mlboydaisuke / 6DRepNet-HeadPose-ExecuTorch

huggingface.co
Total runs: 45
24-hour runs: 0
7-day runs: -22
30-day runs: -18
Model's Last Updated: August 15 2026
image-classification

Introduction of 6DRepNet-HeadPose-ExecuTorch

Model Details of 6DRepNet-HeadPose-ExecuTorch

sixdrepnet_headpose — ExecuTorch XNNPACK

  • Source : thohemp/6DRepNet + osanseviero/6DRepNet_300W_LP_AFLW2000 weights
  • License : MIT
  • Input : [[1, 3, 224, 224]] — RGB, ImageNet norm, 224x224 face crop
  • Output : 6D rotation representation [1,6]. To a rotation matrix: b1 = normalize(v[0:3]); b2 = normalize(v[3:6] - (b1·v[3:6])b1); b3 = cross(b1, b2); R = [b1 b2 b3]. Euler angles follow from R.
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 sixdrepnet_headpose_xnnpack_fp32.pte 157.3 1.000000 7.6

*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: 16.3 ms).

Precisions that did not earn a slot
  • fp16 is not shipped : it comes out at 100% of the fp32 file (157.3 MB vs 157.3 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.
  • int8 is not shipped : worst-output corr 0.815 against fp32 eager, below the 0.95 bar for this precision. The file converts and runs; the numbers do not hold up, so it is left out rather than shipped with a warning.
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, 6] 8.643e-07 1.000000

XNNPACK delegate coverage (fp32): 100.0% (59/59 ops)

Conversion

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

Notes (int8) : int8 is measured but not shipped. Correlation is a weak read on a six-element output, so the check that matters is the angle between the fp32 and int8 rotations: median 46.5 deg over ten faces, worst 104 deg. That is the known failure mode of re-parameterized RepVGG under post-training quantization — its fused branches leave weight ranges too wide for an int8 grid — and it needs quantization-aware training rather than anything on the export side.

Runs of mlboydaisuke 6DRepNet-HeadPose-ExecuTorch on huggingface.co

45
Total runs
0
24-hour runs
-6
3-day runs
-22
7-day runs
-18
30-day runs

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6DRepNet-HeadPose-ExecuTorch install

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