Data Augmentation of a Dog Image in Deep Learning
The quality, amount, and relevance of training data are all significant considerations in machine learning models, particularly deep learning models. One of the most prevalent problems with machine learning is a lack of data.
So, all we have to do is make a few tiny changes to our existing datasets to collect new data. Flips, translations, and rotations are all examples of minor adjustments. In any case, our neural network would recognize these as unique images. Data augmentation is one method of coping with this.
In this tutorial, we are going to learn data augmentation of a dog image using Python programming and deep learning technique.
So let’s get started…
What is Data Augmentation?
Data augmentation is a way of artificially creating fresh training data from existing data. The most well-known sort of data augmentation is picture data augmentation, which involves transforming images in the training dataset into altered versions that belong to the same class as the original image.
In this tutorial, we will learn how to do image data augmentation. In order to increase the model’s performance by augmenting the data we already have.
So, let’s get this tutorial started….
First, make sure you have all of the required Python libraries loaded on your system, and then import all of them as shown below.
The ImageDataGenerator class in the Keras deep learning neural network toolkit allows you to fit models with image data augmentation. A variety of approaches, as well as pixel scaling methods, are supported.
import tensorflow as tf from keras.preprocessing.image import ImageDataGenerator import cv2
We’ll initialize the ImageDataGenerator class after importing the required libraries, and then pass the augmentation parameters in the constructor.
We will augment the image with a series of random changes, ensuring that our model never sees the same image again. This helps the model generalize better by preventing over-fitting.
The first line of the code below can be used to configure random transformations and normalization operations on your image data during training.
from keras.preprocessing.image import ImageDataGenerator,array_to_img, img_to_array, load_img datagen = ImageDataGenerator(rotation_range=10, width_shift_range=0.15, height_shift_range=0.15, shear_range=0.1, brightness_range=(0.3, 1.0), horizontal_flip=True, vertical_flip=True, fill_mode='nearest' )
Following that, we’ll load the data image we want to augment, transform the input image to an array, and then reshape the input image.
i = 0 for batch in datagen.flow(x, batch_size = 1, save_to_dir ='preview', save_prefix ='dog1', save_format ='jpg'): i += 1 if i > 7: break
Hurray, it’s all finished! Let’s have a look at the results.
Data augmentation can be used on any type of data, but it’s particularly beneficial for user information, market the product, and unit sales, where more information can help provide more in-depth analysis. Data augmentation may greatly improve data quality while cutting the level of manual intervention necessary to produce useful information and insight from digital assets.
We’ve successfully learned how to use data augmentation to enhance the model performance. . I hope you all enjoyed this tutorial.