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sapiens2-onnx
WeReCooking/sapiens2-onnx
sapiens2-onnx is a depth estimation model from WeReCooking. Use it for the depth estimation task on the model card, and read the license before you ship it in a product. It is set up for sapiens2. The card lists the license as other.
CPU-friendly ONNX exports of Meta's facebook/sapiens2-. 15 task heads across 4 tasks and 4 sizes.
Access
Public
Updated May 12, 2026
Repo size
62.8 GB
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0
Public
Repo makeup
Click a slice to open those files.
.data62.6 GB · 100%
At a glance
- Task
- Depth Estimation
- Library
- sapiens2
- License
- other
- Access
- Public
- Created
- May 12, 2026
- Updated
- May 12, 2026
- SHA
- 2d6b460e
Spaces
Model card
From the Hugging Face model README
Sapiens2 ONNX
CPU-friendly ONNX exports of Meta's facebook/sapiens2-*. 15 task heads across 4 tasks and 4 sizes.
Folder layout
Each task has its own folder. Each model is split into a small .onnx graph file plus a .onnx.data external sidecar (both must live in the same directory at download time).
| Task | 0.4b | 0.8b | 1b | 5b |
|---|---|---|---|---|
| seg | seg/seg_0.4b_fp16.onnx (777 MB, fp16) | seg/seg_0.8b_fp32.onnx (3.3 GB) | seg/seg_1b_fp32.onnx (5.9 GB) | seg/seg_5b_int8.onnx (5.2 GB) |
| normal | normal/normal_0.4b_fp32.onnx (1.7 GB) | normal/normal_0.8b_fp32.onnx (3.5 GB) | normal/normal_1b_fp32.onnx (6.2 GB) | normal/normal_5b_int8.onnx (6.1 GB) |
| pointmap | pointmap/pointmap_0.4b_fp32.onnx (2.0 GB) | pointmap/pointmap_0.8b_fp32.onnx (3.9 GB) | pointmap/pointmap_1b_fp32.onnx (6.5 GB) | pointmap/pointmap_5b_int8.onnx (6.2 GB) |
| pose | pose/pose_0.4b_fp32.onnx (1.6 GB) | pose/pose_0.8b_fp32.onnx (3.4 GB) | pose/pose_1b_fp32.onnx (6.1 GB) | not shipped |
Cosine similarity vs the PyTorch reference is 0.999 or better on every shipped file.
Precision notes
- seg-0.4b is fp16 (50 percent smaller than fp32 and verified cos 0.99999)
- 0.4b/0.8b/1b for normal, pointmap, pose are fp32. Naive fp16 cast produces NaN (normal L2-normalize divides near zero) or cos around 0.7 (pointmap metric scale, pose sigmoid heatmaps saturate)
- 5B variants are INT8 (per-channel symmetric, MatMulIntegerToFloat)
- pose-5b is not shipped (the int8 quantize attempt did not complete on the available hardware)
Inference
import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
# Download both the .onnx graph and the .onnx.data sidecar side by side
for fn in ("seg/seg_0.4b_fp16.onnx", "seg/seg_0.4b_fp16.onnx.data"):
hf_hub_download(repo_id="WeReCooking/sapiens2-onnx", filename=fn, local_dir=".")
sess = ort.InferenceSession("seg/seg_0.4b_fp16.onnx", providers=["CPUExecutionProvider"])
# Input expects (N, 3, 1024, 768) fp32 BGR mean-subtracted preprocessed tensor
out = sess.run(None, {"input": preprocessed})
For a standalone CLI without sapiens2 or PyTorch, see app.py onnx ... in the source Space WeReCooking/sapiens2-cpu.
License
Same as upstream facebook/sapiens2-*.
- Task
- Depth Estimation
- Library
- sapiens2
- License
- other
- Created
- May 12, 2026
- Updated
- May 12, 2026