Need help with models-comparison.pytorch?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

CeLuigi
148 Stars 22 Forks BSD 3-Clause "New" or "Revised" License 146 Commits 1 Opened issues

Description

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

Services available

!
?

Need anything else?

Contributors list

# 2,668
Lua
Python
pytorch
incepti...
57 commits
# 5,849
Python
pytorch
Lua
benchma...
3 commits
# 34,306
Python
pytorch
Lua
benchma...
3 commits
# 2,968
Python
pandas
benchma...
scikit-...
2 commits
# 40,675
Python
pytorch
Lua
benchma...
2 commits
# 341,239
Python
pytorch
imagene...
convolu...
2 commits
# 58,129
pytorch
Lua
benchma...
resnext
1 commit
# 62,859
pytorch
benchma...
c-sharp
incepti...
1 commit
# 58,231
Rust
Shell
pytorch
Lua
1 commit
# 64,432
Python
pytorch
Lua
benchma...
1 commit
# 64,943
Python
pytorch
Lua
benchma...
1 commit

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”.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.