![]() ![]() brightness_range alters the brightness of the image.horizontal_flip randomly flips images, horizontally.height_shift shifts the image by the fraction of the total image height, if float provided.width_shift shifts the image by the fraction of the total image width, if float provided.rotation_range rotates the image randomly with maximum rotation angle, 40.rescale multiplies each pixel value with the rescale factor.Transformations to be applied datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, zoom_range=0.2, horizontal_flip=True, brightness_range=, fill_mode='nearest') Iterating the generator n_steps_data_aug times generates the required number of images. In this tutorial, we generate a new image corresponding to every sampled image. n_steps_data_aug is analogous to epochs in model training.If you are using transfer learning specific models have their own specifications for the input size of the images to the model. img_size is the image size required by the model.This is analogous to batches in model training. We define a batch size for the image generator.DATA_AUG_BATCH_SIZE = 2 # batch size for data augmentation img_size = (224, 224) # input image size to model # Number of steps to perform data augmentation n_steps_data_aug = np.ceil(df_sample.shape/DATA_AUG_BATCH_SIZE).astype(int) Next, we will define the parameters of the image generator. You have collected training data, and your images are very similar to the ones shown below. Say you want to build a neural network to identify Toyota Corollas. Nevertheless, they are susceptible to overfitting. When to perform data augmentation?ĭeep Learning has helped us achieve state-of-the-art performance on computer vision tasks. Examples of image transformations are image rotation, altering image brightness, flipping an image horizontally or vertically, etc. Data augmentation using ImageDataGenerator What is data augmentation?ĭata augmentation is a technique that leverages transformations or oversampling to artificially inflate the original dataset. Various techniques are available to reduce over-fitting in this article, we will focus on Data Augmentation.īreakdown: 1. Overfitting models to training data is a familiar problem data scientists and machine learning researchers face regularly. ![]()
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