Update README.md

README.md

@@ -48,11 +48,11 @@ Additional images for testing can be found at:
 
 #### High_Energy_Ion_Fe_Nuclei
 
-![High_Energy_Ion_Fe_Nuclei](images/High_Energy_Ion_Fe_Nuclei)
+![High_Energy_Ion_Fe_Nuclei](https://roosevelt.devron-systems.com/HF/P242_73665006707-A6_002_008_proj.tif)
 
 #### XRay_irradiated_Nuclei
 
-![XRay_irradiated_Nuclei](images/XRay_irradiated_Nuclei)
+![XRay_irradiated_Nuclei](https://roosevelt.devron-systems.com/HF/P278_73668090728-A7_003_027_proj.tif)
 
 ## Training data
 The ViT model was pretrained on a dataset consisting of 14 million images and 21k classes ([ImageNet-21k](http://www.image-net.org/).
@@ -67,16 +67,16 @@ from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 from PIL import Image
 import requests
 
-url = 'https://sdo.gsfc.nasa.gov/assets/gallery/preview/211_coronalhole.jpg'
+url = 'https://roosevelt.devron-systems.com/HF/P242_73665006707-A6_002_008_proj.tif'
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("kenobi/SDO_VT1")
-model = AutoModelForImageClassification.from_pretrained("kenobi/SDO_VT1")
+feature_extractor = AutoFeatureExtractor.from_pretrained("kenobi/NASA_GeneLab_MBT")
+model = AutoModelForImageClassification.from_pretrained("kenobi/NASA_GeneLab_MBT")
 inputs = feature_extractor(images=image, return_tensors="pt")
 
 outputs = model(**inputs)
 logits = outputs.logits
-# model predicts one of the three fine-tuned classes (NASA_SDO_Coronal_Hole, NASA_SDO_Coronal_Loop or NASA_SDO_Solar_Flare) 
+# model predicts one of the two fine-tuned classes (High_Energy_Ion_Fe_Nuclei or XRay_irradiated_Nuclei) 
 predicted_class_idx = logits.argmax(-1).item()
 print("Predicted class:", model.config.id2label[predicted_class_idx])
 ```