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README.md

@@ -10,29 +10,36 @@ tags:
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 # modnet_portrait_matting — ExecuTorch XNNPACK
 
-`modnet_portrait_matting_xnnpack_fp32.pte` (26.1 MB, fp32, XNNPACK-delegated)
-
 - **Source**: ZHKKKe/MODNet + DavG25/modnet-pretrained-models ckpt
 - **License**: Apache-2.0
 - **Input**: [[1, 3, 512, 512]] — RGB [-1,1], 512x512
 - **Output**: alpha matte [1,1,512,512] 0-1
 
-## Verification (Mac arm64, executorch 1.4.0, torch 2.13.0)
+## Variants
+
+All variants take and return fp32 tensors — swap the `.pte` file, keep your app code.
+
+| precision | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
+|-----------|------|-----------|------------------------------------|------------------|
+| fp32 | `modnet_portrait_matting_xnnpack_fp32.pte` | 26.1 | 1.000000 | 64.9 |
+| fp16 | `modnet_portrait_matting_xnnpack_fp16.pte` | 24.4 | 1.000000 | 107.8 |
+| int8 | `modnet_portrait_matting_xnnpack_int8.pte` | 6.8 | 0.999949 | 46.5 |
+
+\*Mac arm64, single process, median of 10 — a reference point for relative cost
+only, not a device number (torch eager fp32 on the same machine: 120.1 ms).
 
-Parity vs torch fp32 eager on random input:
+## Verification (executorch 1.4.0, torch 2.13.0)
+
+Parity is measured against the fp32 eager model on real image input; `corr` is
+the correlation over all elements of each output tensor.
 
 | output | shape | max_abs_diff | corr |
 |--------|-------|--------------|------|
-| 0 | [1, 1, 512, 512] | 1.572e-04 | 1.000000 |
+| 0 | [1, 1, 512, 512] | 1.476e-04 | 1.000000 |
 
-Median latency over 10 runs (single Mac process, reference only — device numbers to follow):
-ExecuTorch 69.5 ms vs torch eager 120.2 ms.
+XNNPACK delegate coverage (fp32): 94.7% (302/319 ops); ops left on the portable kernels: `aten._native_batch_norm_legit.no_stats` x16, `aten.expand_copy.default` x1
 
 ## Conversion
 
 torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
 (conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))
-
-
----
-Part of [executorch-models](https://github.com/john-rocky/executorch-models) — a verified .pte zoo for ExecuTorch. Conversion scripts and all models are indexed there.