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所在平台: Udemy |
课程主页: https://www.udemy.com/course/train-object-detection-models-for-android-java-kotlin/
课程评论：没有评论
**课程名称：** Train & Deploy TFLite Object Detection for Android **课程概述：** 本课程专注于将AI能力从云端遷移至移动设备，利用 TensorFlow Lite (TFLite) 实现Android手机上的实时物体检测，无需服务器，实现零延迟。课程提供端到端的完整工作流程，涵盖使用Kotlin和Java训练、转换和部署自定义模型。 **主要学习内容：** * **数据收集与标注：** 学习使用LabelImg、CVAT或Roboflow等工具捕获和标注图像，创建高质量数据集。 * **模型训练：** 通过动手实操的Colab笔记本，学习从零开始训练或微调TensorFlow / YOLO / EfficientDet / SSD-MobileNet等模型。 * **TFLite转换与优化：** 掌握量化、剪枝和添加元数据等技术，以最大化每秒帧率 (FPS) 并最小化电池消耗。 * **Android集成：** 使用Kotlin或Java，结合CameraX和ML Model Binding，构建能够对图像和实时摄像头流进行物体检测的应用。 * **使用预训练模型：** 能够仅通过几行代码，快速集成现成的YOLOv8-Nano、EfficientDet-Lite或SSD-MobileNet模型。 **提供的资源：** * 价值$1,000+的生产级别Android模板（Kotlin & Java） * 可重复使用的模型转换脚本和Colab笔记本 * 预标注的示例数据集，助力快速入门 * 常见TFLite错误与性能调优的速查表 **能构建的实际应用场景：** * 带入侵警报的智能CCTV * 工业生产线的缺陷检测 * 人流计数与零售分析仪表盘 * 自动驾驶或AR应用的原型模块 **适合人群：** * 渴望为设备添加AI功能的Android开发者（初学者至专家） * 目标是移动部署和边缘AI优化的ML工程师 * 希望无需后端即可构建视觉驱动应用的创客、初创公司创始人或爱好者 **必备条件：** * 熟悉Android Studio（布局、活动、Gradle） * 初步了解Python（课程中的笔记本将处理大部分复杂操作） * 拥有8GB RAM的电脑（重度训练可在免费的Google Colab GPU上进行） **课程形式：** * 1080p高清视频讲座（已更新至Android Studio 2025 & TensorFlow Lite 3.x） * 每个章节后的小项目，巩固所学技能 * 终身访问权限、问答支持和30天退款保证 **课程目标：** 帮助学员构建快速、可靠且完全在Android设备上运行的物体检测应用。
Mobile AI is shifting from cloud to on-device. With TensorFlow Lite (TFLite) you can run real-time object detection directly on Android phones-no server, zero latency. This course gives you an end-to-end workflow to train, convert, and deploy custom models using Kotlin and Java.What You'll MasterData Collection & AnnotationCapture images and label them with LabelImg, CVAT, or Roboflow to create high-quality datasets.Model Training in TensorFlow / YOLO / EfficientDet / SSD-MobileNetHands-on Colab notebooks show you how to train from scratch or fine-tune pre-trained weights.TFLite Conversion & OptimizationQuantize, prune, and add metadata for maximum FPS and minimum battery drain.Android Integration (CameraX + ML Model Binding)Build apps in Kotlin or Java that detect objects in both images and live camera streams.Using Pre-Trained ModelsPlug in ready-made YOLOv8-Nano, EfficientDet-Lite, or SSD-MobileNet with just a few lines of code.Included ResourcesProduction-ready Android templates (Kotlin & Java) worth $1,000+Re-usable model-conversion scripts and Colab notebooksPre-annotated sample dataset to get you started fastCheatsheets for common TFLite errors and performance tuningReal-World Use-Cases You'll BuildSmart CCTV with intrusion alertsIndustrial defect detection on assembly linesCrowd counting & retail analytics dashboardsPrototype modules for self-driving or AR appsWho Should Enroll?Android developers eager to add on-device AI (beginner to pro)ML engineers targeting mobile deployment and edge-AI optimizationMakers, startup founders, or hobbyists who want to build vision-powered apps without a backendWhat You NeedBasic Android Studio familiarity (layouts, activities, Gradle)Light Python knowledge (all heavy lifting handled in the provided notebooks)A computer with 8 GB RAM-heavy training runs on free Google Colab GPUsCourse Format1080p HD video lectures (updated for Android Studio 2025 & TensorFlow Lite 3.x)Mini-projects after each section to cement skillsLifetime access, Q & A support, and Udemy's 30-day money-back guaranteeReady to build fast, reliable object-detection apps that run entirely on Android devices?Click Buy Now and start training & deploying your own TFLite models today!
