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.