July 17th Friday (七月 十七日 金曜日)

Wills and Executors

  A will executor manages a collection of values and associated will procedures (a.k.a. finalizers). The will procedure for each
value is ready to be executed when the value has been proven (by the garbage collector) to be unreachable, except through weak
references or as the registrant for other will executors. A will is useful for triggering clean-up actions on data associated with
an unreachable value, such as closing a port embedded in an object when the object is no longer used.

  Calling the will-execute or will-try-execute procedure executes a will that is ready in the specified will executor. Wills are
not executed automatically, because certain programs need control to avoid race conditions. However, a program can create a thread
whose sole job is to execute wills for a particular executor.

  If a value is registered with multiple wills (in one or multiple executors), the wills are readied in the reverse order of registration.
Since readying a will procedure makes the value reachable again, the will must be executed and the value must be proven again unreachable
through only weak references before another of the wills is readied or executed. However, wills for distinct unreachable values are
readied at the same time, regardless of whether the values are reachable from each other.

  A will executor's register is held non-weakly until after the corresponding will procedure is executed. Thus, if the content value
of a weak box is registered with a will executor, the weak box's content is not changed to #f until all wills have been executed for
the value and the value has been proven again reachable through only weak references.

数据集介绍:垃圾分类检测数据集 一、基础信息 数据集名称:垃圾分类检测数据集 图片数量: 训练集:2,817张图片 验证集:621张图片 测试集:317张图片 总计:3,755张图片 分类类别: - 金属:常见的金属垃圾材料。 - 纸板:纸板类垃圾,如包装盒等。 - 塑料:塑料类垃圾,如瓶子、容器等。 标注格式: YOLO格式,包含边界框和类别标签,适用于目标检测任务。 数据格式:图片来源于实际场景,格式为常见图像格式(如JPEG/PNG)。 二、适用场景 智能垃圾回收系统开发: 数据集支持目标检测任务,帮助构建能够自动识别和分类垃圾材料的AI模型,用于自动化废物分类和回收系统。 环境监测与废物管理: 集成至监控系统或机器人中,实时检测垃圾并分类,提升废物处理效率和环保水平。 学术研究与教育: 支持计算机视觉与环保领域的交叉研究,用于教学、实验和论文发表。 三、数据集优势 类别覆盖全面: 包含三种常见垃圾材料类别,覆盖日常生活中主要的可回收物类型,具有实际应用价值。 标注精准可靠: 采用YOLO标注格式,边界框定位精确,类别标签准确,便于模型直接训练和使用。 数据量适中合理: 训练集、验证集和测试集分布均衡,提供足够样本用于模型学习和评估。 任务适配性强: 标注兼容主流深度学习框架(如YOLO等),可直接用于目标检测任务,支持垃圾检测相关应用。
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