How to Train and Deploy YOLOv10 with Intel Emerald Rapids
Blog post from Roboflow
YOLOv10, a cutting-edge computer vision model for object detection developed by researchers at Tsinghua University, was released in May 2024 and offers improved accuracy and reduced latency compared to previous iterations, making it suitable for fast-paced applications. The guide details the process of training and deploying a YOLOv10 model on Intel's Emerald Rapids CPU using Google Cloud Platform, leveraging Roboflow for dataset annotation, labeling, and preprocessing. It outlines the creation of a dataset version, exporting for model training, and provisioning a C4 instance on Google Cloud. After setting up the server, the guide walks through the training process using YOLOv10's codebase and deploying the model with Roboflow Inference, which supports high-performance commercial applications. The guide also includes benchmarking results showing that the Intel Emerald Rapids system offers superior performance per dollar compared to other tested configurations, emphasizing its cost-effectiveness and speed for computer vision tasks without needing GPU upgrades.