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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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Code for the paper Benchmark Analysis of Representative Deep Neural Network Architectures

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