| --- |
| license: apache-2.0 |
| pipeline_tag: image-segmentation |
| library_name: executorch |
| --- |
| |
| # u2netp |
|
|
| This repository hosts the **U²-Netp** models exported for the |
| [React Native ExecuTorch](https://www.npmjs.com/package/react-native-executorch) |
| library as ExecuTorch `.pte` programs, ready to run on device. |
|
|
| U²-Netp predicts a soft mask for salient foreground objects, including animals |
| and products as well as people. It does not classify object categories or |
| separate individual instances. |
|
|
| Upstream model: [U²-Net](https://github.com/xuebinqin/U-2-Net), |
| revision `ac7e1c817ecab7c7dff5ce6b1abba61cd213ff29`. |
|
|
| ## Variants |
|
|
| | Path | Backend | Precision | |
| | --------------------------------- | ------- | --------- | |
| | `coreml/u2netp_coreml_fp16.pte` | coreml | fp16 | |
| | `coreml/u2netp_coreml_fp32.pte` | coreml | fp32 | |
| | `xnnpack/u2netp_xnnpack_fp32.pte` | xnnpack | fp32 | |
| | `xnnpack/u2netp_xnnpack_int8.pte` | xnnpack | selective int8 / fp32 | |
| | `vulkan/u2netp_vulkan_fp16.pte` | vulkan | fp16 with one fp32 convolution | |
|
|
| The exports take RGB float32 `[1,3,320,320]` input in `[0,1]` and return one |
| float32 `[1,1,320,320]` foreground logit map. Image-maximum scaling and ImageNet |
| normalization are included in the graph. RNE applies sigmoid and returns an |
| opaque RGBA grayscale mask; use its foreground intensity as an alpha mask when |
| compositing the original image. Fine structures can be missed. FP16 favors |
| speed and may shift mask boundaries; FP32 preserves the reference more closely. |
|
|
| ## Precision notes |
|
|
| The INT8 variant selectively quantizes four expensive convolutions with static |
| INT8 activations and per-channel INT8 weights. Sensitive layers and skip |
| connections remain FP32; public input and output types are unchanged. |
|
|
| On an Apple M1 Pro, interleaved ExecuTorch runs measured **87 ms** median versus |
| **156 ms** for FP32 (1.78× faster; 30 runs after five warm-ups). Device speedups |
| will vary. Across 13 images outside calibration, exported masks averaged |
| **0.9953 IoU** against FP32 (minimum 0.9868), at a foreground threshold of 0.5. |
| These measure export agreement, not accuracy against ground-truth masks. |
| Choose FP32 when boundary precision matters. |
|
|
| Full-network INT8 remains unpublished because it loses substantial foreground |
| detail. |
|
|
| Vulkan FP16 preserves input normalization and one sensitive convolution in FP32. |
| On a Galaxy S20+ (Android 13, Mali), native forward medians were 337 ms for this |
| variant, versus 316 ms for XNNPACK FP32 and 189 ms for selective INT8. Android uses the standard Vulkan-first default when the Vulkan backend is |
| linked. Performance varies by device. Across 13 device-tested images, Vulkan FP16 averaged 0.9945 mask IoU |
| against FP32 (minimum 0.9854). |
|
|
| ## Repository structure |
|
|
| ``` |
| config.json 28 B |
| coreml/config.json 1.5 kB |
| coreml/u2netp_coreml_fp16.pte 3.3 MB |
| coreml/u2netp_coreml_fp32.pte 5.5 MB |
| vulkan/config.json 886 B |
| vulkan/u2netp_vulkan_fp16.pte 2.4 MB |
| xnnpack/config.json 1.5 kB |
| xnnpack/u2netp_xnnpack_fp32.pte 4.6 MB |
| xnnpack/u2netp_xnnpack_int8.pte 4.0 MB |
| LICENSE 11.4 kB |
| ``` |
|
|
| ## Compatibility |
|
|
| These files are published for the **ExecuTorch v1.4.1** runtime. ExecuTorch |
| gives no forward compatibility guarantee, so an older runtime may fail to load |
| them. CoreML requires iOS 18 / macOS 15 or newer; full RNE execution and |
| performance still require validation on the target device. |
|
|
| To use them in React Native ExecuTorch, pass |
| `models.semanticSegmentation.U2NETP.DEFAULT` to `useSemanticSegmenter` or |
| `createSemanticSegmenter`. See the |
| [documentation](https://docs.swmansion.com/react-native-executorch/docs/fundamentals/downloading-models). |
|
|
| To load these files in your own ExecuTorch runtime, read the |
| [compatibility note](https://github.com/pytorch/executorch/blob/main/runtime/COMPATIBILITY.md) |
| first. |
|
|
