mlboydaisuke / RTMPose-m-Face-ExecuTorch

huggingface.co
Total runs: 21
24-hour runs: 0
7-day runs: -1
30-day runs: -37
Model's Last Updated: August 15 2026
keypoint-detection

Introduction of RTMPose-m-Face-ExecuTorch

Model Details of RTMPose-m-Face-ExecuTorch

rtmpose_m_face — ExecuTorch

  • Source : open-mmlab/mmpose RTMPose (rtmpose-m_simcc-face6_pt-in1k_120e-256x256-72a37400)
  • License : Apache-2.0
  • Input : [[1, 3, 256, 256]] — RGB, ImageNet norm, 256x256 crop around a face (detect first, then crop and resize to this aspect)
  • Output : SimCC pair: x [1,106,512] and y [1,106,512] — a 1-D distribution per keypoint per axis. Decode: keypoint k sits at (argmax(x[k]) / 2, argmax(y[k]) / 2) in crop pixels; the max value doubles as the confidence.
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 rtmpose_m_face_xnnpack_fp32.pte 67.9 1.000000 10.4
Core ML (fp16, iOS) rtmpose_m_face_coreml_all.pte 34.4 0.999897 3.3

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: 94.4 ms).

Checked in the task's own units

Correlation is a first filter. These are the numbers that decide:

  • Core ML (fp16, iOS) — measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.9811 over 10 real images, worst 0.9151.
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, 106, 512] 1.147e-05 1.000000
1 [1, 106, 512] 9.179e-06 1.000000

XNNPACK delegate coverage (fp32): 93.9% (306/326 ops); ops left on the portable kernels: dim_order_ops._to_dim_order_copy.default x8, aten.unsqueeze_copy.default x3, aten.split_with_sizes_copy.default x3, aten.sum.dim_IntList x2, aten.pow.Tensor_Scalar x2, aten.squeeze_copy.dims x2

Conversion

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

Notes : Top-down: crop one face first. In practice a portrait framed on the head works directly, which is how the card check exercises it.


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.

Runs of mlboydaisuke RTMPose-m-Face-ExecuTorch on huggingface.co

21
Total runs
0
24-hour runs
0
3-day runs
-1
7-day runs
-37
30-day runs

More Information About RTMPose-m-Face-ExecuTorch huggingface.co Model

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RTMPose-m-Face-ExecuTorch install

RTMPose-m-Face-ExecuTorch is an open source model from GitHub that offers a free installation service, and any user can find RTMPose-m-Face-ExecuTorch on GitHub to install. At the same time, huggingface.co provides the effect of RTMPose-m-Face-ExecuTorch install, users can directly use RTMPose-m-Face-ExecuTorch installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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