🍅 Deploy ncnn on mobile phones. Support Android and iOS. 移动端ncnn部署，支持Android与iOS。

YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming), Quantization (MQBench) and Deployment (TensorRT, ncnn) Compression Tool Box.

YOLOv3、YOLOv4、YOLOv5、YOLOv5-Lite、YOLOv6-v1、YOLOv6-v2、YOLOv7、YOLOX、YOLOX-Lite、PP-YOLOE、PP-PicoDet-Plus、YOLO-Fastest v2、FastestDet、YOLOv5-SPD、TensorRT、NCNN、Tengine、OpenVINO

yolort is a runtime stack for yolov5 on specialized accelerators such as tensorrt, libtorch, onnxruntime, tvm and ncnn.

The YOLOv5 object detection android example

YOLOv5 C++ Implementation on Android using NCNN framework

Deploy YOLOv5 in your web browser with ncnn and webassembly

ncnn of yolov5 v5.0 branch

c++ version of yolov5 segmentation with ncnn

cv_research项目-移动端NCNN部署，项目支持YOLOv5s等模型，摄像头实时捕获视频流进行检测，也可以上传图片或者视频进行检测！

A Android Library for YOLOv5/YOLOv7/YOLOv8 Detection and Pose Inference Based on NCNN

YoloV5 for a bare Raspberry Pi 4

YoloV5 for Jetson Nano

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An ncnn implementation of YOLOv5 on ARM devices, capable of using GPU to accelerate inference

基于NCNN前向推理的Android代码工程，实现基于图片和实时摄像头流（视频）的目标检测，模型包含YoloV5、YoloV7、YoloV8、YoloV10、YoloV11、rtmdet等NCNN模型文件，已包含opencv、ncnn依赖包，下载后在Android studio上安装依赖环境后可直接运行！如果感觉有所帮忙，望点击star⭐！

Depoly yolov5.ncnn in android.

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yolov5s_ncnn_inference pipeline

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

An ncnn-vulkan implementation of YOLOv5, capable of using GPU to accelerate inference

使用yolov5和ncnn，将其部署到安卓移动端，我提供了精心挑选的相互匹配的版本，不会报错，为您节省了许多时间

ncnn, android, and Yolov5

Implemented communication across languages. (From C++ to Java). Largely based on https://github.com/nihui/ncnn-android-nanodet/

yolov5_ncnn in ununtu16.04

基于ncnn的yolov5 部署，提供了一个单文件编译例程， 实现了调用摄像头实现目标检测

YoloV5 face detection on Raspberry Pi 4

基于ncnn部署yolov5s(7.0)手势检测模型的安卓demo，支持hagrid数据集18类手势识别

YoloDeploy aims to deploy Yolo-series models, including Yolov3, YoloV4, Yolov5, etc. The deployment methods include Pytorch, Libtorch, OpenCV DNN, TensorRT, OpenVino, ncnn, darknet and so on.

use ncnn to run yolov8 model and yolov5 model