How To Optimize Computer Vision Models For Edge Devices — Picsellia

This article will introduce you to “Inference at the Edge” and “Inference Optimization Techniques” in Machine Learning. By the end of this article, you will know about the most relevant optimization techniques for decreasing model size and increasing its inference speed in computer vision. You will also learn about the open-source tools you can use to achieve these optimizations. Last but not least, we will advise you on the deployment optimization techniques you should use in your computer vision projects, depending on your deployment hardware. You may have heard the phrases “AI at the Edge”, “Edge ML” or “Inference at the edge”. These terms refer to trained machine learning models running inference tasks near the production data collection point, usually in real-time. The inference is executed on edge devices (e.g.microcomputers, accelerators, mobiles, IoT). A typical example is self-driving cars. They gather information about the surrounding area through multiple sensors and process

Deploy to edge in minutes Optimize and ship models to edge devices with auto-scaling infrastructure. Start Free Trial Schedule Demo No credit card required 14-day free trial This article will introduce you to “Inference at the Edge” and “Inference Optimization Techniques” in Machine Learning. By the end of this article, you will know about the most relevant optimization techniques for decreasing model size and increasing its inference speed in computer vision. You will also learn about the open-source tools* you can use to achieve these optimizations. Last but not least, we will advise you on

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