uniformer_s_k400

UniFormer-S (Kinetics-400) — acaua mirror (pure-PyTorch port)

Pure-PyTorch port of UniFormer-S (video classification, trained on Kinetics-400 with 16-frame clips at sampling stride 8) hosted under CondadosAI/ for use with the acaua computer vision library.

The architecture has been re-implemented in pure PyTorch under acaua.adapters.uniformer.video — no mmcv, no mmengine, no mmaction2, no trust_remote_code, no timm runtime dependency. The weights are converted from the upstream .pth checkpoint to safetensors with acaua's state-dict key naming (backbone.* + head.fc.*). They are not drop-in compatible with timm or Sense-X/UniFormer loaders — they are designed to load cleanly into acaua's nn.Module tree under load_state_dict(strict=True).

Provenance

Usage via acaua

import acaua

# MIT-declared weights require the explicit opt-in.
model = acaua.Model.from_pretrained(
    "CondadosAI/uniformer_s_k400", allow_non_apache=True
)
result = model.predict("video.mp4")
print(result.labels)   # tuple of top-5 Kinetics-400 action labels
print(result.scores)   # aligned float32 probabilities

Requires pip install 'acaua[video]' for the TorchCodec-backed video decoder and a system-level ffmpeg install.

Files in this mirror

• model.safetensors — acaua-format weights (key-remapped, verified round-trip under load_state_dict(strict=True) at conversion time).
• labels.json — JSON array of 400 Kinetics-400 action labels in index order. Read by the adapter at load time.
• config.json — minimal metadata: acaua_task=video_classification, num_frames, num_classes.
• NOTICE — attribution chain (code AND weights).
• LICENSE — Apache-2.0.

License and attribution

The adapter code (this repository) is redistributed under Apache-2.0. The underlying weights carry upstream's MIT declaration (compatible, permissively redistributable). The acaua UniFormer-video adapter is itself a derivative work of the upstream PyTorch implementation — see NOTICE for the required attribution chain.

Citation

@misc{li2022uniformervideo,
  title        = {UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning},
  author       = {Li, Kunchang and Wang, Yali and Gao, Peng and Song, Guanglu and Liu, Yu and Li, Hongsheng and Qiao, Yu},
  year         = {2022},
  eprint       = {2201.04676},
  archivePrefix = {arXiv},
  primaryClass = {cs.CV},
}