So, you have gathered a dataset, built a neural network, and trained your model.
But despite the hours (and sometimes days) of work you invested to create the model, it spits out predictions with an accuracy of 50–70%. Chances are, this is not what you expected.
Here are a few strategies, or hacks, to boost your model’s performance metrics.
1. Get More Data
Deep learning models are only as powerful as the data you bring in. One of the easiest ways to increase validation accuracy is to add more data. This is especially useful if you don’t have many training instances.
If you’re working on image recognition models, you may consider increasing the diversity of your available dataset by employing data augmentation. These techniques include anything from flipping an image over an axis and adding noise to zooming in on the image. If you are a strong machine learning engineer, you could also try data augmentation with GANs.
Read more about data augmentation here.
Keras has an amazing image preprocessing class to perform data augmentation: ImageDataGenerator.
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