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I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster.

However, in the article Container: Context Aggregation Network, they show that:

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The container has higher FLOPS and less throughput, while the container-light has lower FLOPS and higher throughput.

What is the reason for that?

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In the context of Deeplearning:

  • FLOPS: Floating Point Ops per Second
  • FLOPs: Floating Point Ops

FLOPS, refers to the number of floating point operations that can be performed by a computing entity in one second. It is used to quantify the performance of a hardware.

FLOPs, simply means the total number of floating point operations required for a single forward pass. The higher the FLOPs, the slower the model and hence low throughput.

This thread on stack overflow might help to get a deeper insight: https://stackoverflow.com/questions/58498651/what-is-flops-in-field-of-deep-learning

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