using those simple 7 lines code based on vgg16.py, the accuracy on train and valid data are high as about 0.97. That’s expected.
And I saved the above test weights as a file and loaded it later and called its test() API on test images, but found the accuracy was low. I printed 10 file names and their prediction using bathes.filenames and preds and compared them with actual images by my eye check, the accuracy rate is ONLY 0.6.
Did anyone meet this similar issue? any idea about it?