Input
: [[1, 3, 256, 256]] — RGB, ImageNet norm, 256x256 crop around an animal (detect first, then crop and resize to this aspect)
Output
: SimCC pair: x [1,17,512] and y [1,17,512] — 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_animal_xnnpack_fp32.pte
54.5
1.000000
9.4
*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: 91.4 ms).
Builds that did not earn a slot
Core ML (fp16, iOS) is not shipped
: measured in the units that matter for this model — fraction of keypoints landing within 4 px of fp32: median 0.9412 over 10 real images, worst 0.7647.
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, 17, 512]
2.783e-06
1.000000
1
[1, 17, 512]
7.629e-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
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