Introduction to lite.ai.toolkit
Overview
lite.ai.toolkit is a versatile C++ toolkit designed to facilitate the deployment and usage of cutting-edge AI models. It encompasses an impressive variety of functionalities, including object detection, face detection, face recognition, image segmentation, and image matting. The toolkit serves as a comprehensive collection for both beginners and experts interested in applying AI in practical scenarios.
Key Features
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User-Friendly Functionality: With simple and consistent syntax like lite::cv::Type::Class, the toolkit makes it easy for developers to integrate AI capabilities into their applications. There are robust examples available to guide users through implementation.
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Minimum Dependencies: The toolkit requires only OpenCV and ONNXRuntime by default, making it easy to set up and deploy. This ensures that users can efficiently build applications without worrying about extensive overhead from additional libraries.
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A Wide Range of Supported Models: Boasting over 300 C++ implementations and 500 AI model weights, lite.ai.toolkit offers extensive resources for various AI tasks. Users can explore the Supported Models Matrix to discover the available models.
Installation and Setup
Users can quickly get started with the toolkit by downloading the prebuilt library or building it from source. The setup and installation process is straightforward and accommodates a variety of systems, enabling seamless integration of AI models.
Quick Start
Here is an example to illustrate how to use lite.ai.toolkit for object detection using YOLOv5:
#include "lite/lite.h"
int main(int argc, char *argv[]) {
std::string onnx_path = "yolov5s.onnx";
std::string test_img_path = "test_yolov5.jpg";
std::string save_img_path = "test_results.jpg";
auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path);
std::vector<lite::types::Boxf> detected_boxes;
cv::Mat img_bgr = cv::imread(test_img_path);
yolov5->detect(img_bgr, detected_boxes);
lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
cv::imwrite(save_img_path, img_bgr);
delete yolov5;
return 0;
}
The user can perform object detection with minimal code and effectively utilize their AI models for robust applications.
Enhancing Performance with TensorRT
For boosted inference performance, particularly leveraging NVIDIA GPU resources, lite.ai.toolkit supports integration with TensorRT. This feature is particularly appealing for users dealing with high-performance AI applications.
Flexibility and Extensibility
The objective of lite.ai.toolkit is to provide a flexible platform where users can integrate models from different runtimes, such as MNN or ONNXRuntime. The toolkit's design allows it to function seamlessly with these engines, affording developers the freedom to build customized AI pipelines.
Supported Models and Platforms
lite.ai.toolkit supports a wide array of models across different platforms, including Linux, Mac OS, and Windows. It embraces CPU inference through ONNXRuntime, MNN, NCNN, and TNN, and supports NVIDIA GPU inference using TensorRT. The toolkit is continuously updated to include new models and technologies, making it a future-proof investment for AI enthusiasts.
In summary, lite.ai.toolkit is an essential toolkit for any AI developer who is keen on integrating complex AI functionalities with minimal overhead. Its simplicity, extensive model support, and ability to run on various platforms make it a valuable asset in the toolkit of AI practitioners.