
-However, I thought i would provide some prebuilt LR sets, which are ones I used to train models myself. The resulting models can of course be downloaded and tried out.
+In most cases, only the HR part, meaning the part published here, is needed since LR sets, like a bicubic only downsampled counterpart for trainig 2x or 4x models can very simply be generated by the user.  
+Also, if a degradation pipeline like the real-esrgan otf pipeline is used, only this HR set is needed, since it degrades images during training itself.  
+However, I thought i would provide some prebuilt LR sets for paired training, which are ones I used to train models myself. The resulting models can of course be downloaded and tried out.  
 See links for degradation details and download (separate dataset pages)  
 
 [BHI_LR_multi](https://huggingface.co/datasets/Phips/BHI_LR_multi) was made by using multiple different downsampling/scaling algos.  
 [BHI_LR_multiblur](https://huggingface.co/datasets/Phips/BHI_LR_multiblur) as above, but also added blur for deblurring/sharper results plus added both jpg and webp compression for compression handling.  
-[BHI_LR_real](https://huggingface.co/datasets/Phips/BHI_LR_real) This is my attempt at a real degraded dataset for the trained upscaling model to handle images downloaded from the web.  
+[BHI_LR_real](https://huggingface.co/datasets/Phips/BHI_LR_real) This is my attempt at a real degraded dataset for the trained upscaling model to handle images downloaded from the web.  
+
+## Trained Models
+
+I also provide sisr models I trained on this dataset when either using the real-esrgan otf pipeline or then prebuilt LR sets for paired training, which are the exact sets I released above.  
+These models are based on the realplksr arch (middle sized arch) and on the dat arch (big arch, slower but better quality). There are of course other options I could have gone with, but I might still release other models on this dataset in the future.  
+
+Multiscale: RealPLKSR // only non-degraded input 
+Multiblur: RealPLKSR | DAT2 // handles slight blur and compression  
+OTF(real-esrgan pipeline): RealPLKSR | DAT2 // handles blur, noise, and compression  
+Real: RealPLKSR | DAT2 // handles blur, noise, and compression