The quantity and variability of data are important for the performance of ML models. So the neural networks require very large datasets for training. Only then will it achieve the production-ready model’s accuracy. But what if you have insufficient data? The solution is data augmentation. In this article, you will learn what data augmentation is and how to augment image data.
What is data augmentation?
Image data augmentation is a set of techniques that converts given image datasets into new transformed versions. The goal is not to collect new data but rather to transform data that is already available.
The general idea is to transform the image so that it is relevantly different from the original while still belonging to the same class. Importantly, augmentation helps prevent the network from learning the wrong patterns.
Why use image data augmentation in computer vision?
LARGER DATA SET
By augmenting your images, you create a larger dataset. Having a larger dataset will allow the model to better generalize to actual production situations. However, you should remember to match the appropriate technique to the situation. For example, in a bike data set, it won’t be useful to rotate the image vertically. After all, the path is never reversed.
PROTECTS AGAINST OVERFITTING
Overfitting occurs when the model fits too well with the training data and, as a result, unintended test data cannot be accurately predicted. In other words, the model is not flexible enough to predict real data. To determine if a model represents an overfitting event, consider trained models and their corresponding training and testing accuracies. If the model presents a very high training accuracy and a very low accuracy of the test, then we are talking about overfitting. In addition, over-matching is usually caused by a small database. This is because the model has a limited number of patterns to learn.
Let us consider an example. You want to build a leggings classifier, but you only have images of navy blue ones. Understandably, the model will not fail to identify navy blue leggings. Unfortunately, it will show poor performance if there are leggings of a different color in the test image. However, this problem can be solved in many ways. Researchers have developed several augmentation techniques to extend a dataset and improve the performance of computer vision models.
What are the image augmentation techniques?
There are many image augmentation techniques, and they are very effective for various computer vision tasks. We divided them into three categories.
POSITION MANIPULATION
There are many ways to change the position of an image. Here are some methods for manipulating position:
Scaling
It relates to decreasing and increasing an image. As a rule, the processed image should be of the same size what’s the original:
- Increasing the size cuts the image off.
- Decreasing forces you to take additional action on the resulting frame.
Rotating
It allows you to rotate the image to generate new angles.
Flipping
With this method, you can flip the image right, left, or upside down.
THE PROBLEM OF BLANK SURFACES
As a result of most of the discussed transformations, empty ones have created places. The easiest way to deal with this problem is to crop and then scale the image. More advanced – interpolation.
Interpolation can be:
- Fixed – replaces blank space with a solid color (especially useful for monochrome images).
- Boundry – replaces blank space with border values.
- Reflect – replaces blank space with the values of the pixels reflected along the edges.
- Wrap – replaces blank space with the part of the image on the opposite side.
COLOR MANIPULATION
In machine learning, colors are crucial to determining a model’s performance. You can change them to achieve different effects. Some methods for color manipulation:
- Brightness
- Contrast
- Hue
Those features help you to create a variety of lighting conditions. By altering the RGB color channel histogram or changing the color distribution, the model is made more resistant to lighting biases.
IMAGE MANIPULATION
The last category is image manipulation. Here are some methods:
Blur and sharpening
Depending on what degree of blur or sharpening you want to achieve, you will use different kernel configurations. It allows you to generate images of reduced quality and with different levels of sharpness.
Mixed images
There are two ways of mixing images, i.e., pixel averaging (mixup) and overlying crops (CutMix).
Cropping
Randomly cropped pieces of an image allow the model to learn from imperfect data.
Shearing
This method allows you to transform the orientation of the image.
Conclusion on image data augmentation
Image data augmentation improves the generalizability of the model. And combining different image data augmentation methods allows for the creation of any number of new instances. We covered several image augmentation techniques in this article. Applying an appropriate one to the image in the training data can increase its size. Find out more about computer vision services.
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