Accurate Digits Recognition with the Arduino Portenta H7 and Vision Shield

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📚 Main Topics

Embedded Machine Learning PlatformEdge Impulse provides a user-friendly platform for deploying machine learning applications on low-cost, low-power devices.
Digits Recognition ApplicationsThe technology can be applied in grocery retail for price label detection, manufacturing for quality control, utility metering, and more.
Dual-Core ArchitectureThe Arduino Portenta H7 features a dual-core Cortex-M architecture, allowing efficient processing of computer vision tasks.
Data IngestionUsers can easily ingest data from various sources, including smartphones and cloud storage.
Impulse DesignThe process involves generating an impulse for signal processing and machine learning, followed by feature extraction and model training.
Transfer LearningThe model can be trained using transfer learning techniques, specifically with MobileNet V2.
Model ValidationPerformance metrics such as accuracy and inference speed can be analyzed post-training.
Versioning and DeploymentEdge Impulse allows users to track experiments and easily deploy models to embedded targets.

Iterative ProcessDeploying machine learning applications is iterative; tracking changes and versions is crucial for success.
User EmpowermentThe platform is designed to empower users to create and deploy machine learning models without extensive programming knowledge.
Real-World ApplicationsUnderstanding the practical applications of digits recognition can inspire innovative solutions in various sectors.

For more information on low-power computer vision applications, visit Edge Impulse.