uniformer_s_h14_backbone_coco

UniFormer-S_h14 — COCO-pretrained backbone (acaua mirror)

Backbone-only mirror. This is not a runnable detection or segmentation model on its own. It ships the UniFormer-S backbone weights as trained jointly with the upstream Mask R-CNN detector on COCO; the task head (FPN + RPN + ROI + mask head) has been stripped.

The mirror exists to gate acaua's Stage 1.5 UniFormer-dense-prediction spike — testing whether UniFormer-S can be hosted as a backbone inside torchvision.models. detection.MaskRCNN and transformers.UperNetForSemanticSegmentation without forcing users to download the full 165MB upstream checkpoint from Google Drive every time.

Provenance

Usage via acaua

Not usable through acaua.Model.from_pretrained yet — this ships as backbone-only infrastructure for the upcoming Stage 1.5.b spike. Direct usage:

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from acaua.adapters.uniformer._backbone_dense import UniFormer2DDense
from acaua.adapters.uniformer._config import DENSE_VARIANTS

path = hf_hub_download(
    "CondadosAI/uniformer_s_h14_backbone_coco", "model.safetensors"
)
sd = load_file(path)
m = UniFormer2DDense(DENSE_VARIANTS["s_h14_det"])
m.load_state_dict(sd, strict=True)
m.eval()
# ...plug into a detection/segmentation head...

The backbone emits 4 multi-scale feature maps with strides 4/8/16/32. At 800x1280 input, the shapes are (1, 64, 200, 320), (1, 128, 100, 160), (1, 320, 50, 80), (1, 512, 25, 40).

Files

• model.safetensors — backbone weights (332 tensors, 21M params).
• NOTICE — attribution chain (code + weights).
• LICENSE — Apache-2.0.

License

Apache-2.0. Redistribution of the upstream UniFormer code and weights under their original declaration — 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},
}