mlboydaisuke / TwinLiteNet-ExecuTorch

huggingface.co
Total runs: 47
24-hour runs: 3
7-day runs: 4
30-day runs: -4
Model's Last Updated: August 15 2026
image-segmentation

Introduction of TwinLiteNet-ExecuTorch

Model Details of TwinLiteNet-ExecuTorch

twinlitenet — ExecuTorch XNNPACK

  • Source : chequanghuy/TwinLiteNet (pretrained/best.pth)
  • License : MIT
  • Input : [[1, 3, 360, 640]] — RGB 0-1, 360x640
  • Output : drivable area [1,2,360,640] + lane line [1,2,360,640]
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 twinlitenet_xnnpack_fp32.pte 1.8 1.000000 32.5

*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: 23.9 ms).

Precisions that did not earn a slot
  • fp16 is not shipped : it comes out at 100% of the fp32 file (1.8 MB vs 1.8 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, 2, 360, 640] 1.240e-05 1.000000
1 [1, 2, 360, 640] 1.287e-05 1.000000

XNNPACK delegate coverage (fp32): 79.7% (177/222 ops); ops left on the portable kernels: aten.gt.Scalar x20, aten.where.self x20, aten.avg_pool2d.default x3, aten.max.dim x1, aten.expand_copy.default x1

Conversion

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

Notes : int8 does not run on executorch 1.4.0, and the reason is a collision between two workarounds rather than anything about this model. PReLU segfaults on the XNNPACK delegate (upstream #17559, fix #21480 not in the stable wheel), so it has to be excluded from partitioning — and a quantized graph with a portable PReLU between delegated convolutions fails shape propagation at execute. Three conv+PReLU layers reproduce it; the same graph in fp32 is corr 1.000000. Reported at https://github.com/pytorch/executorch/pull/21480

Runs of mlboydaisuke TwinLiteNet-ExecuTorch on huggingface.co

47
Total runs
3
24-hour runs
4
3-day runs
4
7-day runs
-4
30-day runs

More Information About TwinLiteNet-ExecuTorch huggingface.co Model

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TwinLiteNet-ExecuTorch install

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