CABBAGE

The dataset comprises over 500 images of cabbage (Brassica oleracea var. capitata) classified into two categories: "good" and "bad". These images were captured using a OnePlus Nord CE 2 Lite 5G mobile camera, under consistent daylight conditions against a black background.

Each image represents either a "good" cabbage, characterized by desirable attributes such as freshness, uniformity, absence of blemishes, and overall quality, or a "bad" cabbage, exhibiting defects like rot, discoloration, pest damage, or irregular shape.

The dataset provides a comprehensive representation of the variations in cabbage quality, facilitating the training and evaluation of machine learning models for cabbage classification tasks.

For each image, metadata may include details such as:
- Image resolution
- Image format (e.g., JPEG)
- Date and time of capture
- Lighting conditions
- Camera settings (e.g., aperture, exposure)
- Ground truth label (good/bad)

The images are labeled accordingly, enabling supervised learning algorithms to learn the distinguishing features between good and bad cabbage specimens. This dataset can be utilized across various applications, including agricultural automation, quality control in food processing industries, and computer vision research.

该数据集包含超过500张结球甘蓝（Brassica oleracea var. capitata）的图像，分为“优质”与“劣质”两类。所有图像均由OnePlus Nord CE 2 Lite 5G手机摄像头拍摄，拍摄时采用统一日光照明，背景为纯黑色。

每张图像对应一颗“优质”卷心菜或“劣质”卷心菜：优质卷心菜具备新鲜度佳、形态规整、无瑕疵且整体品质优良等特性；劣质卷心菜则存在腐烂、变色、虫害损伤或形状不规则等缺陷。

该数据集全面涵盖了卷心菜品质的各类差异情况，可用于训练和评估面向卷心菜品质分类任务的机器学习模型。

每张图像的元数据可包含以下细节：
- 图像分辨率
- 图像格式（如JPEG）
- 拍摄日期与时间
- 光照条件
- 相机参数（如光圈、曝光度）
- 真实标注标签（优质/劣质）

所有图像均已完成对应标注，可支撑监督学习算法学习优质与劣质卷心菜样本的区分特征。该数据集可应用于多个场景，包括农业自动化、食品加工行业的品质管控以及计算机视觉研究领域。

搜集汇总

背景与挑战

背景概述

CABBAGE数据集包含500多张卷心菜图像，分为'好'和'坏'两类，图像在统一光照条件和黑色背景下拍摄，适用于农业自动化和食品质量控制等领域的机器学习模型训练。