mlboydaisuke/SSDLite320-MobileNetV3-ExecuTorch

ssdlite320_mobilenetv3 — ExecuTorch

• Source: torchvision ssdlite320_mobilenet_v3_large COCO_V1
• License: BSD-3-Clause
• Input: [[1, 3, 320, 320]] — RGB 0-1, 320x320 (torchvision SSDLite norm baked in model)
• Output: 12 raw heads: (cls [1,A91,H,W], box [1,A4,H,W]) x 6 levels, H=W in {20,10,5,3,2,1}

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	ssdlite320_mobilenetv3_xnnpack_fp32.pte	13.8	1.000000	5.1
int8	ssdlite320_mobilenetv3_xnnpack_int8.pte	3.9	0.968820	7.1
Core ML (fp16, iOS)	ssdlite320_mobilenetv3_coreml_all.pte	7.5	0.999658	0.9

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: 115.3 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 firing detections agreeing: 0.999 of the fp32 build's detections are matched (26988 of 27004 across 10 images), worst single image 0.998.

Builds that did not earn a slot

• fp16 is not shipped: it comes out at 100% of the fp32 file (13.8 MB vs 13.8 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, 546, 20, 20]	3.052e-05	1.000000
1	[1, 24, 20, 20]	3.767e-05	1.000000
2	[1, 546, 10, 10]	2.956e-05	1.000000
3	[1, 24, 10, 10]	8.464e-06	1.000000
4	[1, 546, 5, 5]	1.717e-05	1.000000
5	[1, 24, 5, 5]	9.477e-06	1.000000
6	[1, 546, 3, 3]	1.717e-05	1.000000
7	[1, 24, 3, 3]	9.421e-06	1.000000
8	[1, 546, 2, 2]	2.050e-05	1.000000
9	[1, 24, 2, 2]	3.457e-06	1.000000
10	[1, 546, 1, 1]	1.001e-05	1.000000
11	[1, 24, 1, 1]	9.418e-06	1.000000

XNNPACK delegate coverage (fp32): 94.8% (289/305 ops); ops left on the portable kernels: dim_order_ops._to_dim_order_copy.default x16

Conversion

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