README.md

metadata

license: bsd-3-clause
tags:
  - executorch
  - xnnpack
  - pte
  - on-device
  - image-classification

efficientnet_b1 — ExecuTorch

• Source: torchvision efficientnet_b1 IMAGENET1K_V2
• License: BSD-3-Clause
• Input: [[1, 3, 240, 240]] — RGB, ImageNet norm, 240x240
• Output: ImageNet logits [1,1000]

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	efficientnet_b1_xnnpack_fp32.pte	31.2	1.000000	9.3
fp16	efficientnet_b1_xnnpack_fp16.pte	28.8	0.999816	54.9
Core ML (fp16, iOS)	efficientnet_b1_coreml_all.pte	16.3	0.992817 — see below	0.6

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: 352.7 ms).

Checked in the task's own units

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

• Core ML (fp16, iOS) — measured in the units that matter for this model — fraction of images keeping the fp32 top-1 label: 9 of 10 images keep the fp32 top-1 label.

Builds that did not earn a slot

• int8 is not shipped: measured in the units that matter for this model — fraction of images keeping the fp32 top-1 label: 0 of 10 images keep the fp32 top-1 label.

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, 1000]	1.490e-05	1.000000

XNNPACK delegate coverage (fp32): 100.0% (410/410 ops)

Conversion

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