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90 lines
3.0 KiB
90 lines
3.0 KiB
4 years ago
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# Summary
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**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
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This particular model was trained for study of adversarial examples (adversarial training).
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@article{DBLP:journals/corr/abs-1804-00097,
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author = {Alexey Kurakin and
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Ian J. Goodfellow and
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Samy Bengio and
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Yinpeng Dong and
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Fangzhou Liao and
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Ming Liang and
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Tianyu Pang and
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Jun Zhu and
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Xiaolin Hu and
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Cihang Xie and
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Jianyu Wang and
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Zhishuai Zhang and
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Zhou Ren and
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Alan L. Yuille and
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Sangxia Huang and
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Yao Zhao and
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Yuzhe Zhao and
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Zhonglin Han and
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Junjiajia Long and
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Yerkebulan Berdibekov and
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Takuya Akiba and
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Seiya Tokui and
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Motoki Abe},
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title = {Adversarial Attacks and Defences Competition},
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journal = {CoRR},
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volume = {abs/1804.00097},
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year = {2018},
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url = {http://arxiv.org/abs/1804.00097},
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archivePrefix = {arXiv},
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eprint = {1804.00097},
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timestamp = {Thu, 31 Oct 2019 16:31:22 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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<!--
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Models:
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- Name: adv_inception_v3
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Metadata:
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FLOPs: 7352418880
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Training Data:
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- ImageNet
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Architecture:
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- 1x1 Convolution
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- Auxiliary Classifier
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- Average Pooling
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inception-v3 Module
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- Max Pooling
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- ReLU
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- Softmax
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File Size: 95549439
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Tasks:
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- Image Classification
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ID: adv_inception_v3
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Crop Pct: '0.875'
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Image Size: '299'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L456
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In Collection: Adversarial Inception v3
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Collections:
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- Name: Adversarial Inception v3
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Paper:
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title: Adversarial Attacks and Defences Competition
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url: https://papperswithcode.com//paper/adversarial-attacks-and-defences-competition
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type: model-index
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Type: model-index
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-->
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