license: apache-2.0
tags:
- executorch
- xnnpack
- pte
- on-device
- image-feature-extraction
base_model:
- facebook/dinov2-small
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)
More models in this format: ExecuTorch Model Zoo — 31 models, each with the recipe that produced it.
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.