MobileViT (ICLR'2022)
@article{mehta2021mobilevit,
title={Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer},
author={Mehta, Sachin and Rastegari, Mohammad},
journal={arXiv preprint arXiv:2110.02178},
year={2021}
}
MobileViTV2 (ArXiv'2022)
@article{mehta2022separable,
title={Separable self-attention for mobile vision transformers},
author={Mehta, Sachin and Rastegari, Mohammad},
journal={arXiv preprint arXiv:2206.02680},
year={2022}
}
Segmentor | Pretrain | Backbone | Crop Size | Schedule | Train/Eval Set | mIoU (EMA) | Download |
---|---|---|---|---|---|---|---|
DeepLabV3 | ImageNet-1k-224x224 | MobileViT-Small | 512x512 | LR/POLICY/BS/EPOCH: 0.0009/cosine/64/50 | voc trainaug + cocovocsubet train / voc val | cfg | model | log | |
DeepLabV3 | ImageNet-1k-224x224 | MobileViTV2-0.50 | 512x512 | LR/POLICY/BS/EPOCH: 0.0005/cosine/64/50 | voc trainaug + cocovocsubet train / voc val | cfg | model | log | |
DeepLabV3 | ImageNet-1k-224x224 | MobileViTV2-1.00 | 512x512 | LR/POLICY/BS/EPOCH: 0.0005/cosine/64/50 | voc trainaug + cocovocsubet train / voc val | cfg | model | log |
You can also download the model weights from following sources:
- BaiduNetdisk: https://pan.baidu.com/s/1gD-NJJWOtaHCtB0qHE79rA with access code s757