deftruth/lite.ai.toolkit

Lite.ai.toolkit lets you add AI features like object detection, face recognition, and image segmentation to a C++ application without wrestling with the complexity of each model individually. Instead of building everything from scratch, you get a collection of over a hundred pre-trained AI models packaged in a way that is easy to drop into your code. Note that this specific repository has moved to a new home under the xlite-dev organization, but the functionality remains the same. At a high level, the toolkit acts as a bridge between your application and several popular AI "engines" (the software that actually runs the math for the models). You write a few lines of C++ to load a model file, pass it an image, and get results back, like the coordinates of boxes around detected objects. Behind the scenes, the toolkit handles the communication with whichever engine you choose to use, such as ONNXRuntime for general CPU use or TensorRT for high-performance NVIDIA graphics card processing. This project is designed for C++ developers building applications that run directly on hardware, especially where resources are tight or speed is critical. For example, if you are building a security camera system that needs to detect faces on the device itself, or a desktop photo editor that automatically cuts out backgrounds, this toolkit provides the ready-made building blocks. It supports running on Linux, macOS, Windows, and Android. The main tradeoff here is the technical setup. Because it is a C++ library, using it requires familiarity with compiling code and managing dependencies like OpenCV. However, the project minimizes this burden by requiring only OpenCV and ONNXRuntime by default, and it offers prebuilt versions you can download directly. It also allows you to mix and match different inference engines if you need specialized performance, giving developers flexibility without forcing them into a single ecosystem.