Update README.md

README.md

@@ -16,7 +16,7 @@ MaxVit-T pre-trained on ImageNet-1K was used and a full fine-tuning of the pre-t
   - Viel-Falter Butterfly Monitoring which is financially supported by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK).
   - EuroCC Austria which has received funding from the European High Performance Computing Joint Undertaking (JU) and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia under grant agreement No 101101903.
 - **License:** MIT
-- **Finetuned from model:** MaxVit-T pre-trained on ImageNet-1K
+- **Finetuned from model:** MaxViT-T pre-trained on ImageNet-1K
 
 ## Uses
 
@@ -29,7 +29,7 @@ it were only 31. Not all butterfly and moth species can be determined based on i
 
 ## Training Details
 
-The model was trained on the EuroHPC supercomputers LUMI, hosted by CSC (Finland) and the LUMI consortium through a EuroHPC Regular Access call and LEONARDO, hosted by CINECA (Italy) and the LEONARDO consortium through an EuroHPC Development Access call. 
+The model was trained on the EuroHPC supercomputers LUMI, hosted by CSC (Finland) and the LUMI consortium through a EuroHPC Regular Access call. 
 Training was parallelized using the Pytorch DDP framework and the Hugging Face Accelerate library. More information on model training can be found here (link to publication) and scripts are availble here (link to github). 
 
 ### Training Data