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:
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}
}