Data Augmentation

Data augmentation is a process in which additional data is generated artificially by applying various transformations and modifications to the original data. This is done to increase the size and diversity of the training data in machine learning models.

What is data augmentation for computer vision?

The primary goal of data augmentation is to solve the overfitting issue. When a model is trained with a small sample size and becomes overly focused on fitting the patterns found in that specific dataset, overfitting occurs. As a result, the model can have trouble generalizing to new data and might struggle to handle practical tasks.

Data augmentation can help to solve this problem by increasing the variety of the training data, which strengthens and adapts the model. As it serves to avoid the model from depending too strongly on particular patterns in the training data, it can be seen as a type of regularization.

There are several ways to enhance data, including producing synthetic data, rotating, resizing, or cropping photos, as well as adding noise. The original data can be subjected to these modifications either randomly or in a preset order, producing a larger dataset with a greater range of variations.

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In the field of computer vision, where images are frequently employed as input data, data augmentation is very helpful. The model can learn to detect things from various angles by applying various transformations to photos, such as rotating or flipping, which makes it more resilient and able to manage real-world changes in data.

In summary, data augmentation is a valuable technique for increasing the size and diversity of training data in machine learning models. It helps to prevent overfitting and improve the generalizability of the model, making it more robust and able to handle real-world tasks.