However, the main challenge is creating a balanced dataset that closes the reality gap and generalizes well when deployed in the real world. ![]() Synthetic data can solve this by providing unlimited desired training data with automatic generation. ![]() Training an object detection system can be challenging and time-consuming as machine learning requires substantial volumes of training data with associated metadata. ![]() This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies.
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