I hope this is the right place to ask this question.
I have a model in Caffe that I use it as a feature extractor and use those feature vectors(saved in a npy file) as an input to another classifier.
I have two versions of my feature extractor: one is the original Caffe model and the second is the TensorRT engine created from the original Caffe model.
Now when I train new classifier with the features extracted from Caffe model I get 80% on testset then I use TensorRT engine to extract same features to train same classifier with same config and I get 90% accuracy on my testset.
I would appreciate it if someone can explain how and why I get surprisingly better results when I use TensorRT version of same feature extractor model.