Waymo’s co-CEO Dmitri Dolgov shares Waymo’s approach to building powerful machine learning models for self-driving vehicles

Dmitri Dolgov of Waymo Discusses Data-centric AI - TransformX 2021 Conference - Scale AI

Dmitri Dolgov of Waymo Discusses Data-centric AI

The co-founder of Schmidt Futures and a former CEO of Google, discusses how AI will shape our global future. Eric was joined by Scale AI CEO Alexandr Wang

Eric Schmidt Discusses the Geopolitics of AI - TransformX 2021 Conference - Scale AI

Eric Schmidt Discusses the Geopolitics of AI

Justin Basilico, Director of Machine Learning and Recommender Systems at Netflix describes how ‘everything at Netflix is a recommendation’.

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Today we&rsquo;re thrilled to share that Nucleus now supports 3D Sensor Fusion data, including LiDAR point clouds.
<a href="https://scale.com/nucleus" target="_blank" rel="noopener noreferrer">Scale Nucleus</a> is the mission control for your data, providing advanced tooling for curating, visualizing and exploring machine learning datasets and debugging ML models. We recently launched an all-new Nucleus for dataset management, complete with privacy features and updated Autotag capabilities, all to help teams build better ML models by building better datasets. Now, Nucleus can help teams manage 3D datasets to develop highly accurate perception models for a range of use cases, including autonomous vehicles, robotics, and augmented/virtual reality (AR and VR).
Scale Nucleus helps you visualize and debug 3D Sensor Fusion data by making it easy to:
<ul>
<li>Construct Scenes that contain a sequence of LiDAR point clouds and their corresponding camera images over time</li>
<li>Visualize, query and share sensor fusion data with colleagues</li>
<li>Integrate Sensor Fusion data with your 3D model predictions to uncover and identify:</li>
<li>Discrepancies between 3D object detection model outputs and ground truth</li>
<li>False positive, common class confusions, and cuboid predictions that have low Intersection over Union (IoU) with ground truth</li>
<li class="ql-indent-1">Model performance and improvements over time</li>
</ul>
The Nucleus API makes it easy to import your data, construct scenes, and add model predictions. You can also import data from an existing Scale project directly into Nucleus.
<h2>A Primer: Nucleus Scenes</h2>
Scenes are the building blocks of 3D datasets in Nucleus and are designed with point clouds as the primary unit of data. More formally, a Scene is a sequence of Frames over time, where each Frame contains exactly one point cloud and an optional set of camera images. Here we can see a LiDAR point cloud above two paired images (the full dataset includes more than just front and back cameras, but a full suite of sensor data), which helps explain why we designed Nucleus Scenes to encapsulate data across both time and sensor modalities:
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/00157f16-6ed9-4ffc-45cc-60b676806e00/public" alt="Structure Diagram of Nucleus 3D Scenes">One useful way to interpret a LiDAR Scene is as a table of sensor readings. A row in the table corresponds to a sequence of readings from a particular sensor, while a column in the table represents a collection of sensor data from the same timestamp (we call this a &ldquo;frame&rdquo; of the scene).
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6ea98ea3-b4d4-40eb-f36f-3562514cab00/public" alt="Complex Nucleus 3D Scene on SF's Embarcadero, Quarter Angle View">
On their own, point clouds can be difficult to interpret, due to lack of motion cues or associated camera data. Nucleus Scenes make sets of 3D and 2D data easier to interpret for the following reasons: first, any number of 2D camera frames can be attached to each 3D LiDAR frame; and second, Scenes link frames from the same timespan so that you can examine all frames in a linear fashion.
Let&rsquo;s now view the same Scene in Nucleus and see if we can identify the mystery object from above:
<iframe class="ql-video" src="https://scaleai.wistia.com/embed/medias/8ikm69knof" frameborder="0" allowfullscreen="allowfullscreen"></iframe>
After uploading predicted cuboids from an ML model for your Scene, Nucleus automatically computes performance metrics under the hood. You can sort predicted cuboids by IoU or filter by false positives to quickly narrow in on your model&rsquo;s failure modes. (These features have been available for 2D image analysis for some time, but today we are announcing their ability for 3D data, too.)
To summarize, by creating and uploading 3D Scenes to Nucleus, you can easily visualize LiDAR point clouds and images side-by-side, quality-check your ground truth annotations, and search your data based on metadata or even ML-produced attributes. Lastly, you can use Nucleus to measure key prediction performance metrics, such as IoU and false positives.
<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6908e9df-249d-47df-4db5-a39827556c00/public" alt="Birds' Eye View of SF Embarcadero as Seen through LiDAR and Multiple Image Sensors in Nucleus Scene">
<h2>Getting Started with 3D Data in Nucleus</h2>
Visualizing, querying, and measuring the quality of sensor fusion datasets is a challenge facing even the most sophisticated ML teams. With the newly launched support of multimodal scenes, Nucleus makes it easy to navigate through massive datasets, and quickly narrow down on the most difficult examples. Unleash the full potential of your datasets by leveraging Nucleus for debugging, search, and quality related workflows.
The complete code for this tutorial is available in <a href="https://colab.research.google.com/github/brwillia/nuc-3d/blob/main/nucleus_3d_demo.ipynb#scrollTo=rr88VqEHaL2_" target="_blank" rel="noopener noreferrer">this Colab notebook</a>. If you&rsquo;d like to follow along step by step and create a personal dataset on Nucleus, you will first need to make a <a href="http://scale.com/signup" target="_blank" rel="noopener noreferrer">free Scale account</a> and obtain a test API key (which you can <a href="https://dashboard.scale.com/settings/profile" target="_blank" rel="noopener noreferrer">find here</a> if you navigate to your user icon, and then click on API keys). You can then copy the notebook and add your personal key to upload your data and try out Nucleus for yourself. At the end, you&rsquo;ll also find a section that shows you how to format and upload your own LiDAR data for curation and management in Nucleus if you&rsquo;re ready to manage your own 3D data in Nucleus.

Learn how to ingest and curate 3D LiDAR point cloud data in Nucleus.

Nucleus now supports 3D Scenes.

Managing 3D Sensor Fusion Data with Scenes in Scale Nucleus

Kevin Scott, CTO of Microsoft shares how we can democratize AI for the benefit of everyone in society.

Highlights From Kevin Scott Microsoft - TransformX 2021 Conference - Scale AI

Microsoft CTO Kevin Scott's Seven Insights on the Future of AI

Editor’s note: This post originally appeared on <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">lambdalabs.com</a>. You can find a link to it <a href="https://lambdalabs.com/blog/lambda-scale-nucleus/" rel="noopener noreferrer" target="_blank">here</a>. Training accurate models typically requires a deep understanding of your training data. Through visualization, it’s important to both identify potential flaws in your ground truth labels, and to look for insights that will guide model development. Thus, visualizing your training data is the foundation of every successful deep learning project. Nucleus is a tool from Scale that allows you to visualize and debug your training data. It provides an intuitive web interface to visualize and explore a wealth of different types of data and their labels. It also gives you the tools to explore and understand the relationship between your data and the model predictions that you generate. In this post, we'll show you how you can get started with Nucleus by training a pedestrian detection model. To do this, we will use Nucleus to visualize an object detection dataset containing images of common street scenes, and compare the ground truth to the predictions from a trained neural network. This loop of inspecting your training data and looking for failure modes in a network's predictions is a crucial process in refining both your training dataset and your neural network.<h2>Getting Started</h2> The example model in this post was trained on a <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda Vector workstation</a> running <a href="https://lambdalabs.com/lambda-stack-deep-learning-software" rel="noopener noreferrer" target="_blank">Lambda Stack</a>. If you want to follow along, please see the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>. To start, we’ll download the publicly available <a href="https://www.cis.upenn.edu/~jshi/ped_html/" rel="noopener noreferrer" target="_blank">Penn-Fudan Dataset</a>, which consists of photographs of pedestrian data with labeled bounding boxes. To load these into Nucleus, we need to do two things: first, sign up for a free Scale account and corresponding API key, and second, install the Nucleus client on our Lambda workstation from a command prompt or shell:<pre class="ql-syntax" spellcheck="false">pip install scale-nucleus
</pre> <h2>Training Data</h2> Once we've installed the Nucleus client and downloaded the dataset, we can upload the images and ground truth labels to a new Dataset we created in Nucleus. (For the full details of how to do this, see <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ#scrollTo=jC-728nqvD0L" rel="noopener noreferrer" target="_blank">this section</a> of the notebook.) Now that we’ve uploaded the data, we can visualize the ground truth labels in the Nucleus web dashboard without writing any code.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/edf2a44b-d1e4-4dcc-d6f4-70b3587ac100/public" alt="PennFudan pedestrian dataset with bounding boxes in Nucleus"> <h2>Model Predictions</h2> Now that we've "<a href="https://karpathy.github.io/2019/04/25/recipe/#1-become-one-with-the-data" rel="noopener noreferrer" target="_blank">become one with our data</a>," we can start thinking about training a neural network for object detection. For this example we fine-tuned a <a href="https://arxiv.org/abs/1506.01497" rel="noopener noreferrer" target="_blank">Faster R-CNN</a> model pre-trained on the <a href="https://cocodataset.org/#home" rel="noopener noreferrer" target="_blank">COCO dataset</a>. Training it for 10 epochs only takes a few minutes on an <a href="https://lambdalabs.com/blog/nvidia-rtx-a6000-benchmarks/" rel="noopener noreferrer" target="_blank">NVIDIA RTX A6000</a> in a Lambda Vector Workstation. After training is finished we can see that our model has reached an Average Precision (AP@IoU=0.50:0.95) of 0.83. This sounds OK. It's not too far from 1, but what does it mean? Do we have a good pedestrian detector yet? The AP metric of object detection is notoriously hard to get your head around, and there are endless <a href="https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173" rel="noopener noreferrer" target="_blank">blog posts</a> that aim to provide an intuition for it, but you’ll ultimately need to look at our results to see how we're doing. Nucleus allows us to upload our model predictions and compare them to the ground truth. Here we can see that blue boxes predicted by our model match pretty well with the green ground truths from the dataset: <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/1dc58fa6-c049-4610-324b-aab8d114a600/public" alt="Image Grid, Ground Truth versus Fast-RCNN Bounding Boxes"> <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/005b8ff2-c8e7-44a0-42bc-615be98ba000/public" alt="Nucleus Annotation, Classification, and IoU Detail"> <h2>What to Try Next?</h2> Nucleus also gives some nice tools to help us figure out what else we should try to improve our accuracy results. Here we visualize the ground truth labels with the model predictions so that we can get a feel for where our model is going off the rails. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/08ad13c7-2552-45ce-0650-cc8e4115a200/public" alt="Correcting the Ground Truth Bounding Box in Nucleus"> In many cases, it looks like our model is already doing a pretty good job, and it's the ground truth labels that have actually missed some pedestrians! For anyone who has worked on a practical machine learning project, this is probably a familiar story—finding and fixing errors and problems in your dataset is just as much part of deep learning as designing and training models. In this case, we’ll need to go back to our data labels and fix the ground truth errors in order to get a more accurate evaluation of our model. This is also something we can do directly in Nucleus with its convenient annotation tools. <h2>Conclusion</h2> Nucleus is a convenient way to visualize and organize your training data for a machine learning project, as we showed in our object detection example. Most importantly, it makes it easy to inspect and track the predictions from your trained models, and helps you establish the virtuous cycle of training networks, evaluating predictions, identifying failure cases, and improving both data and models: all of these steps prove to be crucial in developing an effective deep learning project. To learn more about Lambda workstations, <a href="https://lambdalabs.com/" rel="noopener noreferrer" target="_blank">check out Lambda’s website</a>, or to get started managing and improving your dataset, <a href="https://dashboard.scale.com/nucleus" rel="noopener noreferrer" target="_blank">sign up for Scale Nucleus here</a>. If you want to try this workflow out on your own <a href="https://lambdalabs.com/gpu-workstations/vector" rel="noopener noreferrer" target="_blank">Lambda workstation</a> or Lambda <a href="https://lambdalabs.com/service/gpu-cloud" rel="noopener noreferrer" target="_blank">Cloud instance</a>, you can explore the <a href="https://colab.research.google.com/drive/1Cu2Xw0kPwbMMSdqUsSgmMMTzQ5b7usDZ" rel="noopener noreferrer" target="_blank">example notebook here</a>.

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TransformX Conference brought together leaders, engineers, & researchers to explore the shift from research to reality within AI & Machine Learning.

Highlights From TransformX 2021 Conference - Scale AI

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