add benchmarking and remove logos

README.md

@@ -25,6 +25,10 @@ Our main modifications to the ViT architecture are the 3D positional embedding a
 
 The models were pre-trained at the Julich Supercomputing Center with NASA's HLS V2 product (30m granularity) using 4.2M samples with six bands in the following order: Blue, Green, Red, Narrow NIR, SWIR, SWIR 2.
 
+## Benchmarking
+The model was benchmarked on GEO-Bench across 12 different earth observation classification and segmentation tasks at different resolutions against some of the most popular geospatial foundation models. Below the average score across all GEo-Bench tasks is shown.
+
+![benchmarking](assets/Overall_300M_TL.png)
 
 ## Demo and inference
 We provide a **demo** running Prithvi-EO-2.0-300M-TL [here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-EO-2.0-Demo). 
@@ -55,7 +59,3 @@ If this model helped your research, please cite `Prithvi-EO-2.0` in your publica
     year            = {2024}
 }
 ```
-
-### Partners
-
-![logos.png](assets/logos.png)