| --- |
| 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)) |
| |
