offical_mobilevit/examples/range_augment

• RangeAugment: Efficient Online Augmentation with Range Learning
• • Citation

RangeAugment: Efficient Online Augmentation with Range Learning

RangeAugment is an automatic augmentation method that allows us to learn model- and task-specific magnitude range of each augmentation operation.

We provide training and evaluation code along with pretrained models and configuration files for the following tasks:

1. Image Classification on the ImageNet dataset
2. Semantic segmentation on the ADE20k and the PASCAL VOC datasets
3. Object detection on the MS-COCO dataset
4. Contrastive Learning using Image-Text pairs
5. Distillation on the ImageNet dataset

Note: In the codebase, we refer RangeAugment as Neural Augmentor (or NA).

Citation

If you find our work useful, please cite:

@article{mehta2022rangeaugment,
  title={RangeAugment: Efficient Online Augmentation with Range Learning},
  author = {Mehta, Sachin and Naderiparizi, Saeid and Faghri, Fartash and Horton, Maxwell and Chen, Lailin and Farhadi, Ali and Tuzel, Oncel and Rastegari, Mohammad},
  journal={arXiv preprint arXiv:2212.10553},
  year={2022},
  url={https://arxiv.org/abs/2212.10553},
}