Computer Vision
at the Edge

Speed up your computer vision development and reduce resource requirements with ultra low-power computer vision on edge devices.

Object detection in an industrial warehouse identifying people and stacked boxes—demonstrating low-power computer vision performance on edge devices with Edge Impulse.

Develop Real-world Computer Vision Applications for the Edge

Build, optimize, and deploy AI models on any edge system, from industrial gateways to ultra low-power cameras

Automate Data Tasks

Accelerate and automate data collection, labeling, and validation without manual intervention.

Adapt Fast

Iterate quickly and adapt to changing needs and environments.

Boost profit margins

Deploy AI to cost-efficient and ultra low-power cameras.

Long Battery Life

Achieve long battery life (up to 10 years depending on use case).

Save Time with Automated Data Tasks

Synthetic image data

Augment your datasets by leveraging the latest foundational models from OpenAI and Hugging Face to generate photorealistic images all from within the Edge Impulse platform.

Edge Impulse synthetic data interface using Hugging Face models to generate photorealistic images of valves for training AI models with labeled datasets
Side-by-side comparison of real and synthetic pallet detection images—demonstrating how NVIDIA Omniverse Replicator and Edge Impulse simulate real-world environments with photorealistic accuracy.

Simulate the real world

Generate images from a photorealistic simulation leveraging the power of Omniverse Replicator, that is seamlessly integrated with Edge Impulse.

Automate your image data labeling with GenAI

Optimize your labeling workflow with the help of the latest AI models from Hugging Face, Open AI as well as your previously trained models. Avoid tedious and time consuming manual labeling with AI Labeling to automatically inspect, label and validate your dataset in record time, through a powerful and flexible workflow builder.

Edge Impulse AI Labeling interface using OWL-ViT to automatically detect and label capsules in images—streamlining annotation with GenAI-powered bounding boxes.

Latest Edge AI Vision Models

FOMO (Faster Objects, More Objects)

A powerful object detection model that can run on constrained devices, designed from the ground up for the edge, and is able to achieve better performance than MobileNet SSD and to run in <200K of RAM.

FOMO-AD

A set of visual anomaly detection models designed to scale and be optimized to run efficiently on any edge device from constrained microcontrollers to powerful accelerators. Build models without the need to collect anomaly data, especially for unanticipated defects.

YOLO-Pro

An object detection model trained with extensive industrial datasets for optimal performance in real-world industrial scenarios. Optimized and available in various sizes to run efficiently on edge devices from CPUs to accelerators and GPUs.

Unlock Edge AI Experimentation

Edge Impulse enables teams to develop and deploy edge AI models that deliver meaningful business insights.

Experiments

Accelerate your project development by experimenting with different ML models simultaneously.

Edge Impulse ‘Experiments’ interface showing multiple AI models side-by-side with accuracy, latency, and memory metrics—enabling fast iteration and comparison.
EON Tuner

Find and select the best computer vision model for your application within the constraints of your target device. The EON Tuner analyzes your data and all the possible neural network architectures - and gives you an overview of the optimal set of models that will fit your chosen device's latency and memory requirements.

Edge Impulse EON Tuner interface showing model performance breakdown—latency, memory usage, and accuracy—helping users select the best-fit computer vision model for edge deployment.
Optimization and Deployment

Everything you develop will be able to be optimized and run on the largest variety of devices, thanks to our EON Compiler and our flexible deployment options.

Edge Impulse deployment diagram showcasing compatibility with frameworks like TensorFlow, PyTorch, Docker, and C++, optimized through the EON Compiler for use on cameras and gateways.

Use Computer Vision on the Edge for

Visual Inspection
Asset Tracking
Optical Character Recognition
Vehicle recognition
Anomaly Detection
Predictive maintenance
Safety & Security
Visual Inspection
Asset Tracking
Optical Character Recognition
Vehicle recognition
Anomaly Detection
Predictive maintenance
Safety & Security
Visual Inspection
Asset Tracking
Optical Character Recognition
Vehicle recognition
Anomaly Detection
Predictive maintenance
Safety & Security

Start using Edge Impulse for Computer Vision applications

Reach out to our experts to learn how Edge Impulse can give you the competitive edge.

Edge Impulse dashboard displaying labeled capsule images for computer vision—enabling easy dataset management, visualization, and annotation for AI training.