very bad result in the real-plate images

[ghost]

Hi, I've trained the VGG-GRU network for around 700k artificially generated images, and i got 96% accuracy for test data (about 140k generated image), but when i test the trained the model on real-images , the results are very bad, why ?

[qjadud1994]

Due to overfitting, it is no wonder that it does not work properly in real images.

so, I recommend pre-training with synthetic images and fine-tune with real data.

[danishansari]

@qjadud1994 How would you recommend fine-tuning, should we use differential learning rates, i.e. lower lr for CNN and higher for RNN? Or a global lower learning rate like 1e-3 or smaller? Or because it's using Adadelta, this is not required at all, and we can use default lr(1.0)? @mzeynali did you get it working?

[PythonImageDeveloper]

@qjadud1994, Because I used Early Stopping callback, in my opinion, Should not happen the overfitting problem, right?