![]() ![]() For example, in order to train a deep network to detect a cup, a huge data set is needed for training purpose with randomized color, texture, pose, size of the cup and different lighting conditions. One of the main objectives of domain randomization is to enhance the training of deep learning applications by exposing the neural network to wide variety of domain parameters in simulation which will help to generalize well to real world applications. ![]() Some of the parameters include the pose, scale of various objects in a scene, the lighting of the simulated environment, the appearance of simulated objects via their color and texture properties and so on. Domain Randomization varies the parameters that define a scene in the simulation environment. Within replicator there are multiple off-the-shelf randomizers to bridge the domain gap between simulation and reality. Replicator YAML Manual and Syntax Guide.Randomizing appearance, placement and orientation of existing 3D assets with a built-in writer.Using Replicator with a fully developed scene.Using existing 3D assets with Replicator.Visualizing output folder with annotated data programmatically.Instantiate multiple assets from a props folder. ![]() Randomizing pose: position, rotation and scale.Adding semantics with Semantics Schema Editor and programmatically.Core functionalities - "Hello World" of Replicator.Theory behind training with synthetic data. ![]()
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