ViG is a generic backbone trained on the ImageNet-1K dataset for vision tasks.

The primary use of ViG is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an GLA-based backbone. The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.

ViG is pretrained on ImageNet-1K with classification supervision. The training data is around 1.3M images from ImageNet-1K dataset. See more details in this paper.

ViG is evaluated on ImageNet-1K val set, more details can be found in this paper.

 @article{vig,
  title={ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention},
  author={Bencheng Liao and Xinggang Wang and Lianghui Zhu and Qian Zhang and Chang Huang},
  journal={arXiv preprint arXiv:2405.18425},
  year={2024}
}