fsuarez/autotrain-data-logo_identifier_v4_short

该数据集是一个用于图像分类任务的数据集，专门用于项目logo_identifier_v4_short。数据集由AutoTrain自动处理，包含训练集和验证集两个部分。数据实例由图像和对应的目标标签组成，图像为RGB格式的PIL图像，目标标签为ClassLabel类型，表示不同的品牌或公司名称。数据集的语言代码为unk，表示未知。

This dataset is intended for image classification tasks, and is specifically developed for the project logo_identifier_v4_short. It was automatically processed by AutoTrain, and contains two subsets: the training set and the validation set. Each data instance comprises an image and its corresponding target label. The images are RGB-format PIL images, while the target labels are of the ClassLabel type, representing distinct brand or company names. The language code of this dataset is 'unk', which indicates unknown.

原始信息汇总

数据集描述

该数据集是为项目 logo_identifier_v4_short 自动处理生成的。

语言

数据集的语言代码为 unk。

数据结构

数据实例

数据集中的样本示例如下：

json [ { "image": "<128x128 RGB PIL image>", "target": 98 }, { "image": "<100x100 RGB PIL image>", "target": 99 } ]

数据字段

数据集包含以下字段（也称为“特征”）：

json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=[20thTelevision, 3M, 7Eleven, Acer, AmericanExpress, Amul, Anthem, ApolloHospitals, Apple, Armani, Asahi, Asus, Atari, Audi, Avon, Booking, Bosch, Bridgestone, British Airways, Budweiser, Burberry, BurgerKing, BuzzFeed, Canon, CocaColaZero, Coleman, Coles, Converse, CornFlakes, Corona, CostcoWholesale, Crayola, Credit Agricole, Crocs, Crunchyroll, Ctrip, Dropbox, Ducati, DunkinDonuts, Duracell, Dyson, Ethereum, ExxonMobil, FoxNews, FreddieMac, Fujitsu, Goodyear, Grubhub, Gucci, Huawei, Hudson Bay Company, HugoBoss, Hulu, Hyundai, Instagram, Intel, John Lewis & Partners, Johnson&Johnson, Kingston, LouisVuitton, Lowes, Lufthansa, Lululemon, Luxottica, MorganStanley, Motorola, MountainDew, Moutai, Movistar, Msci, Muji, Nike, Nissan, Nokia, Nvidia, Orange, Oreo, Porsche, Power China, Prada, Pringles, Publix, Puma, Purina, PwC, Qualcomm, Rolex, Rolls-Royce, RoyalCaribbean, Spotify, Sprite, Starbucks, StateBankofIndia, StateGrid, Subaru, Subway, SumitomoGroup, Suning, Supreme, Suzuki, Total SA, TotalEnergies, Toyota, TripAdvisor, Twitch, Twitter, UnitedHealthCare, Universal, Volkswagen, Volvo, Wikipedia, Wipro, Wuliangye, Xiaomi, Youtube, Zoom, hennessy, iHeartRadio, koolAid], id=None)" }

数据集分割

数据集分为训练集和验证集，分割大小如下：

分割名称	样本数量
train	6884
valid	1786