地球资源数据云——数据资源详情
葡萄干二元分类
该数据集《Raisin binary classification》主要用于二分类任务，数据形态以图像为主，应用场景偏向安全检测。 题目说明：Images of the Kecimen and Besni raisin varieties were obtained with CVS 任务类型：图像二分类。 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：Raisin_Dataset.csv。 Abstract: Images of the Kecimen and Besni raisin varieties were obtained with CVS. A total of 900 raisins were used, including 450 from both varieties, and 7 morphological features were extracted. Data Set Information: Images of Kecimen and Besni raisin varieties grown in Turkey were obtained with CVS. A total of 900 raisin grains were used, including 450 pieces from both varieties. These images were subjected to various stages of pre - processing and 7 morphological features were extracted. These features have been classified using three different artificial intelligence techniques.
摘要概览
该数据集《Raisin binary classification》主要用于二分类任务，数据形态以图像为主，应用场景偏向安全检测。 题目说明：Images of the Kecimen and Besni raisin varieties were obtained with CVS
任务类型：图像二分类。
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：Raisin_Dataset.csv。
Abstract: Images of the Kecimen and Besni raisin varieties were obtained with CVS. A total of 900 raisins were used, including 450 from both varieties, and 7 morphological features were extracted.
Data Set Information: Images of Kecimen and Besni raisin varieties grown in Turkey were obtained with CVS. A total of 900 raisin grains were used, including 450 pieces from both varieties. These images were subjected to various stages of pre - processing and 7 morphological features were extracted.
These features have been classified using three different artificial intelligence techniques.