Annotated Fire -Smoke Image Dataset for fire detection Using YOLO.

This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

本数据集包含11027张带标注图像，用于在多样化真实场景中开展火情与烟雾实例的检测任务。标注采用YOLO格式，涵盖两类目标的边界框与类别标签：火情（fire）与烟雾（smoke）。数据集按比例划分为三部分：80%为训练集，包含10090个火情实例与9724个烟雾实例；10%为验证集，包含1255个火情实例与1241个烟雾实例；剩余10%为测试集，同样包含1255个火情实例与1241个烟雾实例。本数据集可用于训练与评估火情与烟雾检测模型，例如YOLOv8、YOLOv9及其他同类基于深度学习的目标检测框架，适配应急响应、野火监测与智能监控等应用场景。

搜集汇总

背景与挑战

背景概述

该数据集包含11027张标注图像，用于检测真实场景中的火灾和烟雾实例，标注采用YOLO格式，包含火灾和烟雾两类标签。数据集分为训练集（80%）、验证集（10%）和测试集（10%），适用于YOLOv8、YOLOv9等深度学习框架的训练和评估，主要用于应急响应、野火监测和智能监控等领域。