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, cocktail14 training 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.

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