LeftInCAR: In-Vehicle Object Detection Dataset

This dataset was developed to support research on object detection and recognition, focusing on items forgotten inside vehicles. It captures a diverse range of real-world scenarios under different lighting conditions, both indoors and outdoors, to ensure robustness and applicability in various analytical tasks.

The collection contains 971 high-quality images featuring everyday objects such as: 0 - smartphone, 1 - laptop, 2 - card, 3 - suitcase, 4 - wallet, 5 - backpack, 6 - clothing, 7 - keys, 8 - glasses, 9 - handbag.

The dataset is organized into two main directories (inside leftincar-data.zip):

➔ images/ – contains all visual samples.
➔ labels/ – includes YOLO-format annotation files (.txt), one per image. Images without annotations correspond to negative samples (no objects present).

An additional Python script, yolo_dataset_splitter.py, is provided to automate the division of the dataset into training, validation, and testing subsets.
The script ensures that all images are included in the output, creating empty label files where necessary for full YOLO compatibility.

本数据集专为车辆内部遗留物品的目标检测与识别研究开发。其采集了覆盖室内外、不同光照条件下的多样化真实场景数据，以确保在各类分析任务中具备鲁棒性与适用性。

该数据集共包含971张高质量图像，涵盖的日常物品类别如下：0-智能手机、1-笔记本电脑、2-卡片、3-行李箱、4-钱包、5-背包、6-衣物、7-钥匙、8-眼镜、9-手提包。

数据集在leftincar-data.zip压缩包内分为两个主要目录：
● images/ —— 存储全部视觉样本
● labels/ —— 包含YOLO格式的标注文件（.txt），每张图像对应一个标注文件。无标注的图像即为负样本（无目标存在）。

此外还提供了Python脚本yolo_dataset_splitter.py，用于自动将数据集划分为训练集、验证集与测试子集。该脚本可确保所有图像均被纳入输出结果，必要时会生成空标注文件以完全兼容YOLO框架。