[](LICENSE.md) > com.unity.perception is in active development. Its features and API are subject to significant change as development progresses. # Perception Package (Unity Computer Vision) The Perception package provides a toolkit for generating large-scale datasets for perception-based machine learning training and validation. It is focused on a handful of camera-based use cases for now and will ultimately expand to other forms of sensors and machine learning tasks. ## Getting Started **[Quick Installation Instructions](com.unity.perception/Documentation~/SetupSteps.md)** Get your local Perception workspace up and running quickly. Recommended for users with prior Unity experience. **[Perception Tutorial](com.unity.perception/Documentation~/Tutorial/TUTORIAL.md)** Detailed instructions covering all the important steps from installing Unity Editor, to creating your first Perception project, building a randomized Scene, and generating large-scale synthetic datasets by leveraging the power of Unity Simulation. No prior Unity experience required. ## Documentation In-depth documentation on inidividual components of the package. |Feature|Description| |---|---| |[Labeling](com.unity.perception/Documentation~/GroundTruthLabeling.md)|A component that marks a GameObject and its descendants with a set of labels| |[Label Config](com.unity.perception/Documentation~/GroundTruthLabeling.md#label-config)|An asset that defines a taxonomy of labels for ground truth generation| |[Perception Camera](com.unity.perception/Documentation~/PerceptionCamera.md)|Captures RGB images and ground truth from a [Camera](https://docs.unity3d.com/Manual/class-Camera.html).| |[Dataset Capture](com.unity.perception/Documentation~/DatasetCapture.md)|Ensures sensors are triggered at proper rates and accepts data for the JSON dataset.| |[Randomization (Experimental)](com.unity.perception/Documentation~/Randomization/Index.md)|The Randomization tool set lets you integrate domain randomization principles into your simulation.| ## Example Projects ### 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 the Perception toolset could be used in a smart city or autonomous vehicle simulation. You can generate datasets locally or at scale in [Unity Simulation](https://unity.com/products/unity-simulation). ## Local development The repository includes two projects for local development in `TestProjects` folder, one set up for HDRP and the other for URP. ### Suggested IDE Setup For closest standards conformity and best experience overall, JetBrains Rider or Visual Studio w/ JetBrains Resharper are suggested. For optimal experience, perform the following additional steps: * To allow navigating to code in all packages included in your project, in your Unity Editor, navigate to `Edit -> Preferences... -> External Tools` and check `Generate all .csproj files.` ## Known issues * The Linux Editor 2019.4.7f1 and 2019.4.8f1 might hang when importing HDRP-based Perception projects. For Linux Editor support, use 2019.4.6f1 or 2020.1 ## License * [License](com.unity.perception/LICENSE.md) ## Support For general questions or concerns please contact the Computer Vision team at computer-vision@unity3d.com. For feedback, bugs, or other issues please file a GitHub issue and the Computer Vision team will investigate the issue as soon as possible. ## Citation If you find this package useful, consider citing it using: ``` @misc{com.unity.perception2021, title={Unity {P}erception Package}, author={{Unity Technologies}}, howpublished={\url{https://github.com/Unity-Technologies/com.unity.perception}}, year={2020} } ```