Computer vision is completely transforming the transportation and navigation industries — from self-driving cars to transportation infrastructure management to navigation aids for the visually impaired. Bring your computer vision models to production faster with a labeling partner committed to industry-leading accuracy.
Accurate labeling of LiDAR and video data enables the recognition and tracking of vehicles, pedestrians, traffic signs, traffic lights, and lane marks. Vehicles must be trained to differentiate between a variety of elements: drivable areas, free space, permanent objects and impermanent objects such as construction cones and road debris.
Sama helps you create a robust quality rubric for your requirements, detect proper object alignment (roll, pitch, yaw), correct face of interest, cuboid size, and keep track of your target objects across all 3D frames with high accuracy. Interpolation between frames increases annotation efficiency, and intensity values help you annotate with a high level of precision. With sensor calibration, fix scenes for faster annotation, and project 3D objects to 2D helper images to facilitate object detection and increase quality.
Autonomous vehicles must adequately understand their environment while accounting for a diverse set of angles, lighting, occlusions, and other variables they can encounter in real-time. Accurate data labeling is required for various conditions, from rain or fog, to day or night.
Sama’s scalable and secure annotation platform provides advanced raster and vector image and video annotation tools for semantic, instance and panoptic scene segmentations. ML Assisted Annotation powered by MICROMODEL technology helps labelers annotate 2-4x more efficiently, while predictably producing 94-98% IOU.
Accurate lane detection — staying between lane lines, changing lanes, or merging onto a highway — is a fundamental building block to ensuring a vehicle can nudge its way into a lane similar to a human driver.
Vector annotation tools (polyline, polygon, and arrows) can be used to make lanes and road markings recognizable to autonomous vehicles. Sama APIs help you seamlessly re-prioritize tasks, provide quality feedback and monitor projects in production.
OEMs and policymakers are uncovering ways that AI can increase the safety of vehicles, from assisted driving applications to fully automated vehicles. Approaches such as driver drowsiness detection and in-cabin monitoring are paving the way for the widespread adoption of AI in transportation and navigation.
Sama helps automotive companies develop all aspects of vehicle safety including in-cabin monitoring for driver drowsiness detection and security. High-quality computer vision training data is the fuel for advanced driver-assistance systems, trained to recognize driver emotions and gestures and detect whether drivers are distracted.
"We have been impressed, not only with their consistent level of high quality, but with their entire approach to training data strategy. To us they are a perfect addition to our work in AI."
MAA is an architecture that allows Sama to expedite the labeling process by drawing from a library of models trained on specific use cases. MAA can be used to generate high-quality pre-labeled annotations, which annotators validate to help them continuously improve over time.
Learn more about finding, collecting or creating the right data set to jump-start your ML development: matching data set size to your developmental phase; the near-infinite edge cases dilemma; defining the right quality rubric for your 3D LiDAR annotations; and who should label your data?
Project Guideline is an early stage research project by Google that explores how on-device machine learning can help people with reduced vision to walk and run for exercise independently. The team has partnered with Sama to help fuel their experimental technology; allowing people who are blind and low vision to use a mobile phone, headphones, and a yellow guideline painted on the ground run without a guide.
We offer the highest quality SLA (>95%), even on the most complex workflows. Our team assists with anything from implementing a robust quality rubric to raising edge cases.
As an ethical AI company and one of the only AI companies with B Corp certification, we have provided economic opportunities for over 54,000 people from underserved and marginalized communities.
ML Assisted annotation created up to 3-4x efficiency improvement for a single class annotation. We quickly adapt to ramp-ups, focus shifts, and edge cases, with change frequency of up to 2-3x/week per workflow.
From our tech stack to our physical space, security and trust are paramount to everything we do. Our delivery sites are ISO certified and employ biometric security. Our platform boasts 99.95% - 99.99% uptime.