modnet_portrait_matting — ExecuTorch
- 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
Variants
All variants take and return fp32 tensors — swap the .pte file, keep your app code.
| build | 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 |
| Core ML (fp16, iOS) | modnet_portrait_matting_coreml_all.pte |
13.8 | 0.999997 | 6.8 |
The Core ML build is the same graph lowered to Apple's Neural Engine instead of XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it runs 3.5x to 13.9x faster (median 12x) at roughly half the file size — for example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms against 5.9 ms. 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: 120.1 ms).
Builds that did not earn a slot
- int8 is not shipped: measured in the units that matter for this model — mask IoU at 0.5: median 0.9864 over 10 real images, worst 0.5970.
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] | 1.476e-04 | 1.000000 |
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(partitioner) -> .pte (conversion scripts: executorch-models)
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