EfficientNet-V2 Annotated Paper

EfficientNetV2: Smaller Models and Faster Training

This very recent paper (1-month-old at the time of writing this) introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency. Based on top of the EfficientNet this paper pushes the boundary of model scaling and architecture search by further optimizing the network by using training aware Neural Architecture Search (NAS) and scaling. It jointly optimizes training speed and parameter efficiency to create the lightest best-performing models.

Please feel free to read along with the paper with my notes and highlights.

Color	Meaning
Green	Topics about the current paper
Yellow	Topics about other relevant references
Blue	Implementation details/ maths
Red	Text including my thoughts, questions, and understandings

@misc{tan2021efficientnetv2,
      title={EfficientNetV2: Smaller Models and Faster Training}, 
      author={Mingxing Tan and Quoc V. Le},
      year={2021},
      eprint={2104.00298},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}