docs: acaua mirror model card with code+weights provenance

README.md

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+---
+license: mit
+library_name: acaua
+pipeline_tag: keypoint-detection
+tags:
+  - pose-estimation
+  - keypoint-detection
+  - vision
+  - acaua
+  - native-pytorch-port
+datasets:
+  - coco
+---
+
+# UniFormer-S COCO top-down pose — acaua mirror (pure-PyTorch port)
+
+Pure-PyTorch port of **UniFormer-S** (top-down COCO 17-keypoint pose at
+256x192) 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.uniformer.pose` — no `mmcv`, no `mmengine`, no
+`mmpose`, no `trust_remote_code`, no `timm` runtime dependency. The
+backbone reuses `UniFormer2DDense` (already shipped via PR #9 for the
+Stage 1.5 dense-prediction work); the pose head is a fresh port of
+mmpose's `TopDownSimpleHead`. Decode is the upstream
+`post_process='default'` path (argmax + 0.25-pixel shift, NOT the DARK
+unbiased decoder). Inverse-warp uses the shared
+`acaua.pose.topdown_utils` module (introduced in PR #8 ahead of this
+stage).
+
+## Provenance
+
+| | |
+|---|---|
+| Upstream code | [`Sense-X/UniFormer`](https://github.com/Sense-X/UniFormer) @ `main` (Apache-2.0); files derived: `pose_estimation/mmpose/models/backbones/uniformer.py` (backbone, identical to detection variant up to module-class identity) + `pose_estimation/mmpose/models/keypoint_heads/top_down_simple_head.py` (head) |
+| Upstream weights | Google Drive file id `162R0JuTpf3gpLe1IK6oxRoQK7JSj4ylx`, filename `top_down_256x192_global_small.pth` (101MB) |
+| Upstream SHA256 | `d77059e3e9322c0e20dc89dc0cf2a583ffe2ced7d3e9b350233738add570bc30` |
+| Upstream report | AP 74.0 / AP@50 90.3 / AP@75 82.2 on COCO val 2017, 256x192, single-scale |
+| Architecture | UniFormer-S backbone (`hybrid=False, windows=False`, depth=[3,4,8,3], embed_dims=[64,128,320,512], head_dim=64) + TopDownSimpleHead (3x ConvTranspose2d-stride-2 + BN+ReLU upsample, 1x1 conv to 17 channels) |
+| Total params | 25.23M (backbone 21.04M + head 4.19M) |
+| Mirrored on | 2026-04-25 |
+| Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) |
+
+## Usage via acaua
+
+```python
+import acaua
+
+# MIT-declared weights -> explicit opt-in (same posture as RTMPose +
+# UniFormer image / video classifications). The bundled RTMDet-tiny
+# detector is loaded automatically from CondadosAI/rtmdet_t_coco.
+model = acaua.Model.from_pretrained(
+    "CondadosAI/uniformer_s_coco_pose", allow_non_apache=True
+)
+
+result = model.predict("image.jpg")
+print(result.keypoints.shape)        # (N_persons, 17, 2)
+print(result.keypoint_scores.shape)  # (N_persons, 17)
+
+# COCO skeleton edges are surfaced on the adapter:
+import supervision as sv
+sv.EdgeAnnotator(edges=model.skeleton).annotate(scene, result.to_supervision())
+```
+
+## Files in this mirror
+
+- `model.safetensors` — full pose model weights (backbone + head, 352
+  tensors). Loaded under `load_state_dict(strict=True)` at adapter
+  init time.
+- `config.json` — `acaua_task=pose`, COCO-17 `keypoint_names` +
+  `skeleton`, `detector_repo_id=CondadosAI/rtmdet_t_coco`. Adapter
+  surfaces these as `model.keypoint_names` / `model.skeleton`.
+- `NOTICE` — attribution chain (code AND weights).
+- `LICENSE` — Apache-2.0.
+
+## License and attribution
+
+The adapter code is redistributed under Apache-2.0. The underlying
+weights carry upstream's MIT declaration (compatible). The acaua
+UniFormer-pose adapter is a derivative work of the upstream PyTorch
+implementation — see [`NOTICE`](./NOTICE) for the attribution chain.
+
+## Citation
+
+```bibtex
+@inproceedings{li2022uniformer,
+  title     = {UniFormer: Unifying Convolution and Self-attention for Visual Recognition},
+  author    = {Li, Kunchang and Wang, Yali and Zhang, Junhao and Gao, Peng and Song, Guanglu and Liu, Yu and Li, Hongsheng and Qiao, Yu},
+  booktitle = {ICLR},
+  year      = {2022},
+}
+```