An image dataset of YMushroom edible fungus for deep learning recognition in 2019-2021

The automatic identification of edible fungus based on deep learning can provide technical support for automatic marking and pricing for unmanned supermarkets and vegetable markets in the construction of smart city. Through the identification of intelligent traceability scale, the type and price of edible fungus are automatically displayed, which can reduce the consumption of human resources and save time and cost. At present, the machine recognition of edible fungus mainly depends on a small number of edible fungi pictures collected by some researchers independently in the experimental environment, and there is a lack of edible fungus picture samples obtained in the complex natural environment. This YMushroom dataset provides high-definition edible fungus images that can be used for deep learning image classification model training, including dry and fresh edible fungus in different seasons, different acquisition backgrounds and different acquisition equipment. The dataset is divided into 28 categories, with a total of 49958 pictures. Among them, the sample size of Shaggy Cap pictures is the least, and Oyster Mushroom pictures is the most, 969 and 2578 respectively. The median sample size of a single edible fungus type is 1764, which can meet the training needs of mainstream deep learning models. This dataset can provide basic data for edible fungus image classification, object detection, semantic segmentation, panoptic segmentation and other research.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于深度学习识别的食用菌图像数据集，旨在为智能城市中的无人超市和菜市场提供自动识别与标价的技术支持。数据集包含28个类别的49958张高清图像，覆盖不同季节、背景和采集设备的干鲜食用菌，样本量从969到2578张不等，中位数为1764张，能满足主流深度学习模型的训练需求，适用于图像分类、目标检测和语义分割等研究。