Data augmentation is a technique used in machine learning and artificial intelligence to increase the amount of data available to train a model. This technique involves generating new data from existing data by applying transformations that maintain the original labels.
Data augmentation is commonly used in computer vision and image processing applications, where transformations such as rotation, cropping, colour inversion and resizing are applied to create variations of the original images. In this way, the model can learn to recognise relevant image features regardless of their orientation, size and other factors.
Data augmentation is an effective technique to avoid over-fitting in machine learning models, where the model fits too closely to the training data and does not generalise well to new data. By increasing the amount and variety of training data, the model's ability to generalise to new data and improve its accuracy and performance can be improved.
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