Summary:

Convolution-BatchNorm (ConvBN) blocks:

– Integral components in computer vision tasks and other domains.
– Three modes: Train, Eval, and Deploy.

Trade-off between stability and efficiency:

– Deploy mode is efficient but suffers from training instability.
– Eval mode widely used in transfer learning but lacks efficiency.

Author’s Take:

This article highlights the importance of Convolution-BatchNorm (ConvBN) blocks in various computer vision tasks and discusses the trade-off between stability and efficiency in these blocks. The Deploy mode is efficient but faces training instability, whereas the widely used Eval mode lacks efficiency. This trade-off is an important consideration when using ConvBN blocks in different applications.

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