First paste my experimental results:
Resnet50 (Pytorch official provided, trained by imagenet)
The calibration dataset contains 1000 images from imagenet, it works fine.
SeResNext50 (My custom dataset, 4500+ images used in training)
The calibration contains 400 images from train dataset, the accuray is too low.
But when I use all 4500+ train dataset to do calibration. the result is below:
the accuracy from 25.3% increase to 79.4%
1、how many calibration images should use to do calibration?
2、how to choose the most suitable calibration set?
3、why seresnext50 int8 doesn’t have much speedup?
4、Even though I used all the training data for calibration, the accuracy still decreased a lot, how can I avoid it ?
5、Why resnet50 only uses 1000 pictures for calibration can get huge performance improvement and accuracy does not decrease？