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We at Scale AI felt inspired to use our computer vision training tools to put the two self driving methods to the test. 

Is Elon Musk wrong about LiDAR? We put the two self driving methods to the test.

Is Elon Wrong About LiDAR?

Our mission at Scale is to accelerate the development of AI applications. We’re proud of what we’ve built over the last three years, and today we’re announcing our Series C funding round to support our continued work against that mission. Scale has raised $100m at a valuation of over $1B. <a href="https://foundersfund.com/" rel="noopener noreferrer" target="_blank">Founders Fund</a> led the round, supported by <a href="https://accel.com/" rel="noopener noreferrer" target="_blank">Accel</a>, <a href="https://www.coatue.com/" rel="noopener noreferrer" target="_blank">Coatue Management</a>, <a href="https://www.indexventures.com/" rel="noopener noreferrer" target="_blank">Index Ventures</a>, <a href="http://www.sparkcapital.com/" rel="noopener noreferrer" target="_blank">Spark Capital</a>, <a href="https://www.thrivecap.com/" rel="noopener noreferrer" target="_blank">Thrive Capital</a>, <a href="https://en.wikipedia.org/wiki/Kevin_Systrom" rel="noopener noreferrer" target="_blank">Kevin Systrom</a>, <a href="https://en.wikipedia.org/wiki/Mike_Krieger" rel="noopener noreferrer" target="_blank">Mike Krieger</a> and <a href="https://en.wikipedia.org/wiki/Adam_D%27Angelo" rel="noopener noreferrer" target="_blank">Adam d’Angelo</a>. We are big believers in the transformational impact of AI over the coming decades. Machine learning is likely the most important technological shift of our time, and the overall benefits to the world will be comparable to those of the internet. What we noticed after working on AI at some of the most advanced organizations in the world was that building machine learning systems was challenging due to a lack of mature infrastructure. In particular, we noticed that the critical bottleneck to further progress today was data—in particular, labeled datasets. Many engineers will tell you that getting labeled data is the hardest part of building a machine learning model. Building AI represents a fundamentally different paradigm than building traditional software. The performance of an AI system depends more on the data than the algorithm—in most cases, without labeled data, there is no model. Similarly, for a model to improve and adapt, it requires more data rather than simply more code. This paradigm has been true since the very beginning of deep learning; the modern deep learning age was ignited by the launch of the ImageNet dataset by Fei-Fei Li’s lab at Stanford in 2009.<img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2F8c80d06e32aa42c689836dbda1f7fd09" alt="AI DEV CYCLE"><img src="https://cdn.builder.io/api/v1/image/assets%2Fe0438815ba51486bbb6a202747122d4b%2Fb0518c76600f4b9fa58189d4bfe750ba" alt="TRADITIONAL DEV CICLE"> We set out to solve this problem by building a platform that enables organizations with large high-quality datasets needed to build AI systems. Especially now, as AI is being applied in more and more ways, this matters because:<ul><li> </li><li> Safe, accurate and unbiased AI systems depend on large volumes of high</li><li> quality training data.</li><li> </li><li> </li><li> The process to acquire, label, and verify training data is slow, manual and</li><li> expensive.</li><li> </li><li> </li><li> Today’s training data bottleneck limits AI's impact to a small group of</li><li> well-funded technology companies.</li><li> </li><li> </li></ul> Scale is a solution to these problems. Developers use our API to send their raw data to our platform. We then use a combination of machine learning and human insight to label and annotate this data. We pass this high-quality ground truth data back to our customers, who are then enabled to build world-class models to solve real-world problems. Scale is now used by world leading machine learning teams at companies like OpenAI, Airbnb, Nuro, and Lyft. Our platform is being used to provide high-quality training data to an ever growing number of machine learning teams and industries, including:<ul><li>Autonomous Vehicles: Advancements in AI for perception,</li><li> prediction, and planning have enabled the development of autonomy, which in</li><li> turn relies on data.</li><li> </li><li>Natural Language and Content Understanding: More than ever</li><li> before, systems can understand and drive decision-making on large corpuses</li><li> of speech, text, images, and other forms of content.</li><li> </li><li>E-commerce and Search: Powerful search systems that connect</li><li> consumers to specific content, images, or products increasingly rely on AI</li><li> systems to drive understanding of this content. Our platform helps engineers</li><li> train those systems.</li><li> </li><li>Mapping: Mapping using satellite, drone, and street-level</li><li> imagery for use cases from agriculture to insurance rely on high quality</li><li> training data to ensure accuracy.</li><li> </li><li>Robotics: Robots in factory or warehouse settings need to</li><li> be able to understand their environments, safely interact with humans and</li><li> recognize products or components.</li><li> </li><li>Augmented Reality and Virtual Reality: AR and VR systems</li><li> used in gaming, real estate or manufacturing rely on being able to perceive</li><li> the 3D environment around a camera, headset or user.</li><li> </li><li>Offline retail: Cashierless checkouts, inventory management</li><li> systems and footfall analysis all depend on perception systems, which in</li><li> turn depend on training data.</li><li> </li><li> </li></ul> We’re proud of the progress we’ve made building the Scale platform for our customers. It’s been an incredibly quick 3 years since I started the company, and the ability we’ve had to accelerate progress in machine learning is palpable. But, as we hope is always the case, we’re far more excited about what we will do in the future. There is a lack of mature infrastructure for building AI, forcing teams to build their own technology stack from scratch before they can have a meaningful impact. Per our mission, we know there are many problems beyond the training data bottleneck for us to solve to continue accelerating AI development. Despite our excitement for the potential of AI, we’re closer to the beginning of its history than we are to the end. We’re grateful to our customers for building world-leading AI products with Scale, and we’re looking forward to accelerating the development of machine learning in an even broader set of industries and technologies as we grow. If you are interested in being part of creating the infrastructure machine learning needs, take a look at our <a href="https://scale.com/careers?location=All&amp;team=All" rel="noopener noreferrer" target="_blank">open positions</a>. If you're interested in partnering with us, please <a href="https://scale.com/sales" rel="noopener noreferrer" target="_blank">reach out</a>.

Scale AI’s Series C: Building the data platform for ML

Scale AI’s Series C: Building the data platform to accelerate machine learning

Scale AI is pleased to announce the launch of Sensor Fusion Segmentation - Scale’s endpoint for point cloud segmentation.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://lidar-now.scale.com/examples/semseg" height="100%" width="100%"></iframe> Sensor Fusion Viewer rendering a scene from nuScenes Released at CVPR 2019, Sensor Fusion Segmentation provides the highest precision for annotating complex objects that cannot be easily described with LiDAR cuboid labeling. Examples include vegetation, smoke/exhaust, splashes/puddles, rain and reflections.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/34fe7aaa-a1a6-4737-823a-ffe19dbe8b00/public" alt="fog seen through LiDAR">Fog<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/bbbb9168-544a-4866-b096-9bb07cf87c00/public" alt="fog seen through LiDAR">Smoke/Exhaust<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6cd0ad4c-0d0a-48c6-65f3-e18ac64a1700/public" alt="vegetation seen through LiDAR">Vegetation<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ae620a78-79bd-4c4c-2ade-0e7de9ddf500/public" alt="Trucks with open backs seen through LiDAR">Trucks with open backs<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9cb13a70-42a2-4559-1c44-a5b34bebbc00/public" alt="reflections seen through LiDAR">Reflections<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/dff8770c-e1cc-4e77-8d73-d35007f3f200/public" alt="Splashes &amp; Puddles seen through LiDAR">Splashes &amp; Puddles Semantic scene understanding is important for a variety of applications, particularly autonomous driving. Rigorously tested by a handful of Scale’s customers during a private beta, Sensor Fusion Segmentation annotation provides fine-grained understanding of surfaces and objects in a 3D point cloud. Take a look below, for how we helped the Toyota Research Institute (TRI) accelerate their research projects by giving them greater flexibility and the ability to amend workflows.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://www.youtube.com/embed/2XURB1nE11w"></iframe> Since starting its work with Scale, TRI has been able to support four large annotation pipelines without significantly increasing the size of their team.<a href="https://scale.ai/sensor-fusion-segmentation" rel="noopener noreferrer" target="_blank">Learn More</a>

Introducing Point Cloud Segmentation

We believe that creating jobs can be very impactful. We asked some labelers for times when working for Scale made a positive difference in their lives.

Positive Externalities

Positive Externalities: Life of a Labeler

Deep Learning models have amazing capacity, <a href="https://ai.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html" rel="noopener noreferrer" target="_blank">getting better with more data</a> seemingly without limit. To get a well-functioning model, it is not enough to have large amounts of data, you also need accurate annotations. Although large amounts of data will help the model resolve data inconsistencies between different annotations, humans can still make repeated mistakes that become ingrained in the model. For example, when asked to draw a box around an object, it is natural for humans to always ensure that the box contains the object - i.e., to err on the side of making the box too big. Using such a model for collision avoidance will result in false positives, causing the autonomous vehicle to halt unnecessarily. The oversizing of bounding boxes is an example of a systematic error, as opposed to a random error. Random and systematic errors impact the trained model differently. <h2>Random vs Systematic Errors</h2> Random errors cause the model to require more data to converge to the same result. A model trained with data with random errors will ultimately arrive at the same parameters, just more slowly and after having been fed more data. Systematic errors cause the model to converge to a different result - this does not get better with data quantity. A model trained with oversized boxes will output oversized boxes regardless of how much data it is trained with. <h2>Mitigation Strategies</h2> One simple, if expensive, way to deal with random errors is to use more training data. There are also other more sophisticated and cost-efficient approaches - for example, if your training data has multiple sources, and if each source has a different amount of random errors, you can mitigate the effects of the errors by weighing the low-error sources more heavily while training. Systematic errors, on the other hand, can only be resolved by fixing the data, or understanding the nature of error well enough to turn it into a random error. Many interesting techniques exist in this area, including <a href="https://arxiv.org/abs/1802.05300" rel="noopener noreferrer" target="_blank">Golden Label Correction</a>, where you carefully annotate a subset of the data in order to characterize the non-randomness of the rest of the data. At the end of the day, the key to overcoming annotation errors effectively and efficiently is understanding how the errors are generated. Let’s take the previous example of oversized bounding boxes. Taking into account the tendency of inexperienced workers to oversize boxes, Scale is able to use simple models to check for oversizing. Over time, with feedback from those models, workers learn to make fewer systematic mistakes, consistently drawing tight bounding boxes. By matching complex annotation specifications with annotator training and an understanding of the systematic errors, Scale is able to efficiently produce quality annotations in quantity, facilitating cost-effective training of accurate models.

Training Data - Quantity vs Quality of Annotated Data - Scale

Training Data - Quantity is no Panacea

We’ve had an incredible group of people join Scale—and we’re still <a href="https://scale.com/about#jobs" rel="noopener noreferrer" target="_blank">hiring</a>! We wanted to take a moment to follow up on <a href="https://scale.com/blog/scale-engineering-team" rel="noopener noreferrer" target="_blank">this post</a> and call out the latest team members that have joined the Engineering, Product and Design teams at Scale: <ul><li> </li><li>Catherine Wu / Catherine is a full-stack engineer at Scale working primarily on our infrastructure team. She has previously worked as an engineer at Google and product manager at Yelp. She studied computer science at Princeton University, where she also competed on the synchronized skating team.</li><li> </li><li> </li><li>Eugene Fratkin / Prior to Scale, Eugene led R&amp;D for the Cloud division at Cloudera, responsible for the development of managed distributed data processing services in AWS/Azure. Prior to Cloudera, Eugene was a co-founder and VP of R&amp;D at Lifecode – a Sequoia Capital-backed genomic cancer diagnostic start-up. Prior to Lifecode, he was one of the founding members of Greenplum’s machine learning team, which started the MADlib Apache project. Eugene holds a PhD in Machine Learning from Stanford. Eugene also served as an assault team section leader in the United States Marine Corps for missions such as the anti-terrorism quick reaction force within the continental US, and the initial phase of Operation Iraqi Freedom.</li><li> </li><li> </li><li>Gabriel Andretta / Gabriel previously worked at <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> with Ricky, Martín and Ramiro, where he rebuilt <a href="https://github.com/auth0/lock" rel="noopener noreferrer" target="_blank">Lock</a> from scratch and was the lead developer of the browser SDKs. He was a contributor to the sinatra web framework, a coach in the first edition of <a href="https://twitter.com/ClojureBridgeBA" rel="noopener noreferrer" target="_blank">Clojure Bridge Buenos Aires edition</a>, and built a <a href="https://github.com/gnandretta/literate-2048" rel="noopener noreferrer" target="_blank">2048</a> clone with almost no mutable state.</li><li> </li><li> </li><li>Steven Salka / <a href="http://ssalka.io/" rel="noopener noreferrer" target="_blank">Steven</a> joined the engineering team at Scale as a product-focused full-stack engineer, working largely on internal applications and the frontend for Scale’s LiDAR tool. Previously, he was the first engineering hire at <a href="https://podible.co/" rel="noopener noreferrer" target="_blank">Podible</a>, and before that built out a UI framework for text-based annotations at <a href="https://x.ai/" rel="noopener noreferrer" target="_blank">x.ai</a>. Steven studied Applied Mathematics at UC Berkeley.</li><li> </li><li> </li><li>Ramiro Silveyra d'Avila / Prior to Scale, Ramiro was a software engineer at <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> working on the marketing and engineering teams. He loves open source software, especially web tooling. He has contributed to yarn, babel, stylelint, eslint plugins and webpack loaders.</li><li> </li><li> </li><li>Michael Woo / Michael is a supply product manager at Scale. After graduating from Harvard in Computer Science, he joined McKinsey &amp; Company as a business analyst. Michael is a former captain of the Harvard Fencing Team and National Junior Team Member for the US National Fencing Team.</li><li> </li><li> </li><li>Sara Xiang / Sara is a supply product manager at Scale. She previously worked at Twitter on the Moments team, and at Facebook on the Oculus Developer Technologies and Oculus TV teams. She studied computer science at Carnegie Mellon University.</li><li> </li><li> </li><li>Pato Perez / Pato is a full-stack engineer at Scale working primarily on our LiDAR annotation tool. He previously worked as an Android and iOS engineer at other startups. He was the winner of multiple software engineering competitions, including Microsoft Imagine Cup @ Australia.</li><li> </li><li> </li><li>Shivaal Roy / Shivaal graduated from Stanford with a B.S. and M.S. in computer science. He founded Stanford's health hackathon, <a href="http://healthplusplus.stanford.edu/" rel="noopener noreferrer" target="_blank">Health++</a>, and was president of <a href="http://shift.stanford.edu/" rel="noopener noreferrer" target="_blank">SHIFT</a>, Stanford’s health-tech student organization. Prior to Scale, he worked at Google on the Photos Machine Intelligence team, and at Oscar Health.</li><li> </li><li> </li><li>Mauro Giamberardino / Mauro is a senior full-stack engineer who previously worked for companies like Globant and Spark Digital. He studied Systems Engineering at Buenos Aires University.</li><li> </li><li> </li><li>Martin Cabral / Martin joins Scale as a software engineer with ten years of experience in different positions and companies. His last adventure was as a product manager at <a href="https://www.auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> where he learned to work very closely with customers and understand how they approach products. When he is not working, you can find him gaming (Dota2, CS, Fortnite et al.) or playing real life tennis.</li><li> </li><li> </li><li>Nathan Hayflick / Nathan is a frontend engineer at Scale. He joins from Uber, where he worked for 3 years on the Safety Engineering and Develeper Platform teams, and worked at Rockbot before that. He studied Literature at Stanford University.</li><li> </li><li> </li><li>Jeff Hilnbrand / Jeff is the Design Manager at Scale. He was most recently a Senior Product Designer at Facebook on the Groups and Payments teams. Prior to that, Jeff founded a design agency called Minimill and worked on design teams at Autodesk and Venmo. Jeff studied product design and engineering at the University of Maryland.</li><li> </li><li> </li><li>Andrew Wang / Andrew was a tech lead at Cloudera working on HDFS and cloud-based machine learning platforms for 6 years. He’s one of the biggest contributors to the open-source Apache Hadoop project, served as the release manager for Apache Hadoop 3.0, and is an Apache member. Before that, he was a PhD student in distributed systems at UC Berkeley and studied computer science at the University of Virginia.</li><li> </li><li> </li><li>Craig Weiss / Craig joins Scale with experience from Lyft, Snapchat, Facebook, and Google. Craig frequently competed in and won several awards at regional hackathons and competitive programming competitions (including the ACM-ICPC). Craig studied computer science at the University of Maryland.</li><li> </li><li> </li><li>Aatish Nayak / Aatish is a supply product manager at Scale, with previous experience at Shield AI, Uber, MongoDB, and Skurt. At ShieldAI he worked on life-saving autonomous robots for special operations and law enforcement use cases. Aatish studied computer engineering at Carnegie Mellon University and led Autolab, a startup for auto-grading CS homework assignments.</li><li> </li><li> </li><li>Ty Wilkins / Ty has worked closely with Scale from the beginning to establish Scale’s branding, icon system, and illustration style. Previously, Ty was an early employee at <a href="https://dribbble.com/shots/2994377-Robinhood-Gold" rel="noopener noreferrer" target="_blank">Robinhood</a> where he created product UI, branding, illustration, and marketing. He has extensive experience in branding, working with clients including Google, <a href="https://dribbble.com/shots/2347496-Facebook" rel="noopener noreferrer" target="_blank">Facebook</a>, and <a href="https://dribbble.com/shots/195804-Target-Education" rel="noopener noreferrer" target="_blank">Target</a>. Ty has also branded numerous startups including <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a>, <a href="https://www.useloom.com/" rel="noopener noreferrer" target="_blank">Loom</a>, and <a href="https://dribbble.com/shots/314295-Orchestra" rel="noopener noreferrer" target="_blank">Orchestra</a> in addition to many open source projects including <a href="https://dribbble.com/shots/4738771-Celery" rel="noopener noreferrer" target="_blank">Celery</a> and <a href="https://dribbble.com/shots/2153014-JWT" rel="noopener noreferrer" target="_blank">JWT</a>. His editorial illustration work has been featured in publications including <a href="https://dribbble.com/shots/192949-Monocle" rel="noopener noreferrer" target="_blank">Monocle</a>, <a href="https://dribbble.com/shots/1215603-Bloomberg-Markets" rel="noopener noreferrer" target="_blank">Bloomberg Markets</a>, <a href="https://dribbble.com/shots/2083018-ESPN" rel="noopener noreferrer" target="_blank">ESPN Magazine</a>, and <a href="https://dribbble.com/shots/766236-Wired-UK" rel="noopener noreferrer" target="_blank">Wired UK</a>. Ty holds a B.F.A. in Graphic Design from Auburn University.</li><li> </li><li> </li><li>Facundo Pepe / Facundo is a UI Developer in charge of the marketing landing pages at Scale. He previously worked at Infobae, the most popular news media in Argentina and at <a href="https://aerolab.co/" rel="noopener noreferrer" target="_blank">Aerolab</a>, a digital agency where he and his team won a <a href="https://www.a24.com/actualidad/a24com-gano-un-martin-fierro-digital-por-la-aplicacion-juga-24-12112018_H1xcRshkN" rel="noopener noreferrer" target="_blank">Martín Fierro Digital award for Best New Application</a> with a betting game for the World Cup. Better known as Turbo because he makes things fast, Facundo loves to deliver pixel perfect projects while working on user interactions that look cool and also perform well.</li><li> </li></ul>

Scale's Engineering, Product and Design Teams - Meet Our Team

Scale Engineering, Product and Design Teams

Scale AI is pleased to announce the full release of <a href="https://www.nuscenes.org/" rel="noopener noreferrer" target="_blank">nuScenes by Aptiv</a> —a large-scale open source dataset for autonomous driving. Scale AI was involved as Aptiv's data labeling partner on this initiative. On March 28, 2019, Aptiv announced the full dataset release of nuScenes, an initiative intended to broadly support research into computer vision and autonomous driving by AV innovators and academic researchers to further advance the mobility industry. One key bottleneck in the advancement of deep learning is access to labeled data. Most machine learning algorithms learn from large amounts of examples to understand the world. These incredible algorithms are powered behind the scenes by billions of hand-labeled examples of what humans can easily understand. While we like to believe as computer scientists that it is all about the algorithms, labeled data is incredibly impactful to progress. The current age of deep learning was ignited by the launch of the ImageNet dataset by Fei-Fei Li's lab at Stanford in 2009. As Fei-Fei said once in an interview: <blockquote>Our vision was that Big Data would change the way machine learning works. Data drives learning.</blockquote><blockquote> </blockquote>The release of the full 1000 scenes means nuScenes now includes 1.4M camera images, 390k LiDAR sweeps, 1.4M RADAR sweeps and 1.4M object bounding boxes in 40k keyframes. nuScenes is the largest multi-sensor dataset for autonomous vehicle data with LIDAR, RADAR, camera, IMU, and GPS, and builds upon great work in the field by others such as the KITTI dataset. Collected in Boston and Singapore, nuScenes data contains 7x more object annotations than KITTI and will enable researchers, students and developers to study challenging urban driving situations. <iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://lidar-now.scale.com/examples/demo" height="100%" width="100%"></iframe> Sensor Fusion Viewer From conception, we partnered with Aptiv to produce the nuScenes dataset. By leveraging Aptiv's autonomy stack and <a href="https://scale.com/sensor-fusion-annotation" rel="noopener noreferrer" target="_blank">our sensor fusion and LIDAR annotation products</a>, nuScenes sets a new standard for quality in public datasets, along with a web-based visualizer for LIDAR and camera data for <a href="https://www.nuscenes.org/explore/scene-0120/0" rel="noopener noreferrer" target="_blank">exploring the dataset</a>. It is crucial to machine learning development to be able to visualize data to develop intuitions about the problems at hand. We, <a href="https://eng.uber.com/avs-autonomous-vehicle-visualization/" rel="noopener noreferrer" target="_blank">like</a> <a href="https://medium.com/cruise/introducing-worldview-749aaf98112d" rel="noopener noreferrer" target="_blank">others</a>, noticed a gap where previous options like <a href="http://wiki.ros.org/rviz" rel="noopener noreferrer" target="_blank">rviz</a> were not built for a web-based world. Our LiDAR visualizer allows point cloud data to be easily embedded into any webpage and shared. We've also built it to be developer-friendly and usable for a broad range of use cases. We hope that the nuScenes dataset will become the new standard in the industry and allow researchers to develop safer autonomous driving technology. There is a large gap today in the machine learning community—the larger organizations have significantly larger datasets and compute budgets than smaller organizations or universities. Our hope is to bridge that gap and democratize access to state-of-the-art data in autonomous vehicles. With the launch of nuScenes, we hope to do for the autonomous vehicle community what ImageNet did for computer vision.

nuScenes by Aptiv ImageNet for Autonomous Vehicles

Scale is committed to accelerating the development of AI applications and serving customers with high-speed, accurate, and affordable data annotation. As part of this mission, Scale and Ouster are pleased to announce a data integration partnership that makes it significantly easier and faster for Ouster customers to send their LiDAR data to the Scale platform for annotation. The two companies have worked together for months to establish a common data format and labeling process that combines the image-like output from Ouster’s lidar sensors and the industry-leading capabilities of Scale’s Sensor Fusion annotation (annotation for LiDAR and RADAR) and Semantic Segmentation annotation solutions to save perception teams time and work. The new approach removes some of the most difficult and time-consuming aspects of data labeling for companies working on everything from autonomous vehicles to industrial robotics and drones. The partnership has a number of specific advantages for customers, including: <ul><li>Streamlined data transfer - A standardized, efficient data</li><li> format reduces the amount of work customers must perform prior to sending</li><li> data for annotation.</li><li> </li><li>No sensor fusion required - Ouster’s lidar sensor outputs</li><li> 2D camera-like imagery in addition to 3D data, reducing the time spent on</li><li> calibrating cameras.</li><li> </li><li>Automatic 2D Mask Generation - Customers can now send a</li><li> single image with 2 API calls to Scale and receive both 2D instance and</li><li> semantic segmentation masks and 3D bounding boxes.</li><li> </li><li> </li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8fdb9340-ff9b-47c0-8ac0-b9811d03c400/public" alt="Point cloud computed only from depth information encoded in image pixels."> Point cloud computed only from depth information encoded in image pixels. Ouster and Scale are leading the industry with this first-of-a-kind integration. In the coming months, Scale intends to form similar data integration partnerships with a broad ecosystem of sensor manufacturers to continue to improve customer experience for LiDAR and RADAR annotation.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ec025881-9915-4b23-e38c-b5d13ac21300/public" alt="Full 360-degree camera view helps taskers locate and annotate objects."> Full 360-degree camera view helps taskers locate and annotate objects. These partnerships will reduce the amount of work customers have to put in to send their data to Scale.AI, to develop a more seamless data pipeline.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/91d02897-d750-45b9-96e3-8867cf5e8700/public" alt="Accurate projections of cuboids from 3D to 2D.">Accurate projections of cuboids from 3D to 2D.

Scale is committed to accelerating the development of AI applications and serving customers with high-speed, accurate, and affordable data annotation.

Announcing the Scale and Ouster Data Integration Partnership - Scale

Announcing the Scale and Ouster Data Integration Partnership

At Scale, we pride ourselves in developing superior tooling and implementing forward-looking solutions for problems our customers on the cutting edge of computer vision, including self-driving cars, AR/VR, and drones encounter. For example, our 2D image annotation endpoints now support both per-annotation attributes and label hierarchies, allowing you to get richer labeled data out of Scale.This post will briefly outline the differences between the two. <h2>Per-Annotation Attributes</h2> Defining per-annotation attributes for a task helps you extract additional structured information about individual annotations, beyond what a single selected label can provide. The attribute interface can also adapt dynamically to previously-selected attribute choices. For example, only if workers answer “No” to “Is the car currently parked?” will they be asked “ Which direction is the car heading?” If they answer “yes” to “Is the car currently parked?” the interface will skip the question “Which direction is the car heading?”<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ee91d053-5b42-42f4-32e8-a819c3849300/public" alt="Dashboard selection"> <h2>Label Hierarchies</h2> Nesting labels can also help you extract additional structured information from your data. By default, 2D annotation tasks sent to Scale have a limit of 7 flat labels to reduce the cognitive load on our human labelers and keep their accuracy high. But using subchoices, a worker can, for example, first categorize an object as pedestrian or vehicle, and based on that choice select the specific type of pedestrian or vehicle.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/2a97419c-f9e2-492b-c46d-34c0e8528a00/public" alt="Dashboard selection"> Ready to get started? Take a look at our <a href="https://docs.scale.ai/#annotation-attributes" rel="noopener noreferrer" target="_blank">Annotation Attributes</a> documentation for how to include attributes in your tasks, and for how to use attribute conditions to dynamically request specific attributes. Or see our <a href="https://docs.scale.ai/#nested-annotation-labels" rel="noopener noreferrer" target="_blank">Nested Labels documentation</a> to learn how to specify large lists of labels on annotation tasks.

At Scale, we pride ourselves in developing superior tooling and implementing forward-looking solutions for problems our customers.

Per-Annotation Attributes & Labelling for Image Annotations

Metadata to Enrich Image Annotations

At Scale, we are excited to help you solve your most challenging computer vision problems. One important and well-studied problem within computer vision is that of semantic segmentation, which aims to understand images at the pixel level. More precisely, in semantic segmentation, we want to understand which semantic class each pixel of an image belongs to. Below we show a street scene, semantically segmented into a small set of class labels including vehicles, pedestrians, and road markings:<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/b3f2baa7-c352-4499-b4db-f024cd404a00/public">A street image<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/39d76976-0384-47be-705e-55b47e10ee00/public">The image segmented into semantic classes Supervised approaches trained on large datasets of pixel-wise annotations are currently the best at predicting semantic classes in several public benchmarks and challenges. Unfortunately, obtaining accurate pixel-wise training data can be incredibly time-consuming; each pixel of an image must be manually assigned a label by a human, usually through the use of Photoshop-like tools. To provide our customers with the fastest turnaround times for semantic segmentation, Scale has added a “Guided Automatic Segmentation” feature to our segment annotation tool. We hope to make the process of creating pixel annotations faster and easier by allowing our labelers to provide only a few points of a segment and automatically filling in the rest. Obtaining segment annotations by clicking a few key boundary points! <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9971da18-0d5a-4387-d732-2a126f343900/public"> Segmenting a car with four extremal points as human input. <img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e132db34-0b30-4d48-982d-af66f1afd100/public"> road label with four extremal points as human input. Our new semi-supervised tool allows us to empower our labelers with insights from current data-driven methods. Instead of attempting to segment the entire image from scratch, our labelers must only click on at least four extremal points of any object. By reducing our area of interest from the whole picture to a smaller single-class semantic region, we can use the convolutional features from the image data augmented by the chosen extremal points to create a proposed pixel map, which can then be further fine-tuned at the labeler’s discretion. Our Guided Automatic Segmentation tool ultimately allows our workers to label pixel-wise annotations much faster with higher quality, so we can continue to label all of your semantic segmentation data at best-in-class accuracy and cost. We are excited to continue exploring other ML techniques to improve all of our APIs -- stay tuned for more blog posts coming soon!

AI-Assisted Semantic Segmentation Labeling

Introducing AI-Assisted Semantic Segmentation at Scale

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