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YOLOv10 Training and Real-Time Data Storage

· 7 min read
Anthony Cavin
Data Scientist - ML/AI, Python, TypeScript

Block Diagram

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.

ReductStore v1.12.0 released: record deletion API and storage engine optimization

· 3 min read
Alexey Timin
Software Engineer - Database, Rust, C++

We are pleased to announce the release of the latest minor version of ReductStore, 1.12.0. ReductStore is a time series database designed for storing and managing large amounts of blob data.

To download the latest released version, please visit our Download Page.

What's new in 1.12.0?

Over the last few months we've been working hard to make ReductStore even more powerful and efficient as a central repository for your time series data. Where you can collect data from a variety of sources, including IoT and edge devices, and store it in one place for further analysis and processing.

In this release, we've added a new record delete API that allows you to remove specific records from an entry. This can be useful if you want to clean up your data or remove obsolete records and need more flexibility than FIFO bucket quotas. We have also optimised the storage engine to improve overall performance when reading and writing data.

How to Keep a History of MQTT Data With Python

· 7 min read
Alexey Timin
Software Engineer - Database, Rust, C++

MQTT+ReductStore in Python

The MQTT protocol is an easy way to connect disparate data sources to applications, making it very popular for IoT (Internet of Things) applications. Some MQTT brokers can store messages for a while, even when the MQTT client is offline. However, sometimes you need to keep this data for a longer period of time. In these cases it's a good idea to use a time series database.

There are many time series databases available, but if you need to store a history of images, sensor data or protobuf messages, you might want to use ReductStore. This database is designed to store a lot of blob data and works well with IoT and edge computing.

ReductStore has client SDKs (software development kits) for many programming languages. This means you can easily use it in your existing system. For this example, we'll use the Python SDK from ReductStore.

Let's create a simple MQTT application to see how it all works.