uniformer_s_coco_pose

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 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

Usage via acaua

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 for the attribution chain.

Citation

@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},
}