2018年用于深度学习的苹果图像数据集

DeepFruits数据集用于微调为水果检测的预训练准备的深度学习模型。虽然作者称在研究工作中检查了两种图像模式，但已发布的数据集仅包含 红绿蓝 图像，图像数据均在温室和开阔地中收集。该数据集包含7个不同水果图像子集，包括甜椒、冰瓜、苹果、芒果、橙子和草莓，每种水果分别有42-170 张不同分辨率的png格式图像，并被划分为训练数据集和测试数据集。该数据集为执行水果检测提供了边界框注释依据。https://drive.google.com/drive/folders/1CmsZb1caggLRN7ANfika8WuPiywo4mBbThe DeepFruits dataset is designed for fine-tuning pre-trained deep learning models for fruit detection tasks. Although the authors indicated that two image modalities were examined in their research, the publicly released dataset only contains RGB (Red-Green-Blue) images. All image samples were collected in greenhouses and open fields. This dataset includes 7 distinct fruit image subsets, covering bell peppers, winter melons, apples, mangoes, oranges, and strawberries. Each fruit category has 42 to 170 PNG-format images with varying resolutions, and the dataset is split into training and test sets. This dataset provides bounding box annotations for fruit detection tasks. https://drive.google.com/drive/folders/1CmsZb1caggLRN7ANfika8WuPiywo4mBb