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.

When it comes to computer vision, data storage is a critical component. You need to be able to store images for model training, as well as the results of the processing for model validation. There are a few ways to go about this, each with its own advantages and disadvantages. In this post, we’ll take a look at three different ways to store data in computer vision applications: a file system, an S3-like object storage and ReductStore. We’ll also discuss some of the pros and cons of each option.

A Simple Computer Vision Application​

For demonstration purposes, we’ll use a simple computer vision application which is connected to a CV camera and runs on an edge device:

The camera driver captures images from the CV camera every second and forwards them to the model, which then detects objects and displays the results in the user interface.

Your images and results need to be stored for training and validation purposes. The customer may also wish to view images featuring anomalous objects. These requirements present the challenge of maintaining a history of blob or unstructured data.

We are pleased to announce the release of the latest minor version of ReductStore, 1.11.0. 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 ReductStore v1.11.0​

In this release, we have introduced a new API for changing the labels of existing records and optimized the storage engine to improve database startup and write performance.