mlboydaisuke / SigLIP2-base-patch16-224-ExecuTorch

huggingface.co
Total runs: 63
24-hour runs: 0
7-day runs: -64
30-day runs: -77
Model's Last Updated: September 04 2026
zero-shot-image-classification

Introduction of SigLIP2-base-patch16-224-ExecuTorch

Model Details of SigLIP2-base-patch16-224-ExecuTorch

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 )

Runs of mlboydaisuke SigLIP2-base-patch16-224-ExecuTorch on huggingface.co

63
Total runs
0
24-hour runs
-6
3-day runs
-64
7-day runs
-77
30-day runs

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