Card: add a measured Performance table (Pixel 8a, benchmark_model GPU+CPU), naming the runtime for each row

README.md

@@ -77,6 +77,17 @@ seg = it.get_tensor(out[0]["index"])[0].argmax(0)   # [768,768] 0=bg 1=upper 2=l
 Converted with **litert-torch** (`build_clothseg.py`): loads the MIT U²-Net cloth weights
 and exports the 4-class graph.
 
+## Performance
+
+Measured on a **Pixel 8a** (Tensor G3, Android 16) with the standard TFLite [`benchmark_model`](https://ai.google.dev/edge/litert/models/measurement) tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.
+
+| Runtime | Backend | Graph on GPU | Latency |
+|---|---|---|---|
+| LiteRT `CompiledModel` (`LITERT_CL`) | GPU | 254 / 254 | ~88 ms |
+| TFLite `benchmark_model` (`TfLiteGpuDelegateV2`) | GPU (OpenCL) | 254 / 254 | 496.4 ms |
+
+**The two GPU rows are different runtimes, not a contradiction.** The `LITERT_CL` figure is the one recorded when this model shipped, taken through LiteRT's own `CompiledModel` accelerator — the path the Kotlin sample app and the LiteRT API use. The `TfLiteGpuDelegateV2` figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the `TfLiteGpuDelegateV2` row as a reproducible floor, not as this model's speed on LiteRT.
+
 ## License
 
 MIT (cloth-segmentation / levindabhi). Trained on iMaterialist-Fashion-2019.