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timm/models/efficientnet.py

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""" Generic EfficientNets
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""" PyTorch EfficientNet Family
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An implementation of EfficienNet that covers variety of related models with efficient architectures:
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* EfficientNet (B0-B8 + Tensorflow pretrained AutoAug/RandAug/AdvProp weight ports)
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- EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946
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- CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971
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- Adversarial Examples Improve Image Recognition - https://arxiv.org/abs/1911.09665
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A generic class with building blocks to support a variety of models with efficient architectures:
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* EfficientNet (B0-B7)
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* EfficientNet-EdgeTPU
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* EfficientNet-CondConv
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* MixNet (Small, Medium, and Large)
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* MnasNet B1, A1 (SE), Small
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* MobileNet V1, V2, and V3
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- MixConv: Mixed Depthwise Convolutional Kernels - https://arxiv.org/abs/1907.09595
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* MNasNet B1, A1 (SE), Small
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- MnasNet: Platform-Aware Neural Architecture Search for Mobile - https://arxiv.org/abs/1807.11626
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* FBNet-C
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- FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable NAS - https://arxiv.org/abs/1812.03443
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* Single-Path NAS Pixel1
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* And likely more...
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- Single-Path NAS: Designing Hardware-Efficient ConvNets - https://arxiv.org/abs/1904.02877
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TODO not all combinations and variations have been tested. Currently working on training hyper-params...
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* And likely more...
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Hacked together by Ross Wightman
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"""
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url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
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}
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_DEBUG = False
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