Archives for image data augmentation
The first-hand data is usually messy and comes from different sources and distributions. To feed them into the machine learning model they need to be standardized and cleaned up.
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AugLy, a new open source Python library that helps AI researchers to make use and create data augmentations to improve the robustness of their created machine learning models. Augmentations can include a wide variety of modifications to a dataset, ranging from cropping a photo to changing the pitch of a voice recording. In addition, AugLy provides sophisticated data augmentation tools to create samples to train and test different systems. AugLy currently supports four modalities, namely audio, image text and video and over 100 other augmentations. Each modality’s augmentations are contained within its sub-library. These sub-libraries can help perform both function-based, and class-based transforms, composition operators and are also available with an option to provide metadata about the transformation applied.
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2D synthetic image data generation.
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In this article, we will demonstrate why data augmentation is known as a regularization technique. How to apply data augmentation to our model and whether it is used as a preprocessing technique or post-processing techniques...? All these questions are answered in this demonstration.
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The article demonstrates how to do data augmentation to increase the size of the data. We will first build a deep learning model without performing augmentation and will compute the accuracy. After which we will build a similar deep learning model after performing augmentation and compute the accuracy. Finally, we will compare the performance of both models.
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