# Unity Perception package (com.unity.perception) The Perception package provides a toolkit for generating large-scale datasets for perception-based machine learning training and validation. It is focused on capturing ground truth for camera-based use cases for now and will ultimately expand to other forms of sensors and machine learning tasks. > The Perception package is in active development. Its features and API are subject to significant change as development progresses. [Installation instructions](SetupSteps.md) [Setting up your first perception scene](GettingStarted.md) [Randomizing your simulation](Randomization/Index.md) ## Example projects using Perception ### SynthDet [SynthDet](https://github.com/Unity-Technologies/SynthDet) is an end-to-end solution for training a 2d object detection model using synthetic data. ### Unity Simulation Smart Camera Example The [Unity Simulation Smart Camera Example](https://github.com/Unity-Technologies/Unity-Simulation-Smart-Camera-Outdoor) illustrates how Perception could be used in a smart city or autonomous vehicle simulation. Datasets can be generated locally or at scale in [Unity Simulation](https://unity.com/products/unity-simulation). ## Package contents |Feature|Description |---|---| |[Labeling](GroundTruth-Labeling.md)|Component which marks a GameObject and its descendants with a set of labels| |[LabelConfig](GroundTruth-Labeling.md#LabelConfig)|Asset which defines a taxonomy of labels for ground truth generation| |[Perception Camera](PerceptionCamera.md)|Captures RGB images and ground truth from a [Camera](https://docs.unity3d.com/Manual/class-Camera.html)| |[DatasetCapture](DatasetCapture.md)|Ensures sensors are triggered at proper rates and accepts data for the JSON dataset| |[Randomization](Randomization/Index.md)|Integrate domain randomization principles into your simulation|