《计算平台部署与测试（双语）》课件-项目三：AI视觉算法平台部署

UnderstandingDeepLearning深度学习的认知这些场景，你熟悉吗？Areyoufamiliarwiththesescenarios?支撑现代智能系统的核心技术原理是什么？Whatarethecoretechnologicalprinciplesthatsupportmodernintelligentsystems?深度学习：基于深层神经网络的特征学习DeepLearning:FeatureLearningBasedonDeepNeuralNetworks技术特征：TechnicalFeatures机器学习技术的重要分支AnImportantBranchofMachineLearningTechnology基于多层神经网络架构BasedonMulti-LayerNeuralNetworkArchitecture具备端到端特征学习能力CapableofEnd-to-EndFeatureLearning支持大规模数据分布式处理SupportsLarge-ScaleDistributedDataProcessingInputLayerHiddenLayerOutputLayer人工智能技术体系架构ArtificialIntelligenceTechnologyArchitecture深度学习-核心技术创新领域DeepLearning-Core-TechnologicalInnovationAreas机器学习-中层技术实现路径MachineLearning-Mid-LevelTechnologicalImplementationPath人工智能-外层技术范畴ArtificialIntelligence-Outer-LayerTechnologicalScope技术定位：深度学习是机器学习技术发展的重要方向，共同构成人工智能的核心技术基础TechnologicalPositioning:Deeplearningisanimportantdirectioninthedevelopmentofmachinelearningtechnologies,formingthecoretechnologicalfoundationofartificialintelligence.深度学习技术演进历程DeepLearningTechnologyEvolutionProcess2012至今1980-19902006-20121950-1970理论奠基阶段-感知机模型提出TheoreticalFoundationStage–ThePerceptronModelProposed算法突破阶段-反向传播算法发展AlgorithmBreakthroughStage–DevelopmentoftheBackpropagationAlgorithm技术复兴阶段-深度信念网络提出TechnologicalRevivalStage–ProposaloftheDeepBeliefNetwork应用爆发阶段-AlexNet突破性成果

ApplicationBoomStage–BreakthroughAchievementsofAlexNet1986年，杰弗里·辛顿与他人在《Nature》上发表论文，阐述了如何利用反向传播算法有效训练神经网络。In1986,GeoffreyHintonandhiscolleaguespublishedapaperinNature,explaininghowtoeffectivelytrainneuralnetworksusingthebackpropagationalgorithm.技术发展的核心驱动要素CoreDrivingFactorsofTechnologicalDevelopment数据资源：互联网时代产生的超大规模训练数据集DataResources:Ultra-large-scaletrainingdatasetsgeneratedintheInternetera计算能力：GPU等专用处理器提供的并行计算架构ComputingPower:ParallelcomputingarchitecturesenabledbyGPUsandotherspecializedprocessors算法创新：网络结构与训练方法的持续优化改进AlgorithmicInnovation:Continuousoptimizationandimprovementofnetworkarchitecturesandtrainingmethods深度学习开发框架技术分析TechnicalAnalysisofDeepLearningDevelopmentFrameworks深度学习技术应用领域分析AnalysisofDeepLearningApplicationAreas总结Summarize深度学习技术将持续推动各行业智能化转型，建立系统化的技术认知是专业学习的重要基础。Deeplearningtechnologywillcontinuetodrivetheintelligenttransformationofvariousindustries.Establishingasystematicunderstandingofthetechnologyservesasanessentialfoundationforprofessionallearning.UnderstandingPyTorchPyTorch的认知人工智能就在我们身边ArtificialIntelligenceIsAllAroundUsPyTorch简介IntroductiontoPyTorchPyTorch是什么：一个开源的深度学习框架WhatIsPyTorch:AnOpen-SourceDeepLearningFramework开发者：FacebookAIResearch（FAIR）Developer:FacebookAIResearch(FAIR)核心特点：动态计算图、易用、社区活跃CoreFeatures:DynamicComputationGraph,EaseofUse,andActiveCommunityPyTorch组成ComponentsofPyTorch0103040502核心组件CoreComponentsTorchTensor：多维数组TorchTensor:MultidimensionalArrayAutograd：自动求导Autograd:AutomaticDifferentiationnn模块：神经网络构建nnModule:NeuralNetworkConstructionoptim模块：优化器optimModule:OptimizerDataLoader&Dataset：数据处理DataLoader&Dataset:DataProcessingPyTorch基础知识BasicKnowledgeofPyTorch什么是张量WhatIsaTensor张量Tensor类似于多维数组Similartoamultidimensionalarray张量操作TensorOperations创建、索引、运算Creation,indexing,andcomputationPyTorch基础知识PyTorchBasics计算图&自动微分ComputationalGraph&Autograd计算图ComputationalGraph自动微分AutomaticDifferentiation计算图：节点表示操作，边表示数据ComputationalGraph：Nodesrepresentoperations,andedgesrepresentdata.Autograd：自动求导机制Autograd:AutomaticDifferentiationMechanismPyTorch发展简史ABriefHistoryofPyTorch2018202020162016年：PyTorch1.0前身发布2016:TheprecursortoPyTorch1.0wasreleased2016年：稳定版本发布，受欢迎2018:Thestableversionwasreleasedandgainedwidespreadpopularity2020年以后：广泛应用于学术与工业After2020:WidelyAppliedinAcademiaandIndustryPyTorch安装与配置PyTorchInstallationandConfiguration安装方式：pip或condaInstallationMethods：viapiporcondaCUDA支持（GPU加速）CUDASupport(GPUAcceleration)验证安装VerifyInstallationimporttorch;print(torch.__version__)实际应用PracticalApplications小结SummarizePyTorch概念+组成PyTorchConcept&Components基础知识：张量、计算图、自动微分BasicKnowledge:Tensor,ComputationalGraph,andAutomaticDifferentiation发展历程DevelopmentHistory安装配置方法InstallationandConfigurationMethodsUnderstandingYOLOYOLO的认知这些场景背后，是什么技术让机器能自动“看见”并“认出”物体？Whattechnologyenablesmachinestoautomatically“see”and“recognize”objectsbehindthesescenes?生活中的“眼睛”The“Eyes”inOurDailyLivesPyTorch简介IntroductiontoPyTorch目标检测=找出来+认出来ObjectDetection=Localization+Classification在图片中定位（Localization）出物体的位置，并分类（Classification）出它是什么。Inanimage,localizethepositionoftheobject(Localization)andclassifywhattheobjectis(Classification).早期目标检测方法EarlyObjectDetectionMethods代表算法：RepresentativeAlgorithmsR-CNN（2014）：基于候选区域Region-BasedApproach局限性LimitationsFastR-CNN、FasterR-CNN：速度逐步提升Speedgraduallyincreases检测速度慢Slowdetectionspeed结构复杂、难以部署ComplexstructureanddifficulttodeployYOLO：统一的实时目标检测框架YOLO:AUnifiedReal-TimeObjectDetectionFramework左侧：传统两阶段目标检测流程图（区域提议+分类回归）Left:TraditionalTwo-StageObjectDetectionPipeline(RegionProposal+Classification&Regression)右侧：YOLO单阶段Right:YOLOSingle-StageDetectionYOLO：统一的实时目标检测框架YOLO:AUnifiedReal-TimeObjectDetectionFramework将目标检测重构为单一的回归问题。Reframesobjectdetectionasasingleregressionproblem.对整张图像仅处理一次，即可直接输出目标位置与类别信息。Processestheentireimageonlyoncetodirectlypredictobjectlocationsandclasses.显著提升了检测速度，满足实时性应用需求。Significantlyimprovesdetectionspeed,enablingreal-timeapplications.-YOLO核心优势-CoreAdvantagesofYOLOYOLO系列模型的演进与优化EvolutionandOptimizationoftheYOLOSeriesModels2015YOLOv1–提出统一的实时检测框架。YOLOv1–Proposedaunifiedframeworkforreal-timeobjectdetection.2016YOLOv2–引入锚框机制，提升召回率与多尺度检测能力。YOLOv2–Introducedanchorboxes,improvingrecallandmulti-scaledetectioncapability.2018YOLOv3–采用多尺度预测，在速度与精度间取得良好平衡。YOLOv3–Adoptedmulti-scaleprediction,achievingagoodbalancebetweenspeedandaccuracy.2020YOLOv4/YOLOv5–集成多种训练技巧，v5以其工程友好性广泛应用。YOLOv4/YOLOv5–Integratedmultipletrainingtricks;YOLOv5becamewidelyusedforitsengineeringefficiency.2022–PresentYOLOv6/YOLOv7/YOLOv8...–在网络结构与训练策略上持续创新。YOLOv6/YOLOv7/YOLOv8...–Continuousinnovationinnetworkarchitectureandtrainingstrategies.2025.09YOLOv26–面向边缘端与低功耗设备的全新架构之作。YOLOv26–Anewarchitecturedesignedforedgecomputingandlow-powerdevices.环境配置要求EnvironmentConfigurationRequirements配置要求ConfigurationRequirements:Python3.7及以上版本Python3.7orabovePyCharmIDE开发环境PyCharmIDEdevelopmentenvironmentUltralyticsYOLO软件包UltralyticsYOLOpackage环境作用：提供算法运行与开发的基础平台Purpose:P