? How to Track Model Training Metadata with Neptune-Keras Integration Data Augmentation in PyTorch and MxNet Transforms in Pytorch ? Keras Metrics: Everything You Need To Know ? Keras Loss Functions: Everything You Need To Know ( 0.2)])Īugmented_image = data_augmentation(image) data_augmentation = tf.keras.Sequential([ Keras preprocessingĪs mentioned above, Keras has a variety of preprocessing layers that may be used for Data Augmentation. If you want to read more on the topic please check the official documentation or other articles. TensorFlow API has plenty of augmentation techniques. Of course, that is just the tip of the iceberg. (train_ds, val_ds, test_ds), metadata = tfds.load( Image = tf.image.random_brightness(image, max_delta= 0.5) Image = tf.image.random_crop(image, size=) In most cases it is useful to apply augmentations on a whole dataset, not a single image. Still, you should keep in mind that you can augment the data for the ML problems as well.įor finer control you can write your own augmentation pipeline. That is why throughout this article we will mostly talk about performing Data Augmentation with various DL frameworks.
In general, DA is frequently used when building a DL model. It means that Data Augmentation is also good for enhancing the model’s performance. However, we can improve the performance of the model by augmenting the data we already have. In general, having a large dataset is crucial for the performance of both ML and Deep Learning ( DL) models. Let’s make this clear, Data Augmentation is not only used to prevent overfitting. It is a good practice to use DA if you want to prevent overfitting, or the initial dataset is too small to train on, or even if you want to squeeze better performance from your model.
Best practices, tips, and tricks What is Data Augmentation?ĭata Augmentation is a technique that can be used to artificially expand the size of a training set by creating modified data from the existing one.