EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, being an order-of-magnitude smaller and faster.
EfficientNet v1 is developed based on AutoML and Compound Scaling. In particular, a mobile-size baseline network called EfficientNet v1-B0 is developed from AutoML MNAS Mobile framework, the building block is mobile inverted bottleneck MBConv with squeeze-and-excitation optimization. Then, through a compound scaling method, this baseline is scaled up to obtain EfficientNet v1-B1 to B7.
This model was trained using script available on NGC and in GitHub repo.
The following datasets were used to train this model:
Performance numbers for this model are available in NGC.