Fruits 360 dataset

Fruits 360 dataset: A dataset of images containing fruits Version: 2018.09.07.0 The following fruits are included: Apples (different varieties: Golden, Golden-Red, Granny Smith, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red), Cactus fruit, Cantaloupe (2 varieties), Carambula, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Clementine, Cocos, Dates, Granadilla, Grape (Pink, White, White2), Grapefruit (Pink, White), Guava, Huckleberry, Kiwi, Kaki, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine, Orange, Papaya, Passion fruit, Peach, Pepino, Pear (different varieties, Abate, Monster, Williams), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum, Pomegranate, Quince, Rambutan, Raspberry, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red), Walnut. Dataset properties Total number of images: 55244. Training set size: 41322 images (one fruit per image). Test set size: 13877 images (one fruit per image). Multi-fruits set size: 45 images (more than one fruit (or fruit class) per image) Number of classes: 81 (fruits). Image size: 100x100 pixels. Filename format: image_index_100.jpg (e.g. 32_100.jpg) or r_image_index_100.jpg (e.g. r_32_100.jpg) or r2_image_index_100.jpg. "r" stands for rotated fruit. "r2" means that the fruit was rotated around the 3rd axis. "100" comes from image size (100x100 pixels). Different varieties of the same fruit (apple for instance) are stored as belonging to different classes. How we made it Fruits were planted in the shaft of a low speed motor (3 rpm) and a short movie of 20 seconds was recorded. A Logitech C920 camera was used for filming the fruits. This is one of the best webcams available. Behind the fruits we placed a white sheet of paper as background. However due to the variations in the lighting conditions, the background was not uniform and we wrote a dedicated algorithm which extract the fruit from the background. This algorithm is of flood fill type: we start from each edge of the image and we mark all pixels there, then we mark all pixels found in the neighborhood of the already marked pixels for which the distance between colors is less than a prescribed value. We repeat the previous step until no more pixels can be marked. All marked pixels are considered as being background (which is then filled with white) and the rest of pixels are considered as belonging to the object. The maximum value for the distance between 2 neighbor pixels is a parameter of the algorithm and is set (by trial and error) for each movie. Published research papers Horea Muresan, Mihai Oltean, Fruit recognition from images using deep learning, Acta Univ. Sapientiae, Informatica Vol. 10, Issue 1, pp. 26-42, 2018. License MIT License Copyright (c) 2017-2018 Mihai Oltean, Horea Muresan

Fruits 360 数据集：一款包含水果图像的数据集，版本号：2018.09.07.0。
本数据集包含以下水果品类：苹果（含多个品种：金冠、金红、澳洲青苹（Granny Smith）、红苹果、红蛇果（Red Delicious））、杏、牛油果、成熟牛油果、香蕉（黄皮、红皮）、仙人掌果、甜瓜（2个品种）、杨桃（Carambula）、樱桃（含多个品种，如雷尼尔樱桃（Rainier））、蜡质樱桃（黄、红、黑三色）、克莱门氏小柑橘（Clementine）、椰子、椰枣、紫果西番莲（Granadilla）、葡萄（粉果、白果、白果2号）、西柚（粉肉、白肉）、番石榴、越橘（Huckleberry）、猕猴桃、柿子（Kaki）、金桔（Kumsquats）、柠檬（普通品种、迈耶柠檬（Meyer））、青柠、荔枝、柑橘（Mandarine）、芒果、马拉库尔果（Maracuja）、蛇皮甜瓜（Melon Piel de Sapo）、桑树果、油桃、橙子、木瓜、西番莲果（Passion fruit）、桃子、香瓜茄（Pepino）、梨（含多个品种：阿巴特、蒙斯特、威廉姆斯）、酸浆（普通品种、带壳品种）、菠萝（普通品种、迷你品种）、红皮火龙果（Pitahaya Red）、李子、石榴、榅桲、红毛丹、树莓、蛇皮果（Salak）、草莓（普通品种、楔形草莓）、树番茄（Tamarillo）、橘柚（Tangelo）、番茄（含多个品种：褐果、樱桃红果）、核桃。
数据集属性如下：总图像数量：55244张。训练集规模：41322张图像，每张图像仅含单种水果。测试集规模：13877张图像，每张图像仅含单种水果。多水果集规模：45张图像，每张图像含多种水果或多个水果类别。类别总数：81类水果。图像分辨率：100×100像素。文件名格式为：image_index_100.jpg（例如32_100.jpg）、r_image_index_100.jpg（例如r_32_100.jpg）或r2_image_index_100.jpg。其中“r”代表水果已被旋转，“r2”代表水果沿第三轴进行旋转，“100”对应图像的100×100像素分辨率。同一水果的不同品种（如苹果）将被划分为独立类别进行存储。
数据集制作流程如下：将水果放置于转速为3转每分钟的低速电机转轴上，录制一段20秒的短视频。采用罗技（Logitech）C920网络摄像头进行拍摄，该设备为市面主流优质网络摄像头之一。在水果后方放置白色纸张作为拍摄背景，但由于光照条件存在差异，背景并非均匀纯色，因此我们编写了专用算法以从背景中分离水果。该算法属于泛洪填充（flood fill）类型：从图像的每条边缘开始标记初始像素，随后标记所有已标记像素邻域内、色彩间距小于预设阈值的像素，重复该步骤直至无可标记新像素。所有被标记的像素均被视为背景，后续将填充为白色；剩余像素则归属为拍摄的目标水果物体。相邻像素间的最大色彩间距为算法可调参数，需针对每个录制视频通过试错法进行设置。
已发表的相关研究论文：Horea Mureșan、Mihai Oltean，《基于深度学习的图像水果识别》，发表于《Acta Univ. Sapientiae, Informatica》第10卷第1期，第26-42页，2018年。
许可证：MIT许可证，版权所有 © 2017-2018 Mihai Oltean、Horea Mureșan

搜集汇总

背景与挑战

背景概述

Fruits 360 dataset是一个包含81种水果的55244张图像的数据集，图像尺寸统一为100x100像素，分为训练集和测试集，适用于图像分类和深度学习研究。数据集特点包括覆盖广泛水果品种、背景经过算法处理以突出水果对象，并提供旋转图像变体，主要用于图像处理和卷积神经网络训练。