Hi,
Well this is a very general question indeed. You have many ways to improve such a score. Here are a few possibilities:

Try more complex architectures such as the state of the art model for ImageNet (basically GO DEEPER and at some point you can also make use of “smart modules” such as inception module for instance).
Investigate over fitting of your training by measuring as well the accuracy on the training set and, in case you find a huge gap, look for ways to generalize better your training (dropout, regularization penalties etc…)
The accuracy variance between classes is quite large so it can be due to many different facts (some classes might be underrepresented in the data set or just harder to detect etc…) so you could try to improve the accuracy on classes like frog or cat with some tricks (sur-sampling for instance).
Additional data would also certainly help but this is generaly not what people means by improve the accuracy of a model as adding data almost always improve accuracy.
You might have many other possibilities but here are a few that should definitely improve your performances.

Hope this help,