DWPose (RTMW + YOLOX) — ONNX
The two ONNX graphs behind the DWPose whole-body pose detector, mirrored for
SceneWorks so the pose_detect job and the
OpenPose control-conditioning lanes can be installed from the Model Manager instead of
fetching archives mid-job.
This is rtmlib's performance preset: a YOLOX-m person detector followed by an RTMW-x
COCO-WholeBody-133 SimCC pose model. Both run under onnxruntime — CoreML EP on Apple
Silicon, CUDA EP (CPU fallback) elsewhere.
| file | role | input | size (bytes) |
|---|---|---|---|
yolox_m_8xb8-300e_humanart-c2c7a14a.onnx | person boxes (NMS baked in) | [1,3,640,640] f32, letterboxed, pad 114 | 101,400,344 |
rtmw-dw-x-l_simcc-cocktail14_270e-384x288_20231122.onnx | whole-body 133-keypoint SimCC | [1,3,384,288] f32 BGR, mean/std normalized | 229,320,930 |
Both graphs are ONNX IR version 6, producer: pytorch 1.9.
Provenance
These files are extracted verbatim, with no modification of any kind, from the ONNX SDK
archives published by OpenMMLab. No re-export, no conversion, no quantization, no graph
surgery — the bytes are the end2end.onnx entry of each archive, renamed to the upstream
archive's stem so the two graphs are distinguishable side by side.
Source archives:
| archive | size (bytes) | sha256 |
|---|---|---|
yolox_m_8xb8-300e_humanart-c2c7a14a.zip | 94,223,081 | a000224fd8ba283202bc62d4a5fcdfe353adb9f468777dbac1ea2ada2093adde |
rtmw-dw-x-l_simcc-cocktail14_270e-384x288_20231122.zip | 213,433,855 | a87e1af41a0a067776dba7d46e1c21c8f6e9f18e247e0e606718dd1f31e96ffd |
Extraction map:
yolox_m_8xb8-300e_humanart-c2c7a14a.zip
20230928/yolox_onnx/yolox_m_8xb8-300e_humanart-c2c7a14a/end2end.onnx
-> yolox_m_8xb8-300e_humanart-c2c7a14a.onnx
rtmw-dw-x-l_simcc-cocktail14_270e-384x288_20231122.zip
end2end.onnx
-> rtmw-dw-x-l_simcc-cocktail14_270e-384x288_20231122.onnx
Files in this repo:
sha256 yolox_m_8xb8-300e_humanart-c2c7a14a.onnx 3dea6513388889f0fff4b77bf7a26013600321b9eb9ceb0e9a400a82572f5f23
sha256 rtmw-dw-x-l_simcc-cocktail14_270e-384x288_20231122.onnx bd033156e5104c4f5d2edfe0453e02661e30a2f3da453ec93c8764d561b83054
The archives dropped alongside each end2end.onnx (deploy.json, pipeline.json,
detail.json, and two sample renders) are MMDeploy SDK metadata that SceneWorks does not
read; they are not mirrored here. Re-download either archive, verify its sha256 above, and
take the end2end.onnx entry to reproduce these files bit for bit.
Upstream:
- Project: open-mmlab/mmpose
- RTMW:
projects/rtmpose— RTMW whole-body models,cocktail14training mixture - Model zoo / download index: RTMPose model zoo
- DWPose (the preset these two graphs form): IDEA-Research/DWPose, as packaged by rtmlib
License
Apache-2.0, following mmpose, whose LICENSE (reproduced here verbatim, including its
Copyright 2018-2020 Open-MMLab. All rights reserved. notice) is the license OpenMMLab
publishes these releases under.
Note, without overclaiming: mmpose's LICENSE covers the OpenMMLab project. It does not
itself assert terms for the training data behind these particular checkpoints — the YOLOX
detector is trained on HumanArt, and the RTMW pose model on the COCO-WholeBody
"cocktail14" mixture. Those datasets carry their own terms from their own publishers,
which mmpose's LICENSE file does not speak to and which this mirror therefore does not
represent. If your use is sensitive to dataset provenance, review the HumanArt and
COCO-WholeBody terms directly.