How to increase model's test accuracy?

I am using the InceptionV3 model for training. Here is the link for the code (https://github.com/maxmelnick/tensorflow/blob/no_random/tensorflow/examples/image_retraining/retrain.py) Initially I have a small size dataset. So, I used the augmentation technique to increase the size of the dataset.

While training phase dataset was divided into training, validation, and testing. During the training phase, it shows 96% accuracy for 11 classes. But When I predict any new input image(Unseen data) it gave 56% accuracy. What’s the problem lies with the model?

I have already used Dropout, Cross-validation, OverSampling techniques but not achieved good results over the new input image.

Parameters used while training.

Training Samples – 800 images in each class

• Training Samples – 70%
• Validation Samples – 20%
• Testing Samples – 10%

Testing Samples (Unseen data other than Training Samples) – 51 images in each class

Epochs – 10,000

Thank You.