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This article introduces you how to implement data enhancement operations in TensorFlow. The content is very detailed. Interested friends can refer to it for reference. I hope it can help you.

Data augmentation is an increase in the size and diversity of an existing training dataset without manually collecting any new data. This enhanced data can be obtained by performing a series of preprocessing transformations on existing data, which for image data can include horizontal and vertical flip, tilt, crop, rotation, etc. In summary, this augmented data can simulate a variety of slightly different data points, as opposed to merely copying the same data. The nuances of these "additional" images should be enough to help train a more robust model. Again, this is the idea.

The focus of this article is on the practical implementation of the second approach in TensorFlow to alleviate the problem of small amounts of image training data (data enhancement), while similar practical processing will be done later on for transfer learning.

How Image Enhancement Helps

When a convolutional neural network learns image features, we want to ensure that these features appear in various orientations so that the trained model can recognize that a person's legs can appear in both vertical and horizontal directions of the image. In addition to increasing the original number of data points, enhancement can also help us in this case by employing transformations such as image rotation. As another example, we can also use horizontal flips to help model training identify whether a cat is standing upright or photographed upside down.

Data augmentation is not a panacea; we don't want it to solve all our small data problems, but it can work in many cases and can be done by using it as part of a comprehensive model training approach, or with another dataset extension technique (e.g., transfer learning).

Image Enhancement in TensorFlow

In TensorFlow, data augmentation is done using the ImageDataGenerator class. It is very easy to understand and use. The entire dataset cycles through each epoch and the images in the dataset are transformed according to the options and values selected. These conversions are performed in memory, so no additional storage is required (although save_to_dir can be used to save enhanced images to disk if desired).

If you are using TensorFlow, you may have used ImageDataGenerator's simple method to scale an existing image without any other augmentation. It might look something like this:

ImageDataGenerator performs enhanced updates that might look like this:

What does this mean?

rotation_range-The range of degrees of random rotation; 20 degrees in the example above

width_shift_range-A fraction of the total width if the value

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