These image augmentation methods can include a number of edits to the original image, such as changing pixel colors and using various geometric transformations, like a horizontal flip. This way, the model will be able to classify pugs accurately, even with just a few real photos. With various distortions, we can easily turn, say, 5 real photos into 50 synthetic ones. Instead of adding more real pug images from real dogs, letвЂ™s make an artificial one by duplicating one of the existing ones and distorting it slightly so that it still looks like a pug. For example, letвЂ™s say that our original set of 1000 dog breed images has just 5 pug ones. In this case, weвЂ™re making artificial additions to the dataset. This type of data augmentation is particularly useful for improving accuracy in models when the data is difficult, expensive, or time-consuming to collect. This is helpful for complex tasks like neural style transfer, but itвЂ™s useful for any architecture, whether youвЂ™re using GANs (Generative Adversarial Networks), CNNs (Convolutional Neural Networks), or other deep neural network architectures.ДЇor instance, we could add some fake images of pugs to a dataset of dog images if we want to be able to accurately classify pugs, without having to go out and snap a bunch of pictures. You could merge these two datasets to build a larger training data file for more accurate churn modeling (weвЂ™ll explore a similar example in a walkthrough later in this article).Д«esides adding additional real data, you can also add synthetic data, or fake data that simply looks real. You could export data from your HubSpot account, but if youвЂ™re also using Salesforce, you could merge that data to build a larger training data file to fuel a potentially more accurate predictive model.ДЄlternatively, you might be predicting customer churn based on Snowflake data from your APAC sales team, but youвЂ™re also operating in the MENA region. For instance, suppose youвЂ™re building a model to score incoming leads. Real data augmentation can also mean merging data from other sources, or simply other data files. This could make the model more likely to correctly identify which objects are in an image, and helps improve performance overall when it comes time for us to classify new images into one of our predefined categories like вЂњcatвЂќ or вЂњdog.вЂќ its name and description) so that our AI model has more information about what each image represents before it starts training on those images. For example, we could be adding more metadata about each image (e.g. for images that have been labeled), to images of other things that are similar to the original thing, or even videos of the original thing.ДЇor example: Adding a few extra attributes to an image file can make it easier for a machine learning model to recognize the object in question. This can be anything from text files with additional attributes (e.g. Real data augmentation is when you add real, additional data to a dataset. However, if youвЂ™re generating entirely new data or using a new data source, things get a little more tricky, and youвЂ™ll need to ensure consistency in data formatting. If the data is in the same format as your pre-existing data, then itвЂ™s easy, and you can just merge it with your existing data. For example, if youвЂ™re trying to predict the outcome of a soccer game using an algorithm built for predicting stock market trends, you may end up with some pretty strange results.ДЄnother important consideration is how you will collect and cleanse your incoming augmented data before feeding it into your AI model. Additionally, using a small amount of data can increase the risk of overfitting, while having more data points helps to counter that.Д«ut itвЂ™s not just about having enough data - itвЂ™s also about having the right type of data for your particular use case. This is where data augmentation comes in: even if youвЂ™re starting with very little, you can end up with massive amounts of data to generate insights, predictions, and recommendations that were previously unavailable due to a lack of relevant information. The problem is that most companies donвЂ™t have enough data to train their AI models. Further, large neural networks, or deep learning models, need a huge amount of data, so they benefit even more from data augmentation techniques. In fact, research studies have found that basic data augmentation can greatly improve accuracy on image tasks, such as classification and segmentation. Data augmentation is crucial for many AI applications, as accuracy increases with the amount of training data.

How to use keras data augmentation