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pytorch-image-models/docs/models/.templates/models/ensemble-adversarial.md

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# # Ensemble Adversarial Inception ResNet v2
**Inception-ResNet-v2** is a convolutional neural architecture that builds on the Inception family of architectures but incorporates [residual connections](https://paperswithcode.com/method/residual-connection) (replacing the filter concatenation stage of the Inception architecture).
This particular model was trained for study of adversarial examples (adversarial training).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
Tianyu Pang and
Jun Zhu and
Xiaolin Hu and
Cihang Xie and
Jianyu Wang and
Zhishuai Zhang and
Zhou Ren and
Alan L. Yuille and
Sangxia Huang and
Yao Zhao and
Yuzhe Zhao and
Zhonglin Han and
Junjiajia Long and
Yerkebulan Berdibekov and
Takuya Akiba and
Seiya Tokui and
Motoki Abe},
title = {Adversarial Attacks and Defences Competition},
journal = {CoRR},
volume = {abs/1804.00097},
year = {2018},
url = {http://arxiv.org/abs/1804.00097},
archivePrefix = {arXiv},
eprint = {1804.00097},
timestamp = {Thu, 31 Oct 2019 16:31:22 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: Ensemble Adversarial
Paper:
Title: Adversarial Attacks and Defences Competition
URL: https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition
Models:
- Name: ens_adv_inception_resnet_v2
In Collection: Ensemble Adversarial
Metadata:
FLOPs: 16959133120
Parameters: 55850000
File Size: 223774238
Architecture:
- 1x1 Convolution
- Auxiliary Classifier
- Average Pooling
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inception-v3 Module
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: ens_adv_inception_resnet_v2
Crop Pct: '0.897'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_resnet_v2.py#L351
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 1.0%
Top 5 Accuracy: 17.32%
-->