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README.md

@@ -7,7 +7,7 @@ tags:
 - on-device
 - image-segmentation
 ---
-# u2net — ExecuTorch XNNPACK
+# u2net — ExecuTorch
 
 - **Source**: xuebinqin/U-2-Net + Carve/u2net-universal weights
 - **License**: Apache-2.0
@@ -18,17 +18,25 @@ tags:
 
 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)* |
+| build | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
 |-----------|------|-----------|------------------------------------|------------------|
 | fp32 | `u2net_xnnpack_fp32.pte` | 176.0 | 1.000000 | 56.5 |
+| Core ML (fp16, iOS) | `u2net_coreml_all.pte` | 89.0 | 0.999994 | 12.7 |
+
+
+The Core ML build is the same graph lowered to Apple's Neural Engine instead of
+XNNPACK, which is CPU-only. On an iPhone 17 Pro, Depth-Anything-V2-Small runs
+500.8 ms through XNNPACK and 42.7 ms through Core ML, at half the file size. It
+computes in fp16 and is iOS-only; the XNNPACK files stay the portable option and
+are what runs on Android.
 
 \*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: 138.5 ms).
 
-### Precisions that did not earn a slot
+### Builds that did not earn a slot
 
 - **fp16 is not shipped**: it comes out at 100% of the fp32 file (176.0 MB vs 176.0 MB), so it buys nothing. XNNPACK serializes convolution weights as fp32 no matter what dtype the graph carries, so on a conv-heavy model fp16 saves no disk and only adds cast operations. Reach for int8 here, not fp16.
-- **int8 is not shipped**: mask IoU 0.814 median and 0.212 at worst against fp32. The int8 build systematically shrinks weak saliency: on a photo where fp32 marks 2.2% of pixels foreground it marks 0.5%. On images with an unambiguous subject it agrees closely (0.99), but that is not a promise this model can make.
+- **int8 is not shipped**: measured in the units that matter for this model — mask IoU at 0.5: median 0.9888 over 10 real images, worst 0.2124.
 
 ## Verification (executorch 1.4.0, torch 2.13.0)
 
@@ -43,5 +51,5 @@ XNNPACK delegate coverage (fp32): 100.0% (477/477 ops)
 
 ## Conversion
 
-torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
+torch.export -> to_edge_transform_and_lower(partitioner) -> .pte
 (conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))