Update README.md

README.md

@@ -29,6 +29,30 @@ The model correctly captures positional uncertainty and produces high-level obje
 
 I-JEPA can be used for image classification or feature extraction. This checkpoint in specific is intended for **Feature Extraction**.
 
+## How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+import requests
+
+from PIL import Image
+from transformers import AutoProcessor, IJepaForImageClassification
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+model_id = "jmtzt/ijepa_vith16_1k"
+processor = AutoProcessor.from_pretrained(model_id)
+model = IJepaForImageClassification.from_pretrained(model_id)
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+# model predicts one of the 1000 ImageNet classes
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])
+```
 
 ### BibTeX entry and citation info
 If you use I-JEPA or this code in your work, please cite: