Update ReadMe

README.md

@@ -36,6 +36,13 @@ Second, we considered geolocation (center latitude and longitude) and date of ac
 
 The models were pre-trained at the Jülich Supercomputing Centre 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
+We validated the Prithvi-EO-2.0 models through extensive experiments using [GEO-bench](https://github.com/ServiceNow/geo-bench). 
+While Prithvi-EO-2.0-tiny-TL performs lower than it's bigger counterparts (model params visualized by point size), it is much faster and needs less compute indicated by the GFLOPs.
+The model is small enough to run on edge devices like satellites and smartphones.  
+
+![Prithvi_evaluation.png](assets/Prithvi_evaluation.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). 
 
@@ -47,7 +54,7 @@ python inference.py --data_files t1.tif t2.tif t3.tif t4.tif --input_indices <op
 
 ## Finetuning
 
-You can finetune the model using [TerraTorch](https://github.com/IBM/terratorch). Examples of configs and notebooks are provided in the project repository: [github.com/NASA-IMPACT/Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0#fine-tuning).  
+You can finetune the model using [TerraTorch](https://github.com/IBM/terratorch) (`terratorch>=1.1` required). Examples of configs and notebooks are provided in the project repository: [github.com/NASA-IMPACT/Prithvi-EO-2.0](https://github.com/NASA-IMPACT/Prithvi-EO-2.0#fine-tuning).  
 Example Notebooks:
 
 [Multitemporal Crop Segmentation](https://github.com/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb)   [<b><i>>>Try it on Colab<<</i></b>](https://colab.research.google.com/github/NASA-IMPACT/Prithvi-EO-2.0/blob/main/examples/example_multitemporalcrop.ipynb) (Choose T4 GPU runtime)