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458 lines
12 KiB
458 lines
12 KiB
# AdvProp (EfficientNet)
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**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.
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The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
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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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@misc{xie2020adversarial,
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title={Adversarial Examples Improve Image Recognition},
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author={Cihang Xie and Mingxing Tan and Boqing Gong and Jiang Wang and Alan Yuille and Quoc V. Le},
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year={2020},
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eprint={1911.09665},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: AdvProp
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Paper:
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Title: Adversarial Examples Improve Image Recognition
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URL: https://paperswithcode.com/paper/adversarial-examples-improve-image
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Models:
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- Name: tf_efficientnet_b0_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 488688572
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Parameters: 5290000
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File Size: 21385973
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b0_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '224'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1334
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 77.1%
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Top 5 Accuracy: 93.26%
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- Name: tf_efficientnet_b1_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 883633200
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Parameters: 7790000
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File Size: 31515350
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b1_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.882'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '240'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1344
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 79.28%
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Top 5 Accuracy: 94.3%
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- Name: tf_efficientnet_b2_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 1234321170
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Parameters: 9110000
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File Size: 36800745
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b2_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.89'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '260'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1354
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 80.3%
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Top 5 Accuracy: 95.03%
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- Name: tf_efficientnet_b3_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 2275247568
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Parameters: 12230000
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File Size: 49384538
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b3_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.904'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '300'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1364
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 81.82%
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Top 5 Accuracy: 95.62%
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- Name: tf_efficientnet_b4_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 5749638672
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Parameters: 19340000
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File Size: 77993585
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b4_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.922'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '380'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1374
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 83.26%
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Top 5 Accuracy: 96.39%
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- Name: tf_efficientnet_b5_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 13176501888
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Parameters: 30390000
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File Size: 122403150
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b5_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.934'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '456'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1384
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 84.25%
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Top 5 Accuracy: 96.97%
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- Name: tf_efficientnet_b6_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 24180518488
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Parameters: 43040000
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File Size: 173237466
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b6_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.942'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '528'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1394
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 84.79%
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Top 5 Accuracy: 97.14%
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- Name: tf_efficientnet_b7_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 48205304880
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Parameters: 66349999
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File Size: 266850607
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b7_ap
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LR: 0.256
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Epochs: 350
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Crop Pct: '0.949'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '600'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1405
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 85.12%
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Top 5 Accuracy: 97.25%
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- Name: tf_efficientnet_b8_ap
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In Collection: AdvProp
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Metadata:
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FLOPs: 80962956270
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Parameters: 87410000
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File Size: 351412563
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Architecture:
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- 1x1 Convolution
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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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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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Tasks:
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- Image Classification
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Training Techniques:
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- AdvProp
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Data:
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- ImageNet
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ID: tf_efficientnet_b8_ap
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LR: 0.128
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Epochs: 350
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Crop Pct: '0.954'
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Momentum: 0.9
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Batch Size: 2048
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Image Size: '672'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1416
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 85.37%
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Top 5 Accuracy: 97.3%
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-->
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