![]() Code quality improvements and fixes ( #215 #223).A new include_top (default: True) option ( #208).Here is a comparison: Update (Aug 25, 2020) The models were searched from the search space enriched with new ops such as Fused-MBConv. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The EfficientNetV2 paper has been released! I am working on implementing it as you read this :)ĮfficientNetV2 is a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. from_pretrained ( 'efficientnet-b0' ) Updates Update (April 2, 2021) Install with pip install efficientnet_pytorch and load a pretrained EfficientNet with: from efficientnet_pytorch import EfficientNet model = EfficientNet.
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