> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/timm/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/timm/model-pages/advprop-efficientnet.md).

# AdvProp (EfficientNet)

## AdvProp (EfficientNet)

**AdvProp** is an adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to the method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples.

The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).

### How do I use this model on an image?

To load a pretrained model:

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```
>>> import timm
>>> model = timm.create_model('tf_efficientnet_b0_ap', pretrained=True)
>>> model.eval()
```

To load and preprocess the image:

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```
>>> 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:

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```
>>> import torch
>>> with torch.no_grad():
...     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:

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```
>>> # 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. `tf_efficientnet_b0_ap`. 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](https://huggingface.co/docs/timm/feature_extraction), 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).

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```
>>> model = timm.create_model('tf_efficientnet_b0_ap', 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](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.

### How do I train this model?

You can follow the [timm recipe scripts](https://huggingface.co/docs/timm/scripts) for training a new model afresh.

### Citation

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```
@misc{xie2020adversarial,
      title={Adversarial Examples Improve Image Recognition}, 
      author={Cihang Xie and Mingxing Tan and Boqing Gong and Jiang Wang and Alan Yuille and Quoc V. Le},
      year={2020},
      eprint={1911.09665},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```


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