Add minimal usage snippets (Kotlin + Python)

README.md

@@ -41,18 +41,40 @@ rewrites** (pure CNN).
 - **Output**: `[1, 1, 320, 320]` saliency mask in `[0, 1]` (sigmoid). Upscale to the input
   size and use as the foreground alpha.
 
-## Usage (Android, LiteRT CompiledModel)
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
 
 ```kotlin
-val model = CompiledModel.create(
-    context.assets, "u2net_fp16.tflite",
-    CompiledModel.Options(Accelerator.GPU), null
-)
+val model = CompiledModel.create(context.assets, "u2net_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
 val inputs = model.createInputBuffers()
 val outputs = model.createOutputBuffers()
-inputs[0].writeFloat(nchwFloatArray)   // [1,3,320,320]
+inputs[0].writeFloat(chw)            // [1,3,320,320] /max then ImageNet-norm, NCHW
 model.run(inputs, outputs)
-val mask = outputs[0].readFloat()      // [1,1,320,320] in [0,1]
+val mask = outputs[0].readFloat()    // [1,1,320,320] saliency in [0,1]
+```
+
+**Python (desktop verification)**
+
+```python
+MEAN = np.array([0.485, 0.456, 0.406], np.float32)
+STD  = np.array([0.229, 0.224, 0.225], np.float32)
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+orig = Image.open("photo.jpg").convert("RGB")
+a = np.asarray(orig.resize((320, 320)), np.float32)
+a = a / a.max()                                                   # per-image max, then ImageNet
+x = ((a - MEAN) / STD).transpose(2, 0, 1)[None]                   # [1,3,320,320]
+
+it = Interpreter(model_path="u2net_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+m = it.get_tensor(it.get_output_details()[0]["index"])[0, 0]      # [320,320], [0,1]
+alpha = Image.fromarray((m * 255).astype(np.uint8)).resize(orig.size)
+cutout = orig.copy(); cutout.putalpha(alpha)                      # foreground on transparency
+cutout.save("cutout.png")
 ```
 
 A complete Android sample (live camera + gallery background removal) is available in