Viso Suite
Evaluation Guide

Can I Collect Video Data for Training and Testing?

Table of Contents

The Viso platform allows recording videos for computer vision image annotation, using the same cameras and edge devices used to run the deep learning application after training.
Record video data for computer vision training

Can I use recorded video files?

Yes. Viso Suite provides functionality to process video recordings with deep learning methods. Therefore, video files can be used to simulate the video stream of a camera (camera virtualization). Viso Suite provides a built-in video data management system to upload and integrate videos that can be used in the application builder for simulating video input.

The ability to apply machine learning to a looped video is particularly useful in testing and prototyping. Video file input can be used for testing real-time applications before switching to a physical camera – with one click.

Can I record video files with Viso Suite?

Yes. Viso allows collecting video data in distributed systems with built-in data collection capabilities. You can use collected data for image annotation and model training, all within Viso Suite.

The platform allows collecting data with the same cameras used to run the application after training. This facilitates the video recording process and increases model performance, while only small datasets are needed for deep learning model training.

Who Can Develop With Viso Suite?

Anyone who understands development or computer vision can use Viso Suite, from business analysts with little programming experience to expert developers and anyone in-between.

Why Viso Suite?

Viso Suite is the only end-to-end computer vision application platform to build, deploy, scale, and operate real-world AI vision applications.

What Computer Vision Tasks Can Viso Suite Be Used For?

Most computer vision systems are based on a combination of different techniques. Viso Suite provides extensive no-code and low-code capabilities to combine image recognition methods, traditional image processing, and deep neural networks.