| --- |
| license: mit |
| library_name: acaua |
| pipeline_tag: image-classification |
| tags: |
| - image-classification |
| - vision |
| - acaua |
| - native-pytorch-port |
| datasets: |
| - imagenet-1k |
| --- |
| |
| # UniFormer-S — acaua mirror (pure-PyTorch port) |
|
|
| Pure-PyTorch port of **UniFormer-S** 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` — no `mmcv`, no `mmengine`, no `mmseg`, |
| no `trust_remote_code`, no `timm` runtime dependency. The weights in |
| this mirror 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 |
|
|
| | | | |
| |---|---| |
| | Upstream code | [`Sense-X/UniFormer`](https://github.com/Sense-X/UniFormer) @ `main` (Apache-2.0) | |
| | Upstream weights | [`Sense-X/uniformer_image`](https://huggingface.co/Sense-X/uniformer_image) at revision `ae70a7dc23e2d85972370501db47717efcd2c6f1` (MIT) | |
| | Upstream file | `uniformer_small_in1k.pth` | |
| | Upstream SHA256 | `fd192c31f8bd77670de8f171111bd51f56fd87e6aea45043ab2edc181e1fa775` | |
| | Upstream factory | `uniformer_small()` in `image_classification/models/uniformer.py` | |
| | Conversion script | [`scripts/convert_uniformer.py`](https://github.com/CondadosAI/acaua/blob/main/scripts/convert_uniformer.py) | |
| | Paper | Li et al., [*UniFormer: Unifying Convolution and Self-attention for Visual Recognition*](https://arxiv.org/abs/2201.09450), ICLR 2022 | |
| | Params | 22M | |
| | Top-1 (ImageNet-1k, 224×224) | 82.9% | |
| | FLOPs (224×224) | 3.6G | |
| | Mirrored on | 2026-04-24 | |
| | Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) | |
|
|
| ## Usage via acaua |
|
|
| ```python |
| import acaua |
| model = acaua.Model.from_pretrained("CondadosAI/uniformer_s_in1k") |
| result = model.predict("image.jpg") |
| print(result.labels) # tuple of top-5 ImageNet class names |
| print(result.scores) # aligned float32 probabilities |
| ``` |
|
|
| ## 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 1000 ImageNet-1k class names in |
| index order. Read by the adapter at load time. |
| - `NOTICE` — attribution chain (code AND weights). |
| - `LICENSE` — Apache-2.0. |
|
|
| ## License and attribution |
|
|
| The adapter code (this mirror) is redistributed under Apache-2.0. The |
| underlying weights carry upstream's MIT declaration (compatible, |
| permissively re-distributable). The acaua UniFormer adapter is itself a |
| derivative work of the upstream PyTorch implementation — see |
| [`NOTICE`](./NOTICE) for the required 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}, |
| } |
| ``` |
|
|
