https://github.com/ceciliaresearch/MixedExample
https://github.com/ceciliaresearch/MixedExample
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/ceciliaresearch/MixedExample
- Owner: ceciliaresearch
- Created: 2019-01-19T06:26:00.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-01-19T06:52:58.000Z (almost 7 years ago)
- Last Synced: 2024-12-21T02:33:24.336Z (about 1 year ago)
- Language: Python
- Size: 7.81 KB
- Stars: 17
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Mixup - [Code
README
# Improved Mixed-Example Data Augmentation
This repository provides the code for our paper, *Improved Mixed-Example Data Augmentation*.
Code has been tested with TensorFlow version 1.12.
## Usage
First, make sure CIFAR-10 or CIFAR-100 has been downloaded and extracted to `cifar10_data/cifar-10-batches-bin` or `cifar100_data/cifar-100-binary`.
After that, basic usage is as follows:
```
python train.py --mixed_example_method=vh_mixup --model_dir=cifar10_models/vh_mixup --dataset=cifar10 --weight_decay=1e-4
```
## Notes
On CIFAR-10, use a value of 1e-4 for weight decay for all mixed-example methods, and a value of 5e-4 for the baseline. On CIFAR-100, use a value of 5e-4 for all methods.
Due to the large maximum learning rate used by the default ResNet, training may be unstable for some methods. We recommend lowering the maximum learning rate (in `cifar_model_fn`) from `0.1 ` to `0.75` or, as done in the paper, running multiple copies for a few epochs, and only continuing the ones that didn't exhibit instability. Specifically, for the paper we ran 20 copies of each method for 3 epochs, continuing the 3 models with the lowest training loss.
## Citation
If you use this code, please cite our paper:
Improved Mixed-Example Data Augmentation.
Cecilia Summers and Michael J. Dinneen.
*IEEE Winter Conference on Applications of Computer Vision (WACV)*, 2019.
BibTeX:
```
@inproceedings{summers2019improved,
title={Improved Mixed-Example Data Augmentation},
author={Summers, Cecilia and Dinneen, Michael J},
booktitle={Applications of Computer Vision (WACV), 2019 IEEE Winter Conference on},
organization={IEEE}
year={2019}
}
```