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Adversarial Inception v3

Inception v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an 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.

This particular model was trained for study of adversarial examples (adversarial training).

The weights from this model were ported from Tensorflow/Models.

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How do I use this model on an image?

To load a pretrained model:

    >>> import timm
    >>> model = timm.create_model('adv_inception_v3', pretrained=True)
    >>> model.eval()

To load and preprocess the image:

    >>> import urllib
    >>> from PIL import Image
    >>> from timm.data import resolve_data_config
    >>> from timm.data.transforms_factory import create_transform

    >>> config = resolve_data_config({}, model=model)
    >>> transform = create_transform(**config)

    >>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
    >>> urllib.request.urlretrieve(url, filename)
    >>> img = Image.open(filename).convert('RGB')
    >>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension

To get the model predictions:

    >>> import torch
    >>> with torch.inference_mode():
    ...     out = model(tensor)
    >>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
    >>> print(probabilities.shape)
    >>> # prints: torch.Size([1000])

To get the top-5 predictions class names:

    >>> # Get imagenet class mappings
    >>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
    >>> urllib.request.urlretrieve(url, filename)
    >>> with open("imagenet_classes.txt", "r") as f:
    ...     categories = [s.strip() for s in f.readlines()]

    >>> # Print top categories per image
    >>> top5_prob, top5_catid = torch.topk(probabilities, 5)
    >>> for i in range(top5_prob.size(0)):
    ...     print(categories[top5_catid[i]], top5_prob[i].item())
    >>> # prints class names and probabilities like:
    >>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]

Replace the model name with the variant you want to use, e.g. `adv_inception_v3`. You can find the IDs in the model summaries at the top of this page.

To extract image features with this model, follow the timm feature extraction examples, just change the name of the model you want to use.

##
How do I finetune this model?

You can finetune any of the pre-trained models just by changing the classifier (the last layer).

    >>> model = timm.create_model('adv_inception_v3', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)

To finetune on your own dataset, you have to write a training loop or adapt timm’s training script to use your dataset.

##
How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

##
Citation

    @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}
    }

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