docs: acaua mirror model card with code+weights provenance

README.md

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+---
+license: apache-2.0
+library_name: acaua
+pipeline_tag: object-detection
+tags:
+  - object-detection
+  - vision
+  - acaua
+  - native-pytorch-port
+  - rtmdet
+datasets:
+  - coco
+---
+
+# RTMDet-m — acaua mirror (pure-PyTorch port)
+
+Pure-PyTorch port of RTMDet-m (24.7M params, COCO box AP 49.4) 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_m_8xb32-300e_coco/rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth` |
+| Upstream weights SHA256 | `229f527ca88498e8894a778a62a878a322b4a3ea2cae09ea537d34b7e907792b` |
+| 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_m_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}
+}
+```