EEEA-Net
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EEEA-Net: An Early Exit Evolutionary Neural Architecture Search
EEEA-Net: An Early Exit Evolutionary Neural Architecture Search
This paper has been published to Engineering Applications of Artificial Intelligence.
Paper: EAAI version or arXiv version
This implementation of EEEA-Net (Early Exit Evolutionary Algorithm Network) from EEEA-Net: An Early Exit Evolutionary Neural Architecture Search by Chakkrit Termritthikun, et al.
This code is based on the implementation of DARTS, NSGA-Net, NSGA-Net-v2, Once for All, and TransferLearning-Tasks.
Results
Prerequisite for server
- Tested on Ubuntu OS version 18.04.x
- Tested on PyTorch 1.6 and TorchVision 0.7.0
Quick Usage (EEEA-Net, ImageNet pre-trained)
install darmo package
pip install darmo
import darmo and create model; see more models at darmo
import darmo
model = darmo.create_model("eeea_c2", num_classes=1000, pretrained=True)
supported transfer learning
model.reset_classifier(num_classes=100, dropout=0.2)
Usage
Cloning source code
git clone https://github.com/chakkritte/EEEA-Net/
cd EEEA-Net/EEEA/cifar
Install Requirements
pip install -r requirements.txt
Architecture search on CIFAR-10 (Normal search)
python search_space.py --dataset cifar10 --search normal --th_param 0.0
Architecture search on CIFAR-10 (Early Exit search with beta equal 5)
python search_space.py --dataset cifar10 --search ee --th_param 5.0
Architecture evaluation on CIFAR-10
python train_cifar.py --arch [name]
*[name] is mean a name of models [EA, EEEA_A, EEEA_B, EEEA_C]
Citation
If you use EEEA-Net or any part of this research, please cite our paper:
@article{termritthikun2021eeea,
title="{EEEA-Net: An Early Exit Evolutionary Neural Architecture Search}",
author={Termritthikun, Chakkrit and Jamtsho, Yeshi and Ieamsaard, Jirarat and Muneesawang, Paisarn and Lee, Ivan},
journal={Engineering Applications of Artificial Intelligence},
volume={104},
pages={104397},
year={2021},
publisher={Elsevier}
}
License
Apache-2.0 License