Update README

README.md

@@ -16,6 +16,25 @@ Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 2
 - Fine-tuning Objective: Classify traffic signs into 43 different categories, including various speed limits, warning signs, and prohibitory or regulatory signs.
 - Developer: Aleksandra Cvetanovska
 
+## Example Use
+```
+from transformers import ViTForImageClassification, ViTImageProcessor
+from torch.utils.data import DataLoader
+import torch
+
+url = 'https://images.unsplash.com/photo-1572670014853-1d3a3f22b40f?q=80&w=2942&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D'
+image = Image.open(requests.get(url, stream=True).raw)
+
+model_name = "cvetanovskaa/vit-base-patch16-224-in21k-gtsrb-tuned"
+model = ViTForImageClassification.from_pretrained(model_name)
+processor = ViTImageProcessor.from_pretrained(model_name)
+
+inputs = processor(images=image, return_tensors="pt")
+
+outputs = model(**inputs)
+last_hidden_states = outputs.last_hidden_state
+```
+
 ## Limitations and Bias
 - The model is trained exclusively on data from German traffic signs, which may not generalize well to signs in other regions due to differences in design and context.
 - Performance may vary under different lighting conditions or when signs are partially occluded