Skip to main content

FAQ

  • FAQ

    • How quickly can I get started using the dataset?

      Check out the Colab tutorials for a walkthrough of the data format (Colab for Perception Dataset, Colab for Motion Dataset). Just open the tutorial notebook in Colab. Please note that the tutorial currently uses some sample frames - it does not access the actual dataset files.

      The Quick Start can help you get the codebase installed on your machine locally.

    • Where can I learn the basics of Colab?

      You can learn the basics of Colab with this Welcome notebook.

    • Where can I learn the basics of TensorFlow?

      Google’s Machine Learning Crash Course is the best place to start.

    • Where can I find the metrics code?

      Check out the metrics directory in the GitHub repo.

    • Where can I find the Perception Dataset's labeling specification?

      You can find it in the GitHub repo here.

    • Is there a paper with more details on the composition of the dataset?

      Yes! You can find it on arXiv here. The paper on the Motion Dataset is also on arXiv here.

    • Can I use the Waymo Open Dataset to evaluate real-life vehicle performance?

      No. The Waymo Open Dataset contains an unlabeled mixture of data collected in both manually-driven and autonomously-driven modes. Because it is not possible to distinguish between the two modes, the dataset is not suitable for drawing conclusions about the specific behavior or performance of any real-life vehicle. The dataset is intended to help the research community solve broad perception and motion forecasting challenges.

    • Is this data being offered under an open source license?

      The Dataset license has certain limitations around distribution and should not be considered an open source license. If you are interested in a license with different terms, please contact us at open-dataset@waymo.com.

    • What are some examples of acceptable and unacceptable uses of the dataset under its license?

      Example 1, Acceptable Use:

      You are a researcher at a university. You use the dataset to perform benchmarking on algorithms you’ve developed. You publish the results. Your published paper includes small extracts of data taken from the dataset for purposes of illustration. Your paper provides the appropriate attribution to the Waymo Open Dataset.

      Example 2, Acceptable Use:

      You are a researcher at a technology company. You experiment on the dataset using internal systems that are not used to provide a product or service to customers. You develop algorithms, model definitions, and training code as a result of those experiments, and check them into those internal systems with the appropriate attribution to the Waymo Open Dataset. You submit those algorithms and model definitions to a conference for public review. You include the appropriate attribution to the Waymo Open Dataset. Your submission is accepted. You release the training code to let others replicate your results.

      You are a researcher at another company. You find the publication interesting. You download the dataset from Waymo and train the published model definition against the dataset to see if you can confirm the results.

      Example 3, Unacceptable Use:

      You are an engineer at an autonomous vehicle company. You use the dataset to train a prototype object detection model for use on a vehicle at a test track. You use this trained model as a placeholder until you build a large enough internal dataset to train your model against.

      Example 4, Unacceptable Use:

      You train and fine-tune existing models based on the dataset. You use weights and biases from that model and deploy them in a system you intend to use for current or future customers.

    • Can I use the dataset to create and submit benchmark results for publication on MLPerf.org?

      Yes. We consider benchmarking for the purpose of publishing on MLPerf.org to be a non-commercial use.

    • Is republishing my official MLPerf.org scores or publishing unverified MLPerf benchmark scores elsewhere, like my blog or marketing materials, an acceptable use of the Waymo Open dataset?

      If you used the Waymo Open Dataset to create and submit benchmark results for publication on MLPerf.org, republishing the results in other locations is acceptable. If you used the Waymo Open Dataset to create unverified benchmark results consistent with the MLPerf.org rules, publishing the results in other locations is acceptable.
      Please adhere to the trademark usage guidelines and terms of use required by MLPerf.org and Waymo.

      Example formats:

      achieved a score of <0.X> on the MLPerf with the , trained on the Waymo Open Dataset [1].

      [1] MLPerf v1.0 Training Automotive Object Detection. Retrieved from mlperf.org 21 December 2021, entry 1.0-134. MLPerf name and logo are trademarks. See mlperf.org for more information. Waymo Open is a trademark. See waymo.com/open for more information. No endorsement or affiliation is implied.

      or

      announced an unverified score of <0.X> on the MLPerf with the , trained on the Waymo Open Dataset [1].

      [1] MLPerf v1.0 Training Automotive Object Detection; Result not verified by MLPerf. MLPerf name and logo are trademarks. See mlperf.org for more information. Waymo Open is a trademark. See waymo.com/open for more information. No endorsement or affiliation is implied.

    • What are you doing to ensure the privacy of people in the images?

      The images in the Waymo Open Dataset blur any license plates and faces.

    • How can I remove my entry from the challenge leaderboards?

      If you would like to remove your entry from the leaderboard, please reach out to open-dataset@waymo.com.

    • How can I get Google Cloud credits to work with the dataset?

      Faculty and PhD students can apply for Google Cloud credits.

    • Who should I contact with questions, thoughts, and suggestions on the Open Dataset?

      Please reach out to open-dataset@waymo.com.