mlboydaisuke / EfficientNet-B1-ExecuTorch

huggingface.co
Total runs: 43
24-hour runs: 0
7-day runs: -16
30-day runs: -20
Model's Last Updated: August 15 2026
image-classification

Introduction of EfficientNet-B1-ExecuTorch

Model Details of EfficientNet-B1-ExecuTorch

efficientnet_b1 — ExecuTorch XNNPACK

  • 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.

precision 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

*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).

Precisions 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: median 0.0000 over 10 real images, worst 0.0000.
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(XnnpackPartitioner) -> .pte (conversion scripts: executorch-models )

Runs of mlboydaisuke EfficientNet-B1-ExecuTorch on huggingface.co

43
Total runs
0
24-hour runs
-21
3-day runs
-16
7-day runs
-20
30-day runs

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