google / automl
EfficientNetV2 are a family of image classification models, which achieve better parameter efficiency and faster training speed than prior arts. Built upon EfficientNetV1, our EfficientNetV2 models use neural architecture search (NAS) to jointly optimize model size and training speed, and are scaled up in a way for faster training and inference speed.
For comparison with EfficientNetV1, we have also provided a few smaller V2 models using the same scaling and preprocessing as V1:
Currently, supported model_name includes: efficientnetv2-s, efficientnetv2-m, efficientnetv2-l, efficientnetv2-b0, efficientnetv2-b1, efficientnetv2-b2, efficientnetv2-b3. We also support all EfficientNetV1 models including: efficientnet-b0/b1/b2/b3/b4/b5/b6/b7/b8/l2
