The alwaysAI Blog

Introduction to Computer Vision Model Training

Training a computer vision model is one component of a complex and iterative undertaking, which can often seem daunting. At alwaysAI we want to make the process simple and approachable. To get you started, we have compiled a general overview of the training process of Deep Neural Networks (DNNs) for use in computer vision applications. We will focus on supervised learning in this overview, which uses labeled training data to teach the model what the desired output is. This article provides...

Deploying with Balena

This short guide will show you how to combine alwaysAI and Balena to easily deploy a computer vision application to multiple devices with a single command.

Transform Your Business with Computer Vision

Computer vision (CV) is a huge part of Industry 4.0 and the changing technological landscape as we know it. Computer vision will allow deeper, more impactful insights into businesses in all sectors. Healthcare providers will be able to more quickly and safely diagnose and treat patients. Manufacturing operations will have enhanced security and productivity. Companies looking for more security while operating virtually, can use computer vision to keep track of their assets, and assure the...

Using a Computer Vision Classifier to Sort Images

If you have a host of images that you’d like to sort based on the presence of particular things (like people, cars, buildings, etc.), using computer vision classifiers can make this a pretty simple and fast thing to accomplish.

How to Integrate alwaysAI with External Applications Using TCP Sockets

Sockets are endpoints for inter-process communication over the network, which is supported by most platforms. Using sockets with the alwaysAI platform allows an application to communicate with external applications running locally or externally, as well as with applications written in different programming languages. There are many methods for inter-process communication, but cross-platform communication is handled best by sockets.

How to Detect Pedestrians and Bicyclists in a Cityscape Video

Detecting pedestrians and bicyclists in a cityscape scene is a crucial part of autonomous driving applications. Autonomous vehicles need to determine how far away pedestrians and bicyclists are, as well as what their intentions are. A simple way to detect people and bicycles is to use Object Detection. However, in this case we need much more detailed information about the exact locations of the pedestrians and bicyclists than Object Detection can provide, so we’ll use a technique called...

alwaysAI Technical Requirements Overview

The developer platform at alwaysAI fast-tracks the creation and deployment of computer vision applications on edge devices, making it easy for you to get started building applications in the field of computer vision.

Finding the Right Model with the New alwaysAI Model Catalog

With the new alwaysAI model catalog, you can now search for a computer vision model by specific criteria; for instance, you can search by label to ensure that the model includes a label for the type of entity that your application needs to be able to recognize. You can also search for a model that is compatible with the kind of service that you want your application to provide (object detection, image classification, or pose estimation), and then further refine your search with filters for...

How We Built a Conference Booth Tracking App

alwaysAI offers a number of starter apps that make it easy to quickly deploy computer vision (CV) based applications. In this demo, I'm going to show you how to extend one of these starter apps and, hopefully, provide some insight about how you can create your own custom CV apps. The app we're going to end up with is meant to be used on a conference booth, to track the number of attendees who stop by and provide some basic metrics on how much time they spend at the booth.

How To Change Computer Vision Models in the alwaysAI Platform

In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. First, set up your development computer and edge device (if you're using one). You should also have an app running like this object detector starter app. You can see more about setting up projects and the alwaysAI workflow here. Finally, you should have a Terminal window open.

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