CeLuigi / models-comparison.pytorch

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

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”.