yolox_s — ExecuTorch
- 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.
| build | 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 |
| Core ML (fp16, iOS) | yolox_s_coreml_all.pte | 18.5 | 0.999991 | 15.7 |
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: 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.
Builds 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(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.
