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	ml Morgan Benchmark Analysis of Representative Deep Neural Network Architectures Benchmark Analysis of Representative Deep Neural Network Architectures was a 2018 paper that compared dozens of neural network architectures, looking at model accuracy (on ImageNet 1k), inference speed, FLOPs, memory usage, and parameter count. It follows a 2016 benchmark, but expands it by Including far more architectures Running experiments on two different hardware platforms: one with a top-of-the-line consumer GPU (3840 cores, $1100), and one that is more modest ($400). The paper makes the following claims: Finally, all models use 224x224 images, except for NASNet-A-Large (which uses 331x331) and various Inception nets which use (229x229). The figures below shows accuracy (using center-crop only) versus FLOPs for a single forward pass. The size of the circle shows the number of model parameters.

ml Morgan

Benchmark Analysis of Representative Deep Neural Network Architectures

Benchmark Analysis of Representative Deep Neural Network Architectures was a 2018 paper that compared dozens of neural network architectures, looking at model accuracy (on ImageNet 1k), inference speed, FLOPs, memory usage, and parameter count.

It follows a 2016 benchmark, but expands it by

Including far more architectures
Running experiments on two different hardware platforms: one with a top-of-the-line consumer GPU (3840 cores, $1100), and one that is more modest ($400).

The paper makes the following claims:

Finally, all models use 224x224 images, except for NASNet-A-Large (which uses 331x331) and various Inception nets which use (229x229).

The figures below shows accuracy (using center-crop only) versus FLOPs for a single forward pass. The size of the circle shows the number of model parameters.