Using pretrained weights of ResNet50

Hi .

I am using transfer learning for image classification of yoga poses . This is a part of learning journey in ML for computer vision.
I have tried to building CNN models . As well as tried transfer learning using pre-trained models from keras application.
What is puzzling to me is that while using pre-trained weights of ResNet50 , with out preprocessing the images I get an accuracy of 0.99 . How ever , when I subject the images to preprocess_input function , the accuracy drops to 0.8 .
Any one can point out why ?

Generally the default preprocess is in Caffe mode:

Have you tried to finetune a little bit this model on your dataset:

Hi . No haven’t . Since with out preprocessing , accuracy of 0.98 - 0.99 was achieved on the test data . But unable to understand as to why should accuracy reduce after preprocessing . Will try though.

I cannot see if there is something specific with your images statistics as if you see the images are standardized over ImageNet stats:

zero-center each color channel with respect to the ImageNet dataset

Thanks , will check that .