Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,47 @@
+---
+license: apache-2.0
+tags:
+- executorch
+- xnnpack
+- pte
+- on-device
+- image-segmentation
+---
+# dis_isnet — ExecuTorch XNNPACK
+
+- **Source**: xuebinqin/DIS + NimaBoscarino/IS-Net_DIS-general-use weights
+- **License**: Apache-2.0
+- **Input**: [[1, 3, 1024, 1024]] — RGB, scaled to [-1,1] (x/255 then (x-0.5)/0.5), 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.
+
+| precision | file | size (MB) | parity vs fp32 eager (worst corr) | Mac median (ms)* |
+|-----------|------|-----------|------------------------------------|------------------|
+| fp32 | `dis_isnet_xnnpack_fp32.pte` | 176.1 | 1.000000 | 169.0 |
+| int8 | `dis_isnet_xnnpack_int8.pte` | 44.3 | 0.987820 | 70.1 |
+
+\*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: 392.2 ms).
+
+### Precisions that did not earn a slot
+
+- **fp16 is not shipped**: worst-output corr 0.986 against fp32 eager, below the 0.995 bar for this precision. The file converts and runs; the numbers do not hold up, so it is left out rather than shipped with a warning.
+
+## 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] | 8.941e-07 | 1.000000 |
+
+XNNPACK delegate coverage (fp32): 100.0% (468/468 ops)
+
+## Conversion
+
+torch.export -> to_edge_transform_and_lower(XnnpackPartitioner) -> .pte
+(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))