YOLO26N 轻量化模型移动端与嵌入式部署指南1. YOLO26N 模型规格YOLO26N 核心参数 ├── 参数量2.6M ├── FLOPs5.1G ├── mAP50-9538.5COCO ├── 输入尺寸640x640 ├── 模型文件~5MBFP16/ ~3MBINT8 └── 推理速度 ├── RTX 40901.2ms ├── Jetson Orin NX4.5msFP16/ 3.2msINT8 ├── RK3588 NPU8msINT8 └── 手机 CPU15ms2. 模型导出#!/usr/bin/env python3export_yolo26n.py - YOLO26N 多格式导出fromultralyticsimportYOLO modelYOLO(yolo26n.pt)# ONNX通用model.export(formatonnx,imgsz640,opset11,simplifyTrue)# TensorRTNVIDIA GPUmodel.export(formatengine,imgsz640,halfTrue,batch1)# CoreMLiOSmodel.export(formatcoreml,imgsz640,halfTrue)# TFLiteAndroidmodel.export(formattflite,imgsz640,int8True)# NCNN移动端通用model.export(formatncnn,imgsz640)# OpenVINOIntelmodel.export(formatopenvino,imgsz640,halfTrue)3. Android 部署TFLite// YOLO26NDetector.ktclassYOLO26NDetector(context:Context){privatevarinterpreter:Interpreter?nullprivatevalinputSize640privatevalnumClasses80init{valmodelloadModelFile(context,yolo26n_int8.tflite)valoptionsInterpreter.Options().apply{setNumThreads(4)addDelegate(GpuDelegate())// GPU 加速}interpreterInterpreter(model,options)}fundetect(bitmap:Bitmap):ListDetection{// 预处理valinputpreprocess(bitmap)// 推理valoutputArray(1){FloatArray(84*8400)}interpreter?.run(input,output)// 后处理returnpostprocess(output[0],bitmap.width,bitmap.height)}privatefunpreprocess(bitmap:Bitmap):ByteBuffer{valbufferByteBuffer.allocateDirect(4*inputSize*inputSize*3)buffer.order(ByteOrder.nativeOrder())valresizedBitmap.createScaledBitmap(bitmap,inputSize,inputSize,true)valpixelsIntArray(inputSize*inputSize)resized.getPixels(pixels,0,inputSize,0,0,inputSize,inputSize)for(pixelinpixels){buffer.putFloat((pixelshr16and0xFF)/255f)buffer.putFloat((pixelshr8and0xFF)/255f)buffer.putFloat((pixeland0xFF)/255f)}returnbuffer}}4. iOS 部署CoreML// YOLO26NDetector.swiftimportCoreMLimportVisionclassYOLO26NDetector{privatevarmodel:VNCoreMLModel?init(){guardletmodelURLBundle.main.url(forResource:yolo26n,withExtension:mlmodelc),letmlModeltry?MLModel(contentsOf:modelURL),letvnModeltry?VNCoreMLModel(for:mlModel)else{return}self.modelvnModel}funcdetect(image:UIImage,completion:escaping([Detection])-Void){guardletcgImageimage.cgImageelse{return}letrequestVNCoreMLRequest(model:model!){request,erroringuardletresultsrequest.resultsas?[VNRecognizedObjectObservation]else{return}letdetectionsresults.map{obs-Detectioninletbboxobs.boundingBoxletlabelobs.labels.first!returnDetection(bbox:bbox,confidence:label.confidence,className:label.identifier)}completion(detections)}request.imageCropAndScaleOption.scaleFilllethandlerVNImageRequestHandler(cgImage:cgImage)try?handler.perform([request])}}5. 嵌入式部署NCNN// yolo26n_ncnn.cpp#includencnn/net.h#includencnn/mat.hclassYOLO26NDetector{public:intload(constchar*param_path,constchar*bin_path){net.load_param(param_path);net.load_model(bin_path);return0;}std::vectorDetectiondetect(constcv::Matimage){// 预处理ncnn::Mat inncnn::Mat::from_pixels_resize(image.data,ncnn::Mat::PIXEL_BGR2RGB,image.cols,image.rows,640,640);constfloatnorm_vals[3]{1/255.f,1/255.f,1/255.f};in.substract_mean_normalize(0,norm_vals);// 推理ncnn::Extractor exnet.create_extractor();ex.input(images,in);ncnn::Mat out;ex.extract(output0,out);// 后处理returnpostprocess(out,image.cols,image.rows);}private:ncnn::Net net;};6. 性能基准YOLO26N 各平台性能 ┌──────────────────┬──────────┬──────────┬──────────┐ │ 平台 │ 精度 │ 延迟 │ FPS │ ├──────────────────┼──────────┼──────────┼──────────┤ │ RTX 4090 │ FP16 │ 1.2ms │ 833 │ │ Jetson Orin NX │ FP16 │ 4.5ms │ 222 │ │ Jetson Orin NX │ INT8 │ 3.2ms │ 312 │ │ Jetson Orin Nano │ FP16 │ 8ms │ 125 │ │ RK3588 NPU │ INT8 │ 8ms │ 125 │ │ RK3588 CPU │ FP32 │ 45ms │ 22 │ │ iPhone 15 Pro │ FP16 │ 5ms │ 200 │ │ Pixel 8 Pro │ FP16 │ 12ms │ 83 │ │ Raspberry Pi 5 │ FP32 │ 80ms │ 12 │ └──────────────────┴──────────┴──────────┴──────────┘总结平台推荐格式预期 FPSNVIDIA GPUTensorRT FP16200JetsonTensorRT INT8300AndroidTFLite INT880iOSCoreML FP16200RK3588RKNN INT8120嵌入式NCNN30核心要点YOLO26N 是最轻量的变体仅 2.6M 参数5MB 模型INT8 量化后仅 3MB适合 OTA 更新多框架支持TFLite/CoreML/NCNN/TensorRT 全覆盖实时性能大多数平台 30 FPS