Add U2NetP foreground segmentation exports for XNNPACK and CoreML

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+coreml/u2netp_coreml_fp16.pte filter=lfs diff=lfs merge=lfs -text
+coreml/u2netp_coreml_fp32.pte filter=lfs diff=lfs merge=lfs -text
+xnnpack/u2netp_xnnpack_fp32.pte filter=lfs diff=lfs merge=lfs -text

LICENSE

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README.md

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+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+tags:
+  - executorch
+  - react-native
+  - background-removal
+  - salient-object-detection
+---
+
+# U²-Netp for React Native ExecuTorch
+
+The small U²-Net salient object model, adapted for the existing
+`createSemanticSegmenter` / `useSemanticSegmenter` pipeline in
+[React Native ExecuTorch](https://github.com/software-mansion/react-native-executorch).
+The source model and pretrained checkpoint are from
+[xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net), under Apache-2.0.
+
+This model predicts salient foreground objects, including products and animals,
+as well as people. It produces one soft foreground mask; it does not classify
+object categories or separate individual instances. Fine structures and
+low-contrast boundaries can be missed.
+
+## Files
+
+| Backend | File | Size | Compute precision |
+| --- | --- | --- | --- |
+| XNNPACK | `xnnpack/u2netp_xnnpack_fp32.pte` | 4.6 MB | FP32 |
+| CoreML | `coreml/u2netp_coreml_fp16.pte` | 3.3 MB | FP16 |
+| CoreML | `coreml/u2netp_coreml_fp32.pte` | 5.5 MB | FP32 |
+
+Root `config.json` declares `modelName: u2netp`. Each backend has its own
+`config.json` with the exported tensor signatures. `sha256.json` records the
+artifact checksums. Models were exported with torch 2.14.0, ExecuTorch 1.4.1 and
+coremltools 9.0. CoreML targets iOS 18 / macOS 15 or newer.
+
+## Tensor contract and processing
+
+`forward(float32[1,3,320,320]) -> float32[1,1,320,320]`
+
+Input is RGB, stretched to 320×320, in NCHW layout with values in `[0,1]`.
+The exported graph divides by the image maximum (clamped to `1e-6` for an
+all-black input), then applies ImageNet mean/std normalization. Do not apply
+ImageNet normalization a second time on the client.
+
+Output is the fused foreground **logit** map. The seven upstream probability
+outputs are replaced by the fused logit before sigmoid, retaining the pretrained
+parameters. The client applies sigmoid once and resizes the mask to the source
+image dimensions. The upstream demo's per-image output min/max contrast
+stretch is omitted, so intensities remain foreground probabilities.
+
+In RNE, `models.semanticSegmentation.U2NETP` configures RGB scaling by `1/255`,
+linear input/output interpolation, and labels `background` / `foreground`.
+The semantic segmenter returns an RGBA grayscale mask: RGB contains foreground
+intensity from 0 to 255, alpha is opaque, and `colormap` is undefined. Use an RGB
+channel as the alpha mask when compositing the original image; the returned
+buffer itself is not an image cutout.
+
+## Validation and performance
+
+Host checks used an Apple M1 Pro running macOS 26.5.2. These are warm host
+measurements, not phone benchmarks, and exclude image preprocessing and mask
+rendering in React Native.
+
+XNNPACK runs through the ExecuTorch runtime. Its probability error against
+upstream eager PyTorch is below 0.00015 on the tested bicycle, boat and animal
+images. The graph contains one XNNPACK delegate plus a portable `amax` input
+reduction. Per-op timing was unavailable because the installed runtime wheel
+does not enable event tracing.
+
+The actual CoreML partitions were extracted from the saved `.pte` files and
+executed directly through CoreML, since the installed ExecuTorch wheel cannot
+execute its multifunction CoreML packages. Both CoreML exports contain one
+delegate with no portable operations. The profiler's compute-plan tool places
+369 of 370 dispatched FP16 operations on the Neural Engine; one cast runs on
+CPU.
+
+On the three test images, FP16 foreground-probability MAE is 0.00089–0.00453
+and thresholded-mask IoU against FP32 is 0.9873–0.9991. Individual edge pixels
+can differ by up to 0.312, so FP16 is a speed/quality tradeoff. CoreML FP32
+matches the eager thresholded masks on all three images, with maximum
+probability error below 0.00017. Warm direct-CoreML medians are approximately
+7.2 ms (FP16) and 16.7 ms (FP32).
+
+Black/white inputs were also checked for finite results. This is a conversion
+smoke test, not a dataset accuracy evaluation. Full CoreML execution through
+RNE and both backends' performance require validation on the target devices.
+
+## Reproduce the exports
+
+From the export-scripts repository:
+
+```sh
+uv run --project exporter_models/u2netp exporter export u2netp --backend xnnpack --output-dir exports/u2netp/xnnpack
+uv run --project exporter_models/u2netp exporter export u2netp --backend coreml --variant fp16 --output-dir exports/u2netp/coreml
+uv run --project exporter_models/u2netp exporter export u2netp --backend coreml --variant fp32 --output-dir exports/u2netp/coreml
+```
+
+The model code is vendored from upstream commit
+`ac7e1c817ecab7c7dff5ce6b1abba61cd213ff29`. The checkpoint is downloaded from the
+author's linked Google Drive artifact and checked against SHA-256
+`e7567cde013fb64813973ce6e1ecc25a80c05c3ca7adbc5a54f3c3d90991b854`.

config.json

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+{
+  "modelName": "u2netp"
+}

coreml/config.json

@@ -0,0 +1,74 @@
+{
+  "$schema": "https://huggingface.co/software-mansion/react-native-executorch-spec/resolve/main/config.schema.json",
+  "model": "u2netp",
+  "family": "u2net",
+  "capabilities": [
+    "semantic-segmentation"
+  ],
+  "backend": "coreml",
+  "license": "apache-2.0",
+  "variants": [
+    {
+      "file": "u2netp_coreml_fp16.pte",
+      "precision": "fp16",
+      "methods": {
+        "forward": {
+          "inputs": [
+            {
+              "shape": [
+                1,
+                3,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ],
+          "outputs": [
+            {
+              "shape": [
+                1,
+                1,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ]
+        }
+      },
+      "size_bytes": 3272420
+    },
+    {
+      "file": "u2netp_coreml_fp32.pte",
+      "precision": "fp32",
+      "methods": {
+        "forward": {
+          "inputs": [
+            {
+              "shape": [
+                1,
+                3,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ],
+          "outputs": [
+            {
+              "shape": [
+                1,
+                1,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ]
+        }
+      },
+      "size_bytes": 5476362
+    }
+  ]
+}

coreml/u2netp_coreml_fp16.pte

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coreml/u2netp_coreml_fp32.pte

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sha256.json

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+{
+  "xnnpack/u2netp_xnnpack_fp32.pte": "0f5fa03948fe4f3c148fef5536b0e423e17ef3450c5a5de90acaf3bd5c372504",
+  "coreml/u2netp_coreml_fp16.pte": "0e7ca590135ec7e46b1f7e248ae6a9af0e108e72fda454bd6df2b7d081138b44",
+  "coreml/u2netp_coreml_fp32.pte": "9c3cb8af1ff0aada476945f56d8fc5c3eb4a83a3b5bf5be9b491924c9d52f806"
+}

xnnpack/config.json

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+{
+  "$schema": "https://huggingface.co/software-mansion/react-native-executorch-spec/resolve/main/config.schema.json",
+  "model": "u2netp",
+  "family": "u2net",
+  "capabilities": [
+    "semantic-segmentation"
+  ],
+  "backend": "xnnpack",
+  "license": "apache-2.0",
+  "variants": [
+    {
+      "file": "u2netp_xnnpack_fp32.pte",
+      "precision": "fp32",
+      "methods": {
+        "forward": {
+          "inputs": [
+            {
+              "shape": [
+                1,
+                3,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ],
+          "outputs": [
+            {
+              "shape": [
+                1,
+                1,
+                320,
+                320
+              ],
+              "dtype": "float32"
+            }
+          ]
+        }
+      },
+      "size_bytes": 4610948
+    }
+  ]
+}

xnnpack/u2netp_xnnpack_fp32.pte

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