地球资源数据云——数据资源详情
干豆数据集分类
该数据集《Dry Bean Dataset Classification》主要用于多分类任务，数据形态以图像为主。 题目说明：Images of 13,611 grains of 7 different registered dry beans. 任务类型：图像多分类。 建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：Dry_Bean_Dataset.csv。 Relevant Information: Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification. For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high - resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.
摘要概览
该数据集《Dry Bean Dataset Classification》主要用于多分类任务，数据形态以图像为主。 题目说明：Images of 13,611 grains of 7 different registered dry beans.
任务类型：图像多分类。
建议流程：先检查类别分布与脏样本，再用迁移学习（如 ResNet/EfficientNet）建立基线。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：Dry_Bean_Dataset.csv。
Relevant Information: Seven different types of dry beans were used in this research, taking into account the features such as form, shape, type, and structure by the market situation. A computer vision system was developed to distinguish seven different registered varieties of dry beans with similar features in order to obtain uniform seed classification.
For the classification model, images of 13,611 grains of 7 different registered dry beans were taken with a high - resolution camera. Bean images obtained by computer vision system were subjected to segmentation and feature extraction stages, and a total of 16 features; 12 dimensions and 4 shape forms, were obtained from the grains.