Perception Applications
Reference Implementations for common customer applications
Immediate
RESULTS
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Instantly deploy standard or custom AI models on EVK, Maivin or your own hardware.
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Fully compatible with eIQ® Toolkit graphical development environment for no-code model training and deployment.
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Integration with multiple frameworks to deploy your AI application the way you want.
Best in class
PERFORMANCE
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Achieve industry leading performance with Deep ViewRT™ Inference Engine and Deep View™ VisionPack.
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Ship your product with confidence knowing your vision pipeline is fully optimized for AI compute at the edge.
Commercially
SUPPORTED
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Build your EdgeFirst AI solution with confidence.
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Long Term Support and stability.
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Documented code provenance.
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Field proven reliability.
Deep View Middleware Evaluation Trials
Go from idea to concept to production faster and with more confidence using our Deep View AI Middleware. We’ve optimized runtime and model performance so you can focus on your vision application.
Click the link below for signup for an evaluation trial of our Deep View VisionPack and Deep View ModelPack.
Deep View VisionPack - Product Brief
Accelerated vision pipeline for NPU,CPU, GPU
Deep View ModelPack - Product Brief
State of the art detection models
Available
Object Tracking | NPU, GPU, CPU, MCU
Combining detection, classification, and motion tracking, this application is used to identify and monitor objects belonging to one or more classes, such as people, animals, vehicles, or inanimate objects. Depending on the scene complexity, desired frame rate and available compute, tracking is available on both MCU and high-performance applications processor.
In Flight
In Planning
Face & Body Pose | NPU, GPU, CPU, MCU
Detection and classification models allows this application to identify and monitor objects belonging to multiple classes. Depending on the scene complexity, desired frame rate and available compute, face recognition is available on both MCU and high-performance applications processor.
Anomaly Detection | NPU, GPU, CPU,MCU
This application uses flexible combinations of detection and segmentation to find and identify visual anomalies. Includes support for adjustment over time to accommodate anomaly acceptance criteria. By retraining the Deep View model with your dataset the application can be quickly adapted for your specific needs. Depending on complexity and desired frame rate, this application can be deployed to MCU's or applications processor.
Occupancy Tracking | NPU, GPU, CPU, MCU
Enhanced detection algorithms verify the number of people, animals or objects in the region of interest. This application can be combined with face recognition to authenticate users and quantify key metrics. Depending on the room size, number of people, and video frame rate, this application can be built on an MCU or applications processor.
Inventory Management | NPU, GPU, CPU, MCU
Applies tracking algorithms for retail and other consumer packaged goods applications. Bring in your dataset and let's get started!
Coming Soon
Behavior Analysis | NPU, GPU, CPU,MCU
This visual intelligence application is available for several domains, including safety, security, surveillance, and efficiency. It can be applied to the workplace or public areas. Due to real-time performance constraints, this application is built on an applications processor.
Coming Soon
The DeepView AppPack provides you with the building blocks and glue for robust, turn-key intelligent vision applications.
DeepView DevPack delivers production-grade tools to help you optimize your machine learning models and fine-tune your training data sets.
The DeepView Model Pack provides developers with both public (Open Source) and production ready models.
The DeepViewRT run time inference engine provides developers with the freedom to quickly deploy ML models to a broad selection of embedded devices
DeepView Vision Packs provide the vision pipeline solutions for your edge computing and embedded machine learning applications.
DeepView vision starter kits include the hardware and software you need to accelerate your machine learning development programs from bench-top through field trials and into production.