add basic usage instructions

README.md

@@ -13,3 +13,52 @@ datasets:
 Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. 
 
 This repo contains a Core ML version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224).
+
+## Usage instructions
+
+Create a `VNCoreMLRequest` that loads the ViT model:
+
+```swift
+import CoreML
+import Vision
+
+lazy var classificationRequest: VNCoreMLRequest = {
+  do {
+    let config = MLModelConfiguration()
+    config.computeUnits = .all
+    let coreMLModel = try ViT(configuration: config)
+    let visionModel = try VNCoreMLModel(for: coreMLModel.model)
+
+    let request = VNCoreMLRequest(model: visionModel, completionHandler: { [weak self] request, error in
+      if let results = request.results as? [VNClassificationObservation] {
+        /* do something with the results */
+      }
+    })
+
+    request.imageCropAndScaleOption = .centerCrop
+    return request
+  } catch {
+    fatalError("Failed to create VNCoreMLModel: \(error)")
+  }
+}()
+```
+
+Perform the request:
+
+```swift
+func classify(image: UIImage) {
+  guard let ciImage = CIImage(image: image) else {
+    print("Unable to create CIImage")
+    return
+  }
+
+  DispatchQueue.global(qos: .userInitiated).async {
+    let handler = VNImageRequestHandler(ciImage: ciImage, orientation: .up)
+    do {
+      try handler.perform([self.classificationRequest])
+    } catch {
+      print("Failed to perform classification: \(error)")
+    }
+  }
+}
+```