Deep Vision AI
Advanced computer vision technology recognises and analyses photographs and videos automatically, converting visual input into real-time analytics and actionable insights. For cutting-edge safety and security, the Deep Vision AI platform offers facial recognition and vehicle identification analytics, as well as business intelligence solutions. A team of computer vision and ML experts includes ImageNet researchers with strong technical skills and a passion for innovation. The solution is camera agnostic, which can work with both old and new camera systems. With well-tuned AI models and 15 streams per GPU, this is a cost-effective solution. All deployment possibilities are supported, including cloud, on-premise, edge-based, and hybrid. New bespoke software modules are built on-demand to meet unique client needs. Compliance with the General Data Protection Regulation (GDPR) is possible.
Deep Vision's Vehicle Recognition Model From every perspective, AI can count and recognize the year, make, model, and license plates of automobiles. Vehicle identification is used by governments and municipalities to automatically assess vehicle flows and give law enforcement notifications about specific vehicles. The system may be used to extract demographic information from vehicle recognition, measure vehicle flow and analyse changing traffic patterns, among other things. Advertisers and enterprises may use this data to target contextualised ads based on shifting demographics and measure outdoor advertising return on investment. Types: Vehicle Analytics - Counting Vehicles - Recognizing Year, Make, and Model - Recognising License Plates (Vehicle Inspection Container).
In deep learning, GPU acceleration is essential for both training and inference. Among other areas, NVIDIA supplies GPU acceleration to data centres, PCs, laptops, and the world's fastest supercomputers. If your data is on the cloud, NVIDIA GPU deep learning is available on Amazon, Google, IBM, Microsoft, and many more cloud providers.
The computing world is experiencing major upheaval. Thanks to deep learning and AI, computers are learning to design their software.
Deep learning differs from traditional machine learning techniques. It can learn representations from data such as pictures, video, or text without having humans write hand-coded rules or have domain expertise. Their extremely adaptive systems may be able to learn straight from raw data and increase forecast accuracy as more data becomes available.
Deep learning is frequently used in computer vision, conversational AI, and recommendation systems. Computer vision programs employ deep learning for extracting information from digital photographs and movies—conversational Artificial Intelligence apps aid computers in understanding and communicating in NPL. Request systems employ images, language, and users' interests to give meaningful and relevant search results and services.
Deep learning has paved the path for a host of recent AI breakthroughs, including AlphaGo from Google DeepMind, self-driving cars, intelligent voice assistants, and a bevvy of others.
Researchers and data scientists may cut deep learning training time from days to weeks to hours or days using NVIDIA GPU-accelerated deep learning frameworks. When models are ready for deployment, GPU-accelerated inference systems for the cloud, embedded devices, and self-driving cars can provide high-performance, low-latency inference for even the most computationally expensive deep neural networks.
Performance Engineered Deep Learning Framework Containers
NVIDIA GPU Cloud (NGC) gives you access to the most popular deep learning frameworks for building and training neural network models, such as TensorFlow, PyTorch, and MXNet, as well as the necessary libraries and drivers. The NVIDIA Volta GPU architecture, available in ready-to-run containers for the cloud or on-premises with NVIDIA DGX Systems, is ideally suited for this integrated stack.
The initial stage in building AI applications is to train deep neural networks using large datasets. Deep learning frameworks with GPU acceleration allow you to build and help in training deep neural custom networks while also giving interfaces to common programming languages like Python.
TensorFlow, PyTorch, and other key deep learning frameworks are GPU-accelerated out of the box, allowing data scientists and researchers to get up and running in minutes with no GPU programming necessary. The Deep Learning SDK includes high-performance libraries that define building block APIs for embedding training and inference directly into apps, allowing developers to integrate deep neural networks into cloud-based or embedded applications. Thanks to a single programming environment, developers can start working on their desktop, scale up to the cloud, and deliver to their edge devices with little to no code changes.
The NVIDIA Deep Learning Institute (DLI) offers developers, data scientists, and academics hands-on AI and accelerated computing training. You may get certified in the fundamentals of computer vision by taking this hands-on, self-paced online course. Two-hour electives are offered in Digital Content Creation, Healthcare, and Intelligent Video Analytics.
How to get started?
The E2E Cloud is created to speed up different deep learning-based image processing and segmentation algorithms. For real-time deep learning inference, NVIDIA T4 instances are suggested, while NVIDIA V100 instances are recommended for model training.