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

README.md

@@ -84,6 +84,18 @@ alpha = it.get_tensor(out[0]["index"])[0, 0]   # [512,512] in [0,1]
 Converted with **litert-torch** (`build_modnet.py`): loads the trained MODNet weights,
 applies the two patches (SE 1×1-conv, SafeInstanceNorm), and exports.
 
+## 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 | — | ~79 ms |
+| TFLite `benchmark_model` (`TfLiteGpuDelegateV2`) | GPU (OpenCL) | 551 / 551 | 59.6 ms |
+| TFLite `benchmark_model` | CPU (XNNPACK, 4 threads) | — | 418.9 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
 
 Apache-2.0 (MODNet / ZHKKKe/MODNet).