Keras has a powerful API called ImageDataGenerator that resolve this problem. Keras's ImageDataGenerator and its limit ¶ĭata augmentation could increase the number of training images substantially which could raise a storage problem. This experiment shows that it is essential to increase the data size using data augmentation to develop a robust deep learning model. However, the model performance improves when training data also contains translated images. In my previous blog post, I have seen poor performance of a deep learning model when testing images contain the translation of the training images. Why data augmentation? ¶ĭeep learning model is data greedy and the performance of the model may be surprisingly bad when testing images vary from training images a lot.ĭata augmentation is an essential technique to utilize limited amount of training images. model.fit_generator(data_generator.The python class ImageDataGenerator_landmarks is available at my github account. data_generator.flow_from_directory('path_to_dir')įit_generator function used to train a neural network.
Images in the directory are stored in subdirectories with respect to their class. data_generator.flow(X,Y)įlow_from_directory function generates batches of augmented data located at specific directory of a disk. from import ImageDataGeneratorįlow function loads the image dataset in memory and generates batches of augmented data.
Here, is the example of how to use ImageDataGenerator class.
An ImageDataGenerator class function provide a range of transformations. Image Augmentation using Keras ImageDataGeneratorĪ Keras deep learning library provides the data augmentation function, which applies augmentation automatically while training the model. The numpy package provides a flip operation. Here, I have illustrated various data augmentation technique with example in python.įlip augmentation derived the horizontal flip and vertical flip. It is also important to select the specific data augmentation method carefully in the context of the problem domain.
However, we can generate more data with some modifications. But, the small amount of train data is not sufficient to get high performance. In Real-life, we may have limited train data in various condition. Neural networks would not distinguish the augmented image. These transformations include many operations from the field of image manipulations, like flips, rotate at a various angle, shifts, zooms and many more. Training the neural network on more data leads to achieving higher accuracy. Data Augmentation is a technique of creating new data from existing data by applying some transformations.
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