Learn how to get started with machine learning and computer vision
Discover how to harness the power of ml-agents, Unity Computer Vision, robotics simulation, and more.
Accelerating Computer Vision Training with Synthetic Data
Aggregating data is a significant barrier to machine learning training. Real-world data not only has to be collected, but it also has to be hand-labeled and annotated before it can be used for Computer Vision training. Synthetic data is an excellent machine-generated alternative that comes pre-labeled and annotated. Unity Computer Vision lets you generate synthetic data at scale for your computer vision projects.
Standard Cognition works on building autonomous checkout solutions based on computer vision. The cost of developing computer vision algorithms is very high – due to the costs associated with data collection and labeling. Watch Mattia and Tushar talk about how Standard Cognition has used Unity to reduce algorithm development’s financial cost and time.
Computer Vision developers consistently face challenges regarding the cost, bias, and quality of labeled datasets for machine learning training. Unity’s real-time 3D platform can help overcome these challenges by creating synthetic datasets based on virtualized 3D environments and simulated sensors. In this session, you will learn how Unity Computer Vision tools can bring your applications to production quickly and efficiently.
Synthetic Data: A Scalable Way to Train Perception Systems
Visual understanding is a crucial component of a growing number of automated systems. This understanding extends beyond simple object recognition and into complex areas like semantic segmentation, image classification, and object detection. With one glance at an image, humans can effortlessly imagine the world beyond simple pixels. This is a tremendously difficult task for today’s vision systems, requiring higher-order cognition of the world around them.
Training Digital Avatars in the Metaverse with ML Agents
Unity Machine Learning (ML) Agents allow you to study complex behaviors with realistic physics baked into your simulations. With ML-Agents, enterprises and industrial institutions can implement large-scale parallel training regimes for robotics, autonomous vehicles, drones, digital avatars and more. This session will see how you can use machine learning to train a digital avatar to capture a flag.
Join Kel Guerin, PhD, Co-Founder and Chief Innovation Officer with READY Robotics, and see what he had to say about building educational tools for learning to use robots with Unity and Forge/OS.
Neural Pocket: Boosting computer vision performance with synthetic data
Training computer vision models for production-level quality is no small feat. It requires teams of data scientists to collect and painstakingly annotate real-world data, a time- and resource-intensive process. Neural Pocket is leveraging synthetic datasets generated using Unity Computer Vision to overcome these challenges for computer vision tasks.
