Add full example to README

README.md

@@ -28,20 +28,31 @@ Let's explain why an image of a castle is classified as such by a vision model:
 
 ```julia
 using ExplainableAI
-
-# Load model and input
-model = ...                      # load classifier model
-input = ...                      # input in batch-dimension-last format
+using VisionHeatmaps         # visualization of explanations as heatmaps
+using Zygote                 # load autodiff backend for gradient-based methods
+using Flux, Metalhead        # pre-trained vision models in Flux
+using DataAugmentation       # input preprocessing
+using HTTP, FileIO, ImageIO  # load image from URL
+using ImageInTerminal        # show heatmap in terminal
+
+# Load & prepare model
+model = VGG(16, pretrain=true)
+
+# Load input
+url = HTTP.URI("https://raw.githubusercontent.com/Julia-XAI/ExplainableAI.jl/gh-pages/assets/heatmaps/castle.jpg")
+img = load(url) 
+
+# Preprocess input
+mean = (0.485f0, 0.456f0, 0.406f0)
+std  = (0.229f0, 0.224f0, 0.225f0)
+tfm = CenterResizeCrop((224, 224)) |> ImageToTensor() |> Normalize(mean, std)
+input = apply(tfm, Image(img))               # apply DataAugmentation transform
+input = reshape(input.data, 224, 224, 3, :)  # unpack data and add batch dimension
 
 # Run XAI method
 analyzer = SmoothGrad(model)
-expl = analyze(input, analyzer)  # or: analyzer(input)
-
-# Show heatmap
-heatmap(expl)
-
-# Or analyze & show heatmap directly
-heatmap(input, analyzer)
+expl = analyze(input, analyzer)  # or: expl = analyzer(input)
+heatmap(expl)                    # show heatmap using VisionHeatmaps.jl
 ```
 
 By default, explanations are computed for the class with the highest activation.