Follow the rest of the steps inside the notebook to generate a variety of plots and stats. Keep in mind that this notebook is provided just as an example, and you can modify and extend it according to your own needs using the tools provided by the [Dataset Insights framework](https://datasetinsights.readthedocs.io/en/latest/).
This concludes the Perception Tutorial. The next step in this workflow would be to train an object-detection model using a dataset generated on Unity Simulation. It is important to note that the 1000 large dataset we generated here is probably not sufficiently large for training most models. We chose this number here so that the run would complete in a fairly short period of time, allowing us to move on to learning how to analyze the statistics of the dataset. In order to generate data for training, we recommend a dataset of about 400,000 captures.
The next step in this workflow, which is out of the scope of this tutorial, is to train an object-detection model using our synthetic dataset. It is important to note that the 1000 large dataset we generated here is probably not sufficiently large for training most models. We chose this number here so that the Unity Simulation run would finish quickly, allowing us to move on to learning how to analyze the statistics of the dataset. In order to generate data for training, we recommend a minimum dataset size of around 50,000 captures with a large degree of randomization.
This concludes the Perception Tutorial. In case of any issues or questions, please feel free to open a GitHub issue on the `com.unity.perception` repository so that the Unity Computer Vision team can get back to you as soon as possible.
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