Cut-Thumbnail (Accepted at ACM MULTIMEDIA 2021)

Tianshu Xie, Xuan Cheng, Xiaomin Wang, Minghui Liu, Jiali Deng, Tao Zhou, Ming Liu

This is the official Pytorch implementation of Cut-Thumbnail in the paper Cut-Thumbnail:A Novel Data Augmentation for Convolutional Neural Network.

This implementation is based on these repositories:

• PyTorch ImageNet Example
• CutMix-PyTorch
• Pytorch-classification

Main Requirements

• torch == 1.0.1
• torchvision == 0.2.0
• Python 3

Training Examples

• Mixed Single Thumbnail

python train.py -d [datasetlocation] --depth 50 --mode mst --size 112 --lam 0.25 --participation_rate 0.8

• Self Thumbnail

python train.py -d [datasetlocation] --depth 50 --mode st --size 112 --lam 0.25 --participation_rate 0.8

Results

• ImageNet Results

Model	Accuracy (%)
ResNet50 + CutMix	78.60*
ResNet50 + Cut-Thumbnail (ST)	77.74
ResNet50 + Cut-Thumbnail (MST)	79.21

* denotes results reported in the original papers.

• CIFAR-100 Results

Model	Accuracy (%)
WideResNet-28-10 + Cut-Thumbnail (ST)	81.41
WideResNet-28-10 + Cut-Thumbnail (MST)	83.35

• CUB-200-2011 Results

Model	Accuracy (%)
ResNet50 + Cut-Thumbnail (ST)	85.72
ResNet50 + Cut-Thumbnail (MST)	86.56
ResNet50 + Cut-Thumbnail (MDT)	86.72

Citation

If you find our paper and this repo useful, please cite as

@inproceedings{xie20cut-thumbnail,
    author = {Xie, Tianshu and Cheng, Xuan and Wang, Xiaomin and Liu, Minghui and Deng, Jiali and Zhou, Tao and Liu, Ming},
    title = {Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network},
    year = {2021},
    isbn = {9781450386517},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3474085.3475302},
    doi = {10.1145/3474085.3475302},
    booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
    pages = {1627–1635},
    numpages = {9},
    location = {Virtual Event, China},
    series = {MM '21}
}