mlboydaisuke/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)