Description
Hello,
I'm quite new to tensorflow and I was trying to train an object detector using their api. I was wondering if there is a way to pass customized augmentations in the .config file or include them in some other way in the preprocessing step.
For example, I noticed their api does not implement image blur and noise. What I would like to do in this case is to pass something like the following in the .config file:
data_augmentation_options{
my_own_augmentation{
my_augmentation_parameters
}
}
Where my_own_augmentation is a function implementing, in this case, image blur and noise. As an alternative, it would be also fine to include it dynamically in my python code.
I have been looking online for a while, but the few solutions I have found consist of changing tensorflow's source code, which to me seems a really bad coding practice.
I hope the question is clear. Thank you in advance for the help!