metadata
task_categories:
- image-classification
AutoTrain Dataset for project: logo_identifier_v5_short
Dataset Description
This dataset has been automatically processed by AutoTrain for project logo_identifier_v5_short.
Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"image": "<86x100 RGB PIL image>",
"target": 48
},
{
"image": "<128x128 RGB PIL image>",
"target": 36
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"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', '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)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 6814 |
valid | 1768 |