mlboydaisuke / YOLOX-s-ExecuTorch

huggingface.co
Total runs: 72
24-hour runs: 0
7-day runs: -17
30-day runs: -30
Model's Last Updated: September 01 2026
object-detection

Introduction of YOLOX-s-ExecuTorch

Model Details of YOLOX-s-ExecuTorch

yolox_s — ExecuTorch XNNPACK

  • Source : Megvii-BaseDetection/YOLOX (yolox_s)
  • License : Apache-2.0
  • Input : [[1, 3, 640, 640]] — BGR 0..255 float, NO normalization (YOLOX v0.3+ convention), 640x640 letterbox pad 114
  • Output : [1,8400,85]: cx,cy,w,h (input px), objectness, 80 class scores; postprocess = obj*cls threshold + NMS (required)
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 yolox_s_xnnpack_fp32.pte 35.9 1.000000 25.0
int8 yolox_s_xnnpack_int8.pte 9.2 0.999800 77.3

*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: 38.6 ms).

Checked in the task's own units

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

  • int8 — measured in the units that matter for this model — fraction of post-NMS detections matched at IoU 0.5 and same class: 0.938 of the fp32 build's detections are matched (90 of 96 across 10 images), worst single image 0.571.
Precisions that did not earn a slot
  • fp16 is not shipped : it comes out at 101% of the fp32 file (36.1 MB vs 35.9 MB), so it buys nothing. XNNPACK serializes convolution weights as fp32 no matter what dtype the graph carries, so on a conv-heavy model fp16 saves no disk and only adds cast operations. Reach for int8 here, not fp16.
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, 8400, 85] 2.258e-03 1.000000

XNNPACK delegate coverage (fp32): 88.2% (351/398 ops); ops left on the portable kernels: aten.view_copy.default x9, aten.slice_copy.Tensor x8, aten.arange.start_step x6, aten.expand_copy.default x6, aten.unsqueeze_copy.default x6, aten.cat.default x5, aten.full.default x3, dim_order_ops._to_dim_order_copy.default x2, aten.upsample_nearest2d.vec x2

Conversion

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

Runs of mlboydaisuke YOLOX-s-ExecuTorch on huggingface.co

72
Total runs
0
24-hour runs
-14
3-day runs
-17
7-day runs
-30
30-day runs

More Information About YOLOX-s-ExecuTorch huggingface.co Model

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YOLOX-s-ExecuTorch install

YOLOX-s-ExecuTorch is an open source model from GitHub that offers a free installation service, and any user can find YOLOX-s-ExecuTorch on GitHub to install. At the same time, huggingface.co provides the effect of YOLOX-s-ExecuTorch install, users can directly use YOLOX-s-ExecuTorch installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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