ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployment and uses on mobile phones from the beginning of design. ncnn does not have third-party dependencies. It is cross-platform and runs faster than all known open-source frameworks on mobile phone cpu. Developers can easily deploy deep learning algorithm models to the mobile platform by using efficient ncnn implementation, creating intelligent APPs, and bringing artificial intelligence to your fingertips. ncnn is currently being used in many Tencent applications, such as QQ, Qzone, WeChat, Pitu, and so on.

ncnn 是ぜ一个为手机端极致优化的高性能神经网络前向计算框架。 ncnn 从设计之初はつ深刻しんこく考こう虑手机つくえ端はし的てき部署ぶしょ和わ使用しよう。 无第三さん方ぽう依よ赖，跨またが平台ひらだい，手て机つくえ端はし cpu 的てき速度そくど快かい于目前ぜん所有しょゆう已やめ知的ちてき开源框かまち架か。 基もと于 ncnn，开发者しゃ能のう够将深度しんど学がく习算法ほう轻松移植いしょく到いた手て机つくえ端はし高だか效こう执行， 开发出で人工じんこう智能ちのう APP，将はた AI 带到你的指ゆび尖とんが。 ncnn 目前もくぜん已やめ在ざい腾讯多た款应用よう中ちゅう使用しよう，如：QQ，Qzone，微ほろ信しんじ，天てん天てん P 图等。

https://github.com/Tencent/ncnn/releases/latest

	how to build ncnn library on Linux / Windows / macOS / Raspberry Pi3, Pi4 / POWER / Android / NVIDIA Jetson / iOS / WebAssembly / AllWinner D1 / Loongson 2K1000
	Source
	Build for Android Build for Termux on Android
	Android
	Android shared
	Build for HarmonyOS with cross-compiling
	HarmonyOS
	HarmonyOS shared
	Build for iOS on macOS with xcode
	iOS
	iOS-Simulator
	Build for macOS
	macOS
	Mac-Catalyst
	watchOS
	watchOS-Simulator
	tvOS
	tvOS-Simulator
	visionOS
	visionOS-Simulator
	Apple xcframework
	Build for Linux / NVIDIA Jetson / Raspberry Pi3, Pi4 / POWER
	Ubuntu 20.04
	Ubuntu 22.04
	Ubuntu 24.04
	Build for Windows x64 using VS2017 Build for Windows x64 using MinGW-w64
	VS2015
	VS2017
	VS2019
	VS2022
	Build for WebAssembly
	WebAssembly
	Build for ARM Cortex-A family with cross-compiling Build for Hisilicon platform with cross-compiling Build for AllWinner D1 Build for Loongson 2K1000 Build for QNX
	Linux (arm)
	Linux (aarch64)
	Linux (mips)
	Linux (mips64)
	Linux (ppc64)
	Linux (riscv64)
	Linux (loongarch64)

• Classical CNN: VGG AlexNet GoogleNet Inception ...
• Practical CNN: ResNet DenseNet SENet FPN ...
• Light-weight CNN: SqueezeNet MobileNetV1 MobileNetV2/V3 ShuffleNetV1 ShuffleNetV2 MNasNet ...
• Face Detection: MTCNN RetinaFace scrfd ...
• Detection: VGG-SSD MobileNet-SSD SqueezeNet-SSD MobileNetV2-SSDLite MobileNetV3-SSDLite ...
• Detection: Faster-RCNN R-FCN ...
• Detection: YOLOv2 YOLOv3 MobileNet-YOLOv3 YOLOv4 YOLOv5 YOLOv7 YOLOX ...
• Detection: NanoDet
• Segmentation: FCN PSPNet UNet YOLACT ...
• Pose Estimation: SimplePose ...

use ncnn with alexnet with detailed steps, recommended for beginners :)

ncnn 组件使用しよう指ゆび北きた alexnet 附ふ带详细步骤，新人しんじん强烈きょうれつ推荐 :)

use netron for ncnn model visualization

use ncnn with pytorch or onnx

ncnn low-level operation api

ncnn param and model file spec

ncnn operation param weight table

how to implement custom layer step by step

ncnn throw error

ncnn produce wrong result

ncnn vulkan

• Supports convolutional neural networks, supports multiple input and multi-branch structure, can calculate part of the branch
• No third-party library dependencies, does not rely on BLAS / NNPACK or any other computing framework
• Pure C++ implementation, cross-platform, supports Android, iOS and so on
• ARM NEON assembly level of careful optimization, calculation speed is extremely high
• Sophisticated memory management and data structure design, very low memory footprint
• Supports multi-core parallel computing acceleration, ARM big.LITTLE CPU scheduling optimization
• Supports GPU acceleration via the next-generation low-overhead Vulkan API
• Extensible model design, supports 8bit quantization and half-precision floating point storage, can import caffe/pytorch/mxnet/onnx/darknet/keras/tensorflow(mlir) models
• Support direct memory zero copy reference load network model
• Can be registered with custom layer implementation and extended
• Well, it is strong, not afraid of being stuffed with 卷まき QvQ

• 支持しじ卷まき积神经网络，支持しじ多た输入和わ多分たぶん支ささえ结构，可か计算部分ぶぶん分ぶん支ささえ
• 无任何なん第だい三さん方ぽう库依赖，不ふ依よ赖 BLAS/NNPACK 等とう计算框かまち架か
• 纯 C++ 实现，跨またが平台ひらだい，支持しじ Android / iOS 等とう
• ARM Neon 汇编级良心こころ优化，计算速度そくど极快
• 精せい细的内ない存そん管理かんり和わ数すう据すえ结构设计，内ない存そん占うらない用よう极低
• 支持しじ多核たかく并行计算加速かそく，ARM big.LITTLE CPU 调度优化
• 支持しじ基もと于全新しん低てい消耗しょうもう的てき Vulkan API GPU 加速かそく
• 可か扩展的てき模型もけい设计，支持しじ 8bit 量りょう化か 和かず半はん精度せいど浮点存そん储，可か导入 caffe/pytorch/mxnet/onnx/darknet/keras/tensorflow(mlir) 模型もけい
• 支持しじ直接ちょくせつ内ない存そん零れい拷贝引用いんよう加か载网络模型がた
• 可か注ちゅう册さつ自じ定てい义层实现并扩展てん
• 恩おん，很强就是了りょう，不ふ怕被塞ふさが卷まき QvQ

• ✅ = known work and runs fast with good optimization
• ✔️ = known work, but speed may not be fast enough
• ❔ = shall work, not confirmed
• / = not applied

• https://github.com/nihui/ncnn-android-squeezenet
• https://github.com/nihui/ncnn-android-styletransfer
• https://github.com/nihui/ncnn-android-mobilenetssd
• https://github.com/moli232777144/mtcnn_ncnn
• https://github.com/nihui/ncnn-android-yolov5
• https://github.com/xiang-wuu/ncnn-android-yolov7
• https://github.com/nihui/ncnn-android-scrfd 🤩
• https://github.com/shaoshengsong/qt_android_ncnn_lib_encrypt_example

• https://github.com/magicse/ncnn-colorization-siggraph17
  

• https://github.com/mizu-bai/ncnn-fortran Call ncnn from Fortran

• https://github.com/k2-fsa/sherpa Use ncnn for real-time speech recognition (i.e., speech-to-text); also support embedded devices and provide mobile Apps (e.g., Android App)

BSD 3 Clause