---
license: apache-2.0
library_name: acaua
pipeline_tag: object-detection
tags:
  - object-detection
  - vision
  - acaua
  - native-pytorch-port
  - rtmdet
datasets:
  - coco
---

# RTMDet-x — acaua mirror (pure-PyTorch port)

Pure-PyTorch port of RTMDet-x (94.9M params, COCO box AP 52.8) 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.rtmdet`](https://github.com/CondadosAI/acaua/tree/main/src/acaua/adapters/rtmdet) — no `mmcv`, no `mmengine`, no `mmdet`, no `trust_remote_code`. The weights in this mirror are converted from the upstream mmdet `.pth` checkpoint to safetensors with the acaua adapter's state_dict key naming. They are NOT drop-in compatible with mmdet — they're designed to load cleanly into our `nn.Module` tree.

## Provenance

| | |
|---|---|
| Upstream code | [`open-mmlab/mmdetection`](https://github.com/open-mmlab/mmdetection) @ [`cfd5d3a985`](https://github.com/open-mmlab/mmdetection/tree/cfd5d3a985b0249de009b67d04f37263e11cdf3d) (Apache-2.0) |
| Upstream weights URL | `https://download.openmmlab.com/mmdetection/v3.0/rtmdet/rtmdet_x_8xb32-300e_coco/rtmdet_x_8xb32-300e_coco_20220715_230555-cc79b9ae.pth` |
| Upstream weights SHA256 | `cc79b9ae0970f7f597e2aa5f81663129f3aa24e5612549b4f1a383da7fff825d` |
| Conversion script | [`scripts/convert_rtmdet.py`](https://github.com/CondadosAI/acaua/blob/main/scripts/convert_rtmdet.py) |
| Paper | Lyu et al., *"RTMDet: An Empirical Study of Designing Real-Time Object Detectors"*, arXiv:[2212.07784](https://arxiv.org/abs/2212.07784) |
| Mirrored on | 2026-04-20 |
| Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) |

## Usage

```python
import acaua

model = acaua.Model.from_pretrained("CondadosAI/rtmdet_x_coco")
results = model.predict("image.jpg")
print(results.boxes, results.scores, results.labels)
```

## License and attribution

Redistributed under Apache-2.0, consistent with the upstream code (`open-mmlab/mmdetection`) and the weights released on `download.openmmlab.com`. The acaua adapter is itself a derivative work of the upstream PyTorch implementation — see [`NOTICE`](./NOTICE) for the required attribution chain (code AND weights).

## Citation

```bibtex
@misc{lyu2022rtmdet,
      title={RTMDet: An Empirical Study of Designing Real-Time Object Detectors},
      author={Chengqi Lyu and Wenwei Zhang and Haian Huang and Yue Zhou and Yudong Wang and Yanyi Liu and Shilong Zhang and Kai Chen},
      year={2022},
      eprint={2212.07784},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```