software-mansion/U2NetP-ExecuTorch

u2netp

This repository hosts the U²-Netp models exported for the 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, 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Full-network INT8 remains unpublished because it loses substantial foreground detail.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.

To load these files in your own ExecuTorch runtime, read the compatibility note first.