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models-comparison.pytorch

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

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- _config.yml
- index.md
- - __init__.py
  - bninception.py
  - cafferesnet.py
  - dpn.py
  - fbresnet.py
  - googlenet.py
  - inceptionresnetv2.py
  - inceptionv4.py
  - mobilenet.py
  - mobilenetv2.py
  - nasnet.py
  - nasnet_mobile.py
  - resnext.py
  - senet.py
  - shufflenet.py
  - state_dict_surgery.py
  - torchvision_models.py
  - utils.py
  - vggm.py
  - wideresnet.py
  - xception.py
- __init__.py
- utils.py
- version.py
- compute_accuracy_rate.py
- compute_computational_complexity.py
- compute_inference_time.py
- compute_memory_usage.py
- README.md
.gitignore
_config.yml
LICENSE.txt
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/CeLuigi/models-comparison.pytorch

Downloads the entire project code from GitHub to your computer.

cd models-comparison.pytorch

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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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](https://ieeexplore.ieee.org/document/8506339)).

## Dependencies:
* Python 2.7
* [PyTorch](https://pytorch.org/) 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](https://github.com/CeLuigi/models-comparison.pytorch/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”.