一款简单易用和高性能的AI部署框架 | An Easy-to-Use and High-Performance AI Deployment Framework

🛠A lite C++ AI toolkit: 100+ models with MNN, ORT and TRT, including Det, Seg, Stable-Diffusion, Face-Fusion, etc.🎉

An Android application for super-resolution & interpolation. Contains RealSR-NCNN, SRMD-NCNN, RealCUGAN-NCNN, Real-ESRGAN-NCNN, Waifu2x-NCNN, Anime4kcpp, nearest, bilinear, bicubic, AVIR...

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./apps/Android/MnnLlmChat/README.md). MNN TaoAvatar Android - Local 3D Avatar Intelligence: apps/Android/Mnn3dAvatar/README.md

YOLOv5 C++ inference implemented using multiple frameworks: ncnn, OpenVINO, MNN, ONNXRuntime, and OpenCV.

MNNTaoAvatar项目学习笔记

A third-party MNN server supporting external calls, embedding model, TTS model and ASR model features.一个支持外部调用、向量模型、文字转语音模型和语音识别模型特性的第三方MNN服务器

TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and …

llm deploy project based mnn. This project has merged into MNN.

在Android使用深度学习模型实现图像识别，本项目提供了多种使用方式，使用到的框架如下：Tensorflow Lite、Paddle Lite、MNN、TNN

mnn yolo demos.

基于MNN的猫狗检测项目demo（MNN-based cat and dog detection project demo）

🔥Robust Video Matting C++ inference toolkit with ONNXRuntime、MNN、NCNN and TNN, via lite.ai.toolkit.

🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~

Object classification and detection SDK.

DragGan in NCNN with c++

mediapipe-hand，mediapipe-body，mediapipe-face, mediapipe-embedding, mediapipe-classifier and so on.MNN inference

Stable Diffusion in NCNN with c++, supported txt2img and img2img

fast deployment for yolo detectors

RetinaNet face detection model inference on edge/mobile device utilizing MNN framework.