mlboydaisuke/DINOv2-ViT-S14-ExecuTorch

dinov2_vits14 — ExecuTorch

• Source: facebookresearch/dinov2 (torch.hub)
• License: Apache-2.0
• Input: [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
• Output: cls token [1,384], patch tokens [1,1369,384]

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	dinov2_vits14_xnnpack_fp32.pte	88.4	1.000000	159.0
fp16	dinov2_vits14_xnnpack_fp16.pte	44.8	0.999945	283.7
int8 (dynamic)	dinov2_vits14_xnnpack_int8.pte	24.9	0.998009	155.7
Core ML (fp16, iOS)	dinov2_vits14_coreml_all.pte	44.7	0.999863	41.2

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: 50.8 ms).

Checked in the task's own units

Correlation is a first filter. These are the numbers that decide:

• int8 (dynamic) — measured in the units that matter for this model — cosine similarity of the embeddings: median 0.9986 over 10 real images, worst 0.9965.

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, 384]	3.219e-05	1.000000
1	[1, 1369, 384]	4.311e-04	1.000000

XNNPACK delegate coverage (fp32): 66.7% (414/621 ops); ops left on the portable kernels: aten.expand_copy.default x49, aten.squeeze_copy.dims x36, aten.native_layer_norm.default x25, 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 x1

Conversion

torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)