mlboydaisuke / MoGe-2-ViT-S-ExecuTorch

huggingface.co
Total runs: 149
24-hour runs: 0
7-day runs: -4
30-day runs: -48
Model's Last Updated: September 04 2026
depth-estimation

Introduction of MoGe-2-ViT-S-ExecuTorch

Model Details of MoGe-2-ViT-S-ExecuTorch

moge2_vits — ExecuTorch XNNPACK

  • Source : Ruicheng/moge-2-vits-normal
  • License : MIT
  • Input : [[1, 3, 518, 518]] — RGB, ImageNet norm, 518x518
  • Output : points [1,H,W,3] metric point map, normal [1,H,W,3], mask [1,H,W] validity, metric_scale [1]
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 moge2_vits_xnnpack_fp32.pte 140.8 0.999998 750.9
fp16 moge2_vits_xnnpack_fp16.pte 96.6 0.434851 — see below 1714.8
int8 (dynamic) moge2_vits_xnnpack_int8.pte 76.4 0.998758 746.5

*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: 345.4 ms).

Checked in the task's own units

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

  • fp16 — correlation reads 0.43 on this build, and that number is an artifact: one of the four outputs is a near-binary validity mask whose raw logits correlate badly while the thresholded mask is identical. Measured properly against fp32 — mask IoU 1.0000, point map cosine 1.000000, normals cosine 1.000000, metric scale within 0.6% — the geometry is unchanged.
  • int8 (dynamic) — point map and normals hold at cosine 0.999 against the fp32 build.
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, 518, 518, 3] 1.431e-06 1.000000
1 [1, 518, 518, 3] 1.878e-06 1.000000
2 [1, 518, 518] 1.192e-07 0.999998
3 [1] 5.960e-07 nan

XNNPACK delegate coverage (fp32): 53.7% (623/1160 ops); ops left on the portable kernels: aten.arange.start_step x90, aten.clamp.default x80, aten.index.Tensor x80, aten.expand_copy.default x65, aten.squeeze_copy.dims x38, aten.native_layer_norm.default x26, aten.mul.Scalar x24, aten.logical_not.default x24, aten.where.self x22, dim_order_ops._to_dim_order_copy.default x20, aten.eq.Scalar x12, aten.full_like.default x12, aten.any.dim x12, aten.lt.Scalar x10, aten.sub.Tensor x10, aten.unsqueeze_copy.default x5, aten.sum.dim_IntList x2, aten.pow.Tensor_Scalar x2, aten._upsample_bilinear2d_aa.default x1, aten.select_copy.int x1, aten.split_with_sizes_copy.default x1

Conversion

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

Runs of mlboydaisuke MoGe-2-ViT-S-ExecuTorch on huggingface.co

149
Total runs
0
24-hour runs
6
3-day runs
-4
7-day runs
-48
30-day runs

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