| # 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: | |
| ```python | |
| import timm | |
| model = timm.create_model('tf_efficientnet_b0_ap', pretrained=True) | |
| model.eval() | |
| ``` | |
| To load and preprocess the image: | |
| ```python | |
| 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: | |
| ```python | |
| 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: | |
| ```python | |
| # 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://rwightman.github.io/pytorch-image-models/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). | |
| ```python | |
| 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://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh. | |
| ## Citation | |
| ```BibTeX | |
| @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} | |
| } | |
| ``` | |
| <!-- | |
| Type: model-index | |
| Collections: | |
| - Name: AdvProp | |
| Paper: | |
| Title: Adversarial Examples Improve Image Recognition | |
| URL: https://paperswithcode.com/paper/adversarial-examples-improve-image | |
| Models: | |
| - Name: tf_efficientnet_b0_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 488688572 | |
| Parameters: 5290000 | |
| File Size: 21385973 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b0_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.875' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '224' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1334 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 77.1% | |
| Top 5 Accuracy: 93.26% | |
| - Name: tf_efficientnet_b1_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 883633200 | |
| Parameters: 7790000 | |
| File Size: 31515350 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b1_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.882' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '240' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1344 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 79.28% | |
| Top 5 Accuracy: 94.3% | |
| - Name: tf_efficientnet_b2_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 1234321170 | |
| Parameters: 9110000 | |
| File Size: 36800745 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b2_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.89' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '260' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1354 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 80.3% | |
| Top 5 Accuracy: 95.03% | |
| - Name: tf_efficientnet_b3_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 2275247568 | |
| Parameters: 12230000 | |
| File Size: 49384538 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b3_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.904' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '300' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1364 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 81.82% | |
| Top 5 Accuracy: 95.62% | |
| - Name: tf_efficientnet_b4_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 5749638672 | |
| Parameters: 19340000 | |
| File Size: 77993585 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b4_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.922' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '380' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1374 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 83.26% | |
| Top 5 Accuracy: 96.39% | |
| - Name: tf_efficientnet_b5_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 13176501888 | |
| Parameters: 30390000 | |
| File Size: 122403150 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b5_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.934' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '456' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1384 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 84.25% | |
| Top 5 Accuracy: 96.97% | |
| - Name: tf_efficientnet_b6_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 24180518488 | |
| Parameters: 43040000 | |
| File Size: 173237466 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b6_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.942' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '528' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1394 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 84.79% | |
| Top 5 Accuracy: 97.14% | |
| - Name: tf_efficientnet_b7_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 48205304880 | |
| Parameters: 66349999 | |
| File Size: 266850607 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b7_ap | |
| LR: 0.256 | |
| Epochs: 350 | |
| Crop Pct: '0.949' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '600' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1405 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 85.12% | |
| Top 5 Accuracy: 97.25% | |
| - Name: tf_efficientnet_b8_ap | |
| In Collection: AdvProp | |
| Metadata: | |
| FLOPs: 80962956270 | |
| Parameters: 87410000 | |
| File Size: 351412563 | |
| Architecture: | |
| - 1x1 Convolution | |
| - Average Pooling | |
| - Batch Normalization | |
| - Convolution | |
| - Dense Connections | |
| - Dropout | |
| - Inverted Residual Block | |
| - Squeeze-and-Excitation Block | |
| - Swish | |
| Tasks: | |
| - Image Classification | |
| Training Techniques: | |
| - AdvProp | |
| - AutoAugment | |
| - Label Smoothing | |
| - RMSProp | |
| - Stochastic Depth | |
| - Weight Decay | |
| Training Data: | |
| - ImageNet | |
| ID: tf_efficientnet_b8_ap | |
| LR: 0.128 | |
| Epochs: 350 | |
| Crop Pct: '0.954' | |
| Momentum: 0.9 | |
| Batch Size: 2048 | |
| Image Size: '672' | |
| Weight Decay: 1.0e-05 | |
| Interpolation: bicubic | |
| RMSProp Decay: 0.9 | |
| Label Smoothing: 0.1 | |
| BatchNorm Momentum: 0.99 | |
| Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1416 | |
| Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth | |
| Results: | |
| - Task: Image Classification | |
| Dataset: ImageNet | |
| Metrics: | |
| Top 1 Accuracy: 85.37% | |
| Top 5 Accuracy: 97.3% | |
| --> |