CondadosAI/rtmpose_t_coco

RTMPose-tiny (COCO 17-keypoint) — acaua mirror (pure-PyTorch port)

This is a pure-PyTorch port of RTMPose-tiny hosted under CondadosAI/ for use with the acaua computer vision library.

The architecture has been re-implemented in pure PyTorch under acaua.adapters.rtmpose — no mmcv, no mmengine, no mmpose, no trust_remote_code. The weights in this mirror are converted from the upstream .pth checkpoint to safetensors with the acaua adapter's state_dict key naming, and load cleanly via load_state_dict(strict=True) into our nn.Module tree.

RTMPose is a top-down model: it consumes a person bounding box and predicts COCO 17-keypoint pose. The acaua adapter bundles CondadosAI/rtmdet_t_coco as the person detector, giving you a single-call predict(image) API that returns boxes + keypoints together.

Provenance

Upstream code (architecture)	open-mmlab/mmpose @ 759b39c13fea6ba094afc1fa932f51dc1b11cbf9
Upstream code (backbone)	open-mmlab/mmdetection @ cfd5d3a985b0249de009b67d04f37263e11cdf3d (CSPNeXt)
Upstream weights URL	https://download.openmmlab.com/mmpose/v1/projects/rtmposev1/rtmpose-tiny_simcc-aic-coco_pt-aic-coco_420e-256x192-cfc8f33d_20230126.pth
Upstream weights SHA256	e84eb5b9ee9432259bdd19d6a01156604ba27139ca6373ddb4ee7aa290d528e9
Conversion script	scripts/convert_rtmpose.py
Bundled detector	CondadosAI/rtmdet_t_coco
Paper	Jiang et al., "RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose", arXiv:2303.07399
COCO val AP	68.5 @ 256×192 (top-down, 17 keypoints)
Mirrored on	2026-04-22
Mirrored by	CondadosAI/acaua

Usage

import acaua
import supervision as sv

model = acaua.Model.from_pretrained("CondadosAI/rtmpose_t_coco")
result = model.predict("photo.jpg")

# `result` is a PoseResult: boxes (from RTMDet), keypoints (from RTMPose).
kp = result.to_supervision()  # supervision.KeyPoints
sv.EdgeAnnotator(edges=model.skeleton).annotate(scene, kp)

License and attribution

Redistributed under Apache-2.0, consistent with both the upstream code (mmpose / mmdetection, both Apache-2.0 by OpenMMLab) and the upstream weights declaration. The acaua adapter is a derivative work of the upstream PyTorch implementations — see NOTICE for the required attribution chain (code AND weights).

Citation

@misc{jiang2023rtmpose,
  title={RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose},
  author={Tao Jiang and Peng Lu and Li Zhang and Ningsheng Ma and Rui Han and Chengqi Lyu and Yining Li and Kai Chen},
  year={2023},
  eprint={2303.07399},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}