mlboydaisuke/YOLOX-s-ExecuTorch

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	24.8
Core ML (fp16, iOS)	yolox_s_coreml_all.pte	18.5	0.999991	15.9

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: 36.0 ms).

Checked in the task's own units

Correlation cannot judge a detector, and this model is the clearest case of it on the shelf. The head is [1, 8400, 85] and almost all 8400 anchors are background that every build agrees on trivially, so the correlation is dominated by the part nobody reads. The withdrawn int8 build below reads 0.999800 and drops ten objects.

So both arms run the post-processing this card specifies — score = objectness x class, threshold 0.30, per-class NMS at 0.65 — and the build has to reproduce eager's detection set. Measured over 142 detections on 32 photographs of people and animals, held out from the street images the int8 build was calibrated on:

build	added	dropped	relabelled	worst matched IoU	worst score shift
fp32	0	0	0	1.000	0.0000
Core ML	1	2	0	0.832	0.0793

Core ML's three disagreements all sit within its own score error of the threshold — at +0.001, +0.030 and +0.076 against a score shift of 0.0793. Those are detections the cut is ambivalent about anyway; a photograph shifted by a pixel moves them in eager too. No object more than a score error above the threshold changes.

Withdrawn: int8 (2026-08-27)

yolox_s_xnnpack_int8.pte was published and has been withdrawn. Measured the same way:

• adds 11, drops 10 and relabels 3 of 142 detections — 17% of the answer changes
• objects lost 0.24 and 0.21 above the threshold, not at the boundary
• boxes that survive move to IoU 0.689 against eager's
• worst score shift 0.3585, larger than the threshold itself is deep (0.30)

At 9.2 MB against fp32's 35.9 it was the small build, and it was cleared on a correlation of 0.999800 — a number that, on this output shape, a build could earn while finding nothing at all. The Core ML build is 18.5 MB, runs faster than either XNNPACK build, and keeps the answers.

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. Quantization, not fp16, is the reduction that can shrink a graph like this.

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]	4.013e-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)