Anthony Cavin

Deploying a vision model like YOLOv10 at the edge has become a game-changer for real-time object detection. Developed by researchers at Tsinghua University, YOLOv10 introduces architectural innovations that optimizes speed and accuracy, making it ideal for vision tasks that require low inference latency.

This article provides resources for training a YOLOv10 model and managing data storage for real-time performance on edge devices. We will look at a combination of tools, including Roboflow for dataset preparation, Ultralytics for model training, and ReductStore for efficient data storage.

Computer vision is transforming industries by automating decision making based on visual data. From facial recognition to autonomous driving, the need for efficient computer vision solutions is growing rapidly. This article explores how Roboflow combined with ReductStore, a time-series object store optimized for managing continuous data streams, can improve computer vision applications. ReductStore is designed to efficiently handle high-frequency time-series data, such as video streams, making it a perfect fit for storing and retrieving large datasets generated by computer vision tasks.

In How to Store Vibration Sensor Data | Part 1, we discussed the importance of efficiently storing both raw vibration data and pre-processed metrics, and the benefits of using time-series databases such as ReductStore. We explored best practices for setting up a time-series database and implementing data retention policies to effectively manage high-frequency sensor data.

In How to Store Vibration Sensor Data | Part 2, we provided a practical example of how to use ReductStore to store and query vibration sensor readings. We also showed how to store vibration sensor values in 1-second chunks, each packaged as binary data, to optimize the storage process when dealing with high-frequency data such as vibration or acoustic measurements.

In this post, we compare ReductStore and InfluxDB in a real-world benchmark scenario, focusing on their write and read performance for high-frequency sensor data. We show how ReductStore's binary storage provides superior efficiency and scalability over InfluxDB when handling large volumes of unstructured time-series data.

The benchmark was run on an SSD drive, but results may vary depending on hardware configuration and database settings; to explore how it performs on your setup, you can run the benchmark yourself using the Reduct Vibration Example repository on GitHub.