Learn how to Implement Image Augmentation for Your Deep Learning Models. In this article, we’ll see how this can be done using the open-source Albumentation package.
One of the biggest problems in the deep learning field is obtaining enough training data. As we know, deep learning models perform better with more training data. Very little data could lead to poor performance as well as overfitting. This problem is addressed via image augmentation: This is a technique used to generate more training samples from existing data. In this article, we’ll see how this can be done using the open-source Albumentation package.
With image augmentation, various transformations are applied to the original data in order to generate new data. This can be flipping or shearing the image. Other ways of doing this include blurring or cropping the image. This technique is applied to classification, segmentation, pose estimation, and object detection tasks. It helps to prevent overfitting as well as to improve a model’s performance.
The Albumentations package provides a variety of techniques for performing image augmentations. I have seen it being widely used in Kaggle competitions. It is also used in industry, deep learning research, and open-source projects. The tool is loved for its performance and speed. It uses NumPy and Open CV for data processing. At the moment, Albumentations supports 60 image augmentations. The tool also allows developers to easily add new augmentations and use them in their machine learning pipeline.
The problem with deep learning models is they need lots of data to train a model. There are two major problems while training deep learning models is overfitting and underfitting of the model. Those problems are solved by data augmentation is a regularization technique that makes slight modifications to the images and used to generate data.
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Being a practitioner in Machine Learning, you must have gone through an image classification, where the goal is to assign a label or a class to the input image.
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