Interesting result - VGG 2x faster on @pytorch vs keras + @tensorflow https://medium.com/@vishnuvig/transfer-learning-using-pytorch-part-2-9c5b18e15551 …
Seems to mainly be due to preprocessing time
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You can get even better performance by removing the overhead of feed_dict. Simply use a TF queue and build your Keras model on top of that.
The above performance comparison does not make sense, unfortunately (makes input preprocessing *blocking* instead of parallel to the model).
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It's effectively not a runtime comparison but a preprocessing pipeline comparison --inefficiently configured one vs efficiently configured.
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