license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- image-segmentation
- background-removal
base_model:
- schirrmacher/ormbg
ormbg_isnet — ExecuTorch
- Source: schirrmacher/ormbg
- License: Apache-2.0
- Input: [[1, 3, 1024, 1024]] — RGB 0-1, 1024x1024
- Output: alpha mask [1,1,1024,1024] 0-1 (sigmoid)
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 | ormbg_isnet_xnnpack_fp32.pte |
176.1 | 1.000000 | 121.7 |
| int8 | ormbg_isnet_xnnpack_int8.pte |
44.3 | 0.999988 | 87.2 |
| Core ML (fp16, iOS) | ormbg_isnet_coreml_all.pte |
89.0 | 0.999999 | 28.5 |
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: 375.5 ms).
Checked in the task's own units
Correlation is a first filter. These are the numbers that decide:
- int8 — measured in the units that matter for this model — mask IoU at 0.5: median 0.9987 over 10 real images, worst 0.9868.
Builds that did not earn a slot
- fp16 is not shipped: it comes out at 100% of the fp32 file (176.1 MB vs 176.1 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.
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, 1024, 1024] | 2.205e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 100.0% (467/467 ops)
Conversion
torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)