* **Action**: Navigate to the dataset path addressed above.
* **Action**: Navigate to the dataset path addressed in the _**Console**_.
\- Logs
- Logs
- JSON data
- RGB images (raw camera output) (if the `Save Camera Output to Disk` checkmark is enabled on `Perception Camera`)
- Semantic segmentation images (if the `SemanticSegmentationLabeler` is added and active on `Perception Camera')
* **Action**: Follow the instructions laid out in the notebook and run each code block to view its outputs. Not how parts of the code that are relevant to Unity Simulation are commented.
Below, you can a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome.
Below, you can see a sample plot generated by the Dataset Insights notebook, depicting the number of times each of the 10 foreground objects appeared in the dataset. As shown in the histogram, there is a high level of uniformity between the labels, which is a desirable outcome.
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This concludes Phase 1 of the Perception tutoial. In the next phase, you will dive a little bit into randomization code and learn how to build your own custom `Randomizer` quickly. Click here to continue to Phase 2:
This concludes Phase 1 of the Perception tutoial. In the next phase, you will dive a little bit into randomization code and learn how to build your own custom `Randomizer` quickly. [Click here to continue to Phase 2: Custom Randomizations](Phase2.md)
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