Update README.md

README.md

@@ -28,6 +28,7 @@ This is a model fine-tuned to classify whether an aerial/satellite image contain
 You can find some images to test inference with [in this old repo from the original project](https://github.com/pszemraj/BoulderAreaDetector/tree/cbb22bdb3373b4b72d798dedfcb28543c0dc769d/test_images)
 
 
+
 ## Model description
 
 This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the pszemraj/boulderspot dataset.
@@ -39,10 +40,30 @@ It achieves the following results on the evaluation set:
 - Recall: 0.9883
 - Matthews Correlation: 0.8962
 
+## example usage
+
+```py
+import requests
+from PIL import Image
+from transformers import pipeline
+
+pipe = pipeline(
+    "image-classification",
+    model="pszemraj/convnextv2-nano-22k-384-boulderspot",
+)
+
+url = "https://huggingface.co/pszemraj/convnextv2-nano-22k-384-boulderspot/resolve/main/test_img_magic_wood.png?download=true"
+image = Image.open(requests.get(url, stream=True).raw)
+result = pipe(image)[0]
+print(result)
+# image.show()
+```
+
 ## Intended uses & limitations
 
 Classification of aerial/satellite imagery, ideally with spacial resolution 10-25 cm (_i.e. for 10 cm, each pixel in the image corresonds to approx. 10 cm x 10 cm area on the ground_). It may be suitable outside of that, but should be validated as other resolutions were not present in the training data.
 
+
 ## Training procedure
 
 ### Training hyperparameters