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#Ibm data generator manual
#Ibm data generator Offline

One of the best examples of Synthetic Data Generation is GANs ( Generative Adversarial Networks ) which uses the Generator-Discriminator technique to generate images that are very realistic.

#Ibm data generator Offline

It is a booming technology that is being used by various businesses from online to offline business. They are mainly used in the business field where,Īre the key reasons to use. Applications of Synthetic Data Generation? So, it is evident that using the Synthetic Data Generation technique can significantly increase your model performance and accuracy. There are many resources and research that claims to give better results.

#Ibm data generator manual

Alright! but how does this exactly work? Will the accuracy improve by using this technique? Does it give performance compared to manual data collection?

The advantage of using this technique is to have heterogeneity in the images for better learning of the model. In this article, we will focus on Synthetic Image data Generation. The data that can be used in these techniques can be images, text, audio, video, and so on. The process of generating any kind of data synthetically or artificially via programming is called Synthetic Data Generation. That doesn't look like a lot of topics, right? Let’s see.

Simple Implementation of Synthetic Data Generation.

Applications of Synthetic Data Generation.

Let’s look at the contents that we will cover in this article. Let’s get into what exactly is synthetic image generation. It is to use a synthetic image generation technique to generate more images with the few images that you have. Well, to answer all that question there is a simple solution to it. It is okay for text-based data, what about images? Well, we can obviously automate the process which is again annoying right? So, why can’t we generate more data with just a few data(images) that you have? Can we do it? How can it be done? A simple workaround is to automate the process ( Stay tuned for my web scrapping article). This process can be tedious when we try to collect the data manually. In such cases, all we can do is create our own training dataset from scratch. We usually solve real-world problems and for every such problem, we cannot expect to have pre-collected data for us. But, this process has a catch, based on the problem you are working we can get the data easily by just surfing Kaggle datasets or any other websites for data.

The number of images required to train your model is directly proportional to the model’s performance. Insufficient data can be a major reason for low accuracy and inefficient use of the model itself. The crucial job of a Data Scientist is to collect /create data as much as possible so that we can train the model and get better accuracy. Individual Photos by Ivars Krutainis, Wolfgang Hasselmann and Glen Carrie on Unsplash Introduction