depth_anything_v2_small — ExecuTorch
- Source: depth-anything/Depth-Anything-V2-Small-hf
- License: Apache-2.0
- Input: [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
- Output: relative inverse depth [1,518,518]
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 | depth_anything_v2_small_xnnpack_fp32.pte | 99.0 | 1.000000 | 167.3 |
| fp16 | depth_anything_v2_small_xnnpack_fp16.pte | 55.5 | 0.999992 | 289.7 |
| Core ML (fp16, iOS) | depth_anything_v2_small_coreml_all.pte | 50.2 | 0.999992 | 48.1 |
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: 85.0 ms).
Builds that did not earn a slot
- int8 (dynamic) is not shipped: measured in the units that matter for this model — fraction of pixels within 1.25x of the fp32 depth: median 0.9941 over 10 real images, worst 0.9748.
Verification (executorch 1.4.0, torch 2.13.0)
Parity is measured against the fp32 eager model on random input; corr is
the correlation over all elements of each output tensor.
| output | shape | max_abs_diff | corr |
|---|
| 0 | [1, 518, 518] | 4.768e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 73.4% (482/657 ops); ops left on the portable kernels: aten.expand_copy.default x49, aten.native_layer_norm.default x28, 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, dim_order_ops._to_dim_order_copy.default x1, aten.squeeze_copy.dims 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.
