Adding noiseįor blurry images, adding noise on the image can be useful. It is easy to find many coding examples for these augmentation transformations from open source libraries and in articles on the topic. There are geometric and color space augmentation methods for images to create image diversity in the model. What are data augmentation techniques in computer vision? penalty for a bad prediction) & accuracy and validation loss & accuracy than a deep learning model without augmentation for image classification task. According to an experiment, a deep learning model after image augmentation performs better in training loss (i.e. Computer vision and natural language processing (NLP) models use data augmentation strategy to handle with data scarcity and insufficient data diversity.ĭata augmentation algorithms can increase accuracy of machine learning models. Data augmentation techniques generate different versions of a real dataset artificially to increase its size.
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