Synthetic data can also be generated for a fraction of the cost, especially when compared to remote sensing imagery that otherwise relies on satellite, aerial, or drone image collection. Modalities include bounding boxes, polygons, depth, and segments. The label accuracy can be done at a very fine granularity, such as on a sub-object or pixel level, and across modalities. This allows synthetic data to be created in enormous quantities and with highly accurate labels for annotations across thousands of images. Synthetic data itself is created by simple rules, statistical models, computer simulations, or other techniques. One way to mitigate this data challenge is by adding synthetic data to the mix.Īdvantages of Combining Real-World Data with Synthetic DataĬombining your real-world data with synthetic data helps to create more complete training datasets for training your ML models. Manually labeling images is slow and open to human error, and building custom labeling tools and setting up scaled labeling operations can be time-consuming and expensive. In some cases, finding all data variations might even be impossible, for example, sourcing images of rare product defects, or expensive, if you have to intentionally damage your products to get those images.Īnd once all data is collected, you need to accurately label the images, which is often a struggle in itself. Yet, collecting the data to train these CV models often takes a long time or can be impossible.Īs a data scientist, you might spend months collecting hundreds of thousands of images from the production environments to make sure you capture all variations in data the model will come across. They help improve manufacturing quality or automate warehouses. CV applications have come to play a key role in the industrial landscape. Let’s take computer vision (CV) applications as an example.

And especially the first step, collecting large, diverse, and accurately labeled datasets for your model training, is often challenging and time-consuming.

Today, I am happy to announce that you can now use Amazon SageMaker Ground Truth to generate labeled synthetic image data.īuilding machine learning (ML) models is an iterative process that, at a high level, starts with data collection and preparation, followed by model training and model deployment.

Synthetic data generator