CeLuigi / Models Comparison.pytorch
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Code for the paper Benchmark Analysis of Representative Deep Neural Network Architectures
Stars: ✭ 148
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Benchmark Analysis of Representative Deep Neural Network Architectures
Code for the paper Benchmark Analysis of Representative Deep Neural Network Architectures (IEEE Access).
Dependencies:
- Python 2.7
- PyTorch 0.4.0
- Torchvision
- munch
Citation
If you use our code, please consider cite the following:
- Simone Bianco, Remi Cadene, Luigi Celona, and Paolo Napoletano. Benchmark Analysis of Representative Deep Neural Network Architectures. In IEEE Access, volume 6, issue 1, pp. 2169-3536, 2018.
@article{bianco2018dnnsbench,
author = {Bianco, Simone and Cadene, Remi and Celona, Luigi and Napoletano, Paolo},
year = {2018},
title = {Benchmark Analysis of Representative Deep Neural Network Architectures},
journal = {IEEE Access},
volume = {6},
pages = {64270-64277},
doi = {10.1109/ACCESS.2018.2877890},
ISSN = {2169-3536},
}
Summary
Visit the Wiki for more details about deep neural network architectures and indices considered.
Acknowledgement
- Thanks to the deep learning community and especially to the contributers of the PyTorch ecosystem.
- Evaluation of Automatic Image Color Theme Extraction Methods This work has been partially supported by E4S: ENERGY FOR SAFETY Sistema integrato per la sicurezza della persona ed il risparmio energetico nelle applicazioni di Home & Building Automation, CUP: E48B17000310009 - Call “Smart Living”.
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