Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,54 @@
+---
+license: apache-2.0
+tags:
+- executorch
+- xnnpack
+- pte
+- on-device
+- zero-shot-image-classification
+base_model:
+- google/siglip2-base-patch16-224
+---
+# siglip2_base_p16_image — ExecuTorch
+
+- **Source**: google/siglip2-base-patch16-224
+- **License**: Apache-2.0
+- **Input**: [[1, 3, 224, 224]] — RGB scaled to [-1, 1] (mean .5, std .5), 224x224
+- **Output**: image embedding [1,768] from the attention pooler (unnormalized; L2-normalize before cosine)
+
+## 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 | `siglip2_base_p16_image_xnnpack_fp32.pte` | 371.7 | 0.999994 | 195.6 |
+| fp16 | `siglip2_base_p16_image_xnnpack_fp16.pte` | 187.3 | 0.999986 | 227.8 |
+| Core ML (fp16, iOS) | `siglip2_base_p16_image_coreml_all.pte` | 185.1 | 0.999806 | 4.4 |
+
+
+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: 34.4 ms).
+
+## 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, 768] | 1.195e-02 | 0.999994 |
+
+XNNPACK delegate coverage (fp32): 68.6% (395/576 ops); ops left on the portable kernels: `aten.expand_copy.default` x48, `aten.native_layer_norm.default` x26, `aten.mul.Scalar` x24, `aten.logical_not.default` x24, `aten.eq.Scalar` x12, `aten.full_like.default` x12, `aten.any.dim` x12, `aten.where.self` x12, `aten.select_copy.int` x3, `aten.split_with_sizes_copy.default` x2, `aten.addmm.default` x2, `aten.repeat.default` x1, `aten.embedding.default` x1, `aten.unsqueeze_copy.default` x1, `aten.squeeze_copy.dims` x1
+
+## Conversion
+
+torch.export -> to_edge_transform_and_lower(partitioner) -> .pte
+(conversion scripts: [executorch-models](https://github.com/john-rocky/executorch-models))