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Synthetic Training Data Used for Retail Merchandising Audit System
In this example created by Deep Vision Data, a deep learning model based on the ResNet101 architecture was trained to classify product SKU’s, stock outs and mis-merchandised products for a retail store merchandising audit system. The model was trained with 20,000 synthetic product images using a 50-50 split of structured and unstructured domain randomized subsets and an 80-20 training/validation data split. Model validation was also completely done with 100% synthetic training data. The test set was comprised of actual photos; a sampling of results images are shown to the right.
Domain randomization (DR) is a powerful tool available with synthetic data; it enables the creation of data variability that encompasses both expected and unexpected real-world input, forcing the model to focus on the data features most important to the problem understanding. DR is much more costly and difficult to implement with physical data. For example, the creation of a dataset of thousands of products where each product is shown in thousands of poses on dozens of backgrounds requires many millions of labeled images. That dataset is easily created synthetically, while virtually impossible to create using physical product photos. In addition, the distribution of rare (but possibly very important) events or conditions is easily controlled, unlike physical data where rare occurrences are by definition poorly represented.
Synthetic training data can be utilized for almost any machine learning application, either to augment a physical dataset or completely replace it. By effectively utilizing domain randomization the model interprets synthetic data as just part of the DR and it becomes indistinguishable from the physical information. Synthetic training data is inherently less costly, faster to create, perfectly annotated, and isn’t constrained by availability, time or even the physics of the natural world.
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