---
license: apache-2.0
library_name: acaua
pipeline_tag: keypoint-detection
tags:
  - pose-estimation
  - keypoint-detection
  - vision
  - acaua
  - native-pytorch-port
  - rtmpose
datasets:
  - coco
  - aic
---

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

This is a **pure-PyTorch port** of [RTMPose-tiny](https://github.com/open-mmlab/mmpose/tree/main/projects/rtmpose) hosted under `CondadosAI/` for use with the [acaua](https://github.com/CondadosAI/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`](https://huggingface.co/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`](https://github.com/open-mmlab/mmpose) @ `759b39c13fea6ba094afc1fa932f51dc1b11cbf9` |
| Upstream code (backbone) | [`open-mmlab/mmdetection`](https://github.com/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`](https://github.com/CondadosAI/acaua/blob/main/scripts/convert_rtmpose.py) |
| Bundled detector | [`CondadosAI/rtmdet_t_coco`](https://huggingface.co/CondadosAI/rtmdet_t_coco) |
| Paper | Jiang et al., *"RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose"*, arXiv:[2303.07399](https://arxiv.org/abs/2303.07399) |
| COCO val AP | 68.5 @ 256×192 (top-down, 17 keypoints) |
| Mirrored on | 2026-04-22 |
| Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) |

## Usage

```python
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`](./NOTICE) for the required attribution chain (code AND weights).

## Citation

```bibtex
@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}
}
```