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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/deep-learning-object-detection
课程评论：没有评论
课程名称：深度学习与目标检测 课程概述：目标检测与定位是深度学习在计算机视觉领域最常见的应用之一。应用场景包括帮助自主系统在复杂环境中导航、定位医学状况如肿瘤，以及在农业中识别成熟的收割作物。在课程项目中，学员将把检测模型应用于实际场景，并训练模型以检测各种停车标志。完成本课程将使学员具备为其应用训练检测模型的技能。 课程大纲： 1. 名称：使用预训练模型进行目标检测 描述：通过使用预训练模型入门目标检测。 2. 名称：训练目标检测模型 描述：利用迁移学习重新训练YOLO模型以适应新的应用。 3. 名称：评估目标检测模型 描述：使用召回率、精确率和平均精确度等指标来评估模型性能。 4. 名称：最终项目：训练与评估检测模型 描述：在最终项目中应用完整的目标检测工作流程。 本课程将为学员提供实践经验和理论知识，是希望深入了解目标检测技术的学习者的理想选择。
Name:Detecting Objects with Pre-trained Models
Description:Get started with object detection by using pre-trained models
Name:Training Object Detection Models
Description:Use transfer learning to retrain YOLO models for new applications
Name:Evaluating Object Detection Models
Description:Use metrics like recall, precision, and mean average precision to evaluate your models
Name:Final Project: Train and Evaluate a Detection Model
Description:Apply the full object detection workflow on a final project
Detecting and locating objects is one of the most common uses of deep learning for computer vision. Applications include helping autonomous systems navigate complex environments, locating medical conditions like tumors, and identifying ready-to-harvest crops in agriculture. In the course projects, you will apply detection models to real-world scenarios and train a model to detect various parking signs. Completing this course will give you the skills to train detection models for your application
