Data Augmentation for Computer Vision

When given enough training data, machine learning algorithms can do amazing feats. Unfortunately, many applications still struggle to access high-quality data.

Making copies of current data and making small modifications to them is one method for increasing the diversity of the training dataset. This is referred to as “data augmentation.” Data augmentation is a low-cost and effective approach to improving the performance and accuracy of machine learning models in data-constrained scenarios.

For example: Let’s suppose your image classification dataset has ten images of cats. You’ve increased the number of cats for the “cat” class by making duplicates of your cat images and turning them horizontally. Rotation, cropping, and translation are some of the additional changes available. You can also combine the changes to increase the number of unique training instances in your collection.

The process of changing, or “augmenting” a dataset with extra information is known as data augmentation. This additional input might range from images to text, and its integration into machine learning algorithms increases their productivity. In order to increase the amount of a real dataset, data augmentation techniques artificially create many versions of the dataset. Computer vision and natural language processing NLP models use data augmentation tactics to address data scarcity and a lack of data diversity.Data augmentation is not restricted to images and may be used on other forms of data as well. In text datasets, synonyms can be used to change nouns and verbs. Training examples in audio data can be adjusted by adding noise or adjusting the playback speed.

Data Augmentation techniques in Computer Vision:

Some of the methods for data augmentation that are frequently used are:

Noise Addition

To the existing images, add gaussian noise.

Cropping

A portion of the image is selected, cropped, and resized to its original size.

Flipping

The image is flipped horizontally and vertically. Flipping rearranges the pixels while protecting the features of the image.

Rotation

The image is rotated by a degree ranging from 0° to 360°. In the model, each rotated image will be unique.

Scaling

The image is scaled outward and inward. When scaled outward, the image size increases, whereas when scaled inward, the image size decreases.

Translation

Along the x-axis or y-axis, the image is shifted into various locations.

Brightness

The image’s brightness is changed, and the new image will be darker or lighter. This technique enables the model to identify images in a variety of lighting conditions.

Contrast

The contrast of the image is changed and the new image will be different from luminance and color aspects. The following image’s contrast is changed randomly.

Color Augmentation

The color of the image is changed by new pixel values. There is an example image that is grayscale.

Saturation

The depth or intensity of color in an image is referred to as saturation. The data augmentation process has saturated the image below.

Conclusion

TagX tried to provide an overview of several Data Augmentation approaches and demonstrated how data augmentation techniques are frequently used in combination, for example, cropping after resizing. So it is important to note that Data Augmentation is used to boost training data size and Machine learning model performance.

TagX is the industry leader in providing high-quality training datasets for machine learning and deep learning. Working with renowned clients, it is offering data annotation and data collection for computer vision and NLP-based AI model developments.