Recent advances in technology have greatly broadened the scope of object detection and related computer vision (CV) services. Hardware with advanced features paired with smarter neural networks has attracted developers and data scientists from numerous industries to start leveraging computer vision to solve complex business challenges. Combined with the rising popularity of embedded devices capturing data on the edge, computer vision on a grand scale has been exploding with seemingly endless potential to revolutionize the way the world collects and analyzes real-world data.
Developers have several libraries available to them to make the process of building and deploying computer vision simpler and more effective. Most of these libraries are written in C/C++, ensuring a fast execution. In most cases, however, there is a Python API that wraps the C++ implementation. This is because Python has become the go-to language for prototyping and developing deep neural networks. An extremely popular and versatile language, Python enables interactive development, and its rich set of libraries and frameworks accelerates the prototyping of complex software. Many Python implementations run a fair bit slower than native C++, hence when speed is an issue, C++ is typically preferred over Python.
Computer vision-based deep learning projects can seem like a completely different kind of animal compared to what you and your development team have tackled in the past. Even though it is an emerging technology, the software development cycle remains relatively similar to many projects you may be more used to.
In this tutorial, we will show you the steps needed to boost the performance of your edge device. You will need a hardware accelerator that is supported by alwaysAI - like Intel’s Neural Compute Stick 2. We'll have more information on hardware accelerators in our documentation which will be publicly available soon. Until then, to get access to our docs.
In this tutorial, we will show you the steps needed to change the computer vision model in the alwaysAI application. Your development computer and edge device (if you're using one) should be set up based on the instructions on our dashboard. You should also have an app running like this object detector starter app. Finally, you should have a Terminal window open.
In this tutorial, we will show you the steps needed to get a real-time object detector starter app up and running quickly and easily on an edge device. You should have already set up your development computer and installed the alwaysAI dependencies in CLI. For more information on system requirements and supported boards, check out our Docs.
alwaysAI provides a platform to deploy computer-vision applications onto edge devices.Learn More