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](./project/android/apps/MnnLlmApp/README.md)
💎1MB lightweight face detection model (1MB轻�级人脸检测模型)
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
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 …
🛠A lite C++ toolkit of 100+ Awesome AI models, support ORT, MNN, NCNN, TNN and TensorRT. 🎉🎉
���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~
MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB:fire::fire::fire:
llm deploy project based mnn. This project has merged into MNN.
Sharpen your low-resolution pictures with the power of AI upscaling
AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集æˆ?è¿?行时环境 (IRE)，帮助开å?‘者æ??å?‡æ•ˆçŽ‡ã€‚
nndeploy is an end-to-end model deployment framework. Based on multi-terminal inference and directed acyclic graph model deployment, it is committed to providing users with a cross-platform, easy-to-use, and high-performance model deployment experience.
A toolbox for deep learning model deployment using C++ YoloX | YoloV7 | YoloV8 | Gan | OCR | MobileVit | Scrfd | MobileSAM | StableDiffusion
an edge-real-time anchor-free object detector with decent performance
C++ Helper Class for Deep Learning Inference Frameworks: TensorFlow Lite, TensorRT, OpenCV, OpenVINO, ncnn, MNN, SNPE, Arm NN, NNabla, ONNX Runtime, LibTorch, TensorFlow
alibaba MNN, mobilenet classifier, centerface detecter, ultraface detecter, pfld landmarker and zqlandmarker, mobilefacenet
benchmark for embededded-ai deep learning inference engines, such as NCNN / TNN / MNN / TensorFlow Lite etc.
在Android使用深度å¦ä¹ 模型实现图åƒ?识别，本项目æ??供了多ç§?使用方å¼?，使用到的框架如下：Tensorflow Liteã€?Paddle Liteã€?MNNã€?TNN
Raspberry Pi 4 Buster 64-bit OS with deep learning examples
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