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🚀 你的YOLO部署神器。TensorRT Plugin、CUDA Kernel、CUDA Graphs三管齐下,享受闪电般的推理速度。| Your YOLO Deployment Powerhouse. With the synergy of TensorRT Plugins, CUDA Kernels, and CUDA Graphs, experience lightning-fast inference speeds.
Detect and identify different species of harmful algae within natural water in real-time with AI and a camera (i.e., ESP32-CAM, smartphone, or webcam).
Lightweight inference library for ONNX files, written in C++. It can run Stable Diffusion XL 1.0 on a RPI Zero 2 (or in 298MB of RAM) but also Mistral 7B on desktops and servers. ARM, x86, WASM, RISC-V supported. Accelerated by XNNPACK.
⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
Sistema de asistencia a la conducción que integra algoritmos en MATLAB y Simulink con datos de un simulador de realidad virtual (Unreal Engine y Airsim). Utiliza aprendizaje profundo para reconocer señales de tráfico y límites de la calzada, proporcionando retroalimentación en tiempo real a través de una interfaz hombre-máquina (HMI).
基于官方yolov8的onnxruntime的cpp例子修改,目前已经支持图像分类、目标检测、实例分割。Based on the cpp example modification of official yolov8's onnxruntime, it currently supports image classification, target detection, and instance segmentation.