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image_path
imagewidth (px)
321
524
industry_name
stringclasses
9 values
company_name
class label
3k classes
bbox
sequencelengths
4
4
Clothes
2,020nicole lee-1
[ 22, 68, 366, 448 ]
Necessities
1,119Ifb
[ 94, 120, 455, 278 ]
Others
575Cesars
[ 4, 1, 522, 242 ]
Others
526Cargill
[ 1, 19, 235, 257 ]
Food
484Cailler
[ 254, 54, 523, 240 ]
Clothes
2,288rapha
[ 163, 130, 323, 200 ]
Electronic
52ASUS
[ 182, 155, 325, 194 ]
Clothes
1,990nasty pig-2
[ 101, 38, 489, 334 ]
Necessities
2,999zwitsal
[ 194, 173, 289, 196 ]
Necessities
504Candle
[ 1, 198, 356, 243 ]
Sports
2,891wiffle bat and ball
[ 136, 64, 367, 233 ]
Necessities
420Bruno Banani
[ 246, 110, 413, 153 ]
Necessities
2,671teflon
[ 93, 216, 282, 286 ]
Necessities
284Ben Franklin Stores
[ 174, 82, 395, 134 ]
Transportation
1,739lifan-1
[ 159, 112, 330, 234 ]
Necessities
2,006neutrogena
[ 9, 98, 472, 203 ]
Others
2,915woolworths (south africa)
[ 142, 292, 340, 321 ]
Food
2,262quickchek
[ 72, 305, 184, 349 ]
Necessities
2,224pritt stick
[ 68, 185, 112, 219 ]
Food
2,042nutren
[ 174, 44, 316, 98 ]
Food
2,344robeks
[ 124, 174, 283, 238 ]
Food
581Cheader's
[ 38, 143, 459, 234 ]
Leisure
1,925mohawk-1
[ 1, 200, 524, 360 ]
Clothes
2,408salomon
[ 2, 253, 522, 320 ]
Necessities
1,680kimani
[ 325, 306, 459, 361 ]
Clothes
1,082Hummel-2
[ 71, 214, 132, 272 ]
Food
1,232Kotipizza
[ 103, 6, 379, 183 ]
Food
2,762tully's coffee
[ 143, 34, 385, 379 ]
Others
161Argos Energies
[ 22, 171, 115, 210 ]
Food
269Bear Republic
[ 137, 18, 329, 252 ]
Clothes
166Armani
[ 247, 219, 385, 264 ]
Food
2,645taco john's
[ 90, 37, 273, 102 ]
Clothes
1,531conlia
[ 38, 39, 122, 59 ]
Leisure
2,732tonka toys
[ 20, 62, 518, 333 ]
Others
2,591stein mart
[ 189, 23, 295, 55 ]
Food
1,525coca cola
[ 19, 24, 468, 310 ]
Clothes
2,428satya paul
[ 43, 224, 452, 310 ]
Clothes
1,986nanjiren
[ 102, 131, 168, 170 ]
Necessities
1,428bluemoon
[ 351, 163, 501, 246 ]
Food
416Brothers Cider
[ 66, 254, 178, 278 ]
Leisure
2,617sun 'n' sand
[ 8, 192, 504, 312 ]
Others
1,691la corona
[ 400, 183, 473, 238 ]
Food
1,677kichesippi-1
[ 128, 270, 368, 308 ]
Clothes
1,479chaps
[ 3, 6, 493, 258 ]
Others
457CHOW TAI SENG-1
[ 70, 435, 201, 447 ]
Electronic
1,742lightolier
[ 128, 17, 370, 103 ]
Leisure
2,862war horse
[ 5, 1, 511, 97 ]
Food
2,790upslope
[ 159, 65, 353, 314 ]
Necessities
1,328Marsh Wheeling
[ 302, 33, 476, 150 ]
Electronic
113Amoi
[ 203, 126, 379, 255 ]
Clothes
2,453seiko
[ 52, 152, 444, 212 ]
Transportation
1,740lifan-2
[ 65, 47, 438, 313 ]
Food
2,574square one organic
[ 77, 145, 200, 243 ]
Necessities
497Camay
[ 76, 280, 462, 341 ]
Necessities
1,881mentadent sr
[ 75, 125, 250, 246 ]
Others
1,755liu gong
[ 28, 92, 504, 293 ]
Food
2,637swiss miss
[ 251, 102, 430, 174 ]
Clothes
749ERAL
[ 353, 54, 401, 74 ]
Necessities
177Arturo Fuente
[ 162, 181, 328, 212 ]
Others
1,517china post group corporation
[ 47, 240, 94, 286 ]
Clothes
42xist
[ 230, 308, 485, 343 ]
Clothes
173Arri
[ 2, 1, 489, 366 ]
Others
2,915woolworths (south africa)
[ 408, 154, 465, 198 ]
Clothes
2,958youngor-2
[ 92, 263, 427, 329 ]
Necessities
2,765tungsram
[ 10, 360, 364, 487 ]
Food
2,042nutren
[ 15, 39, 470, 195 ]
Food
2,637swiss miss
[ 202, 170, 301, 199 ]
Electronic
1,601hd
[ 37, 12, 212, 72 ]
Necessities
1,365amish
[ 1, 180, 251, 278 ]
Food
1,678kichesippi-2
[ 217, 76, 310, 153 ]
Food
2,991zjs express
[ 200, 328, 302, 405 ]
Clothes
2,496simon
[ 81, 134, 473, 350 ]
Electronic
2,803vax
[ 28, 115, 486, 295 ]
Electronic
624Chronoswiss-2
[ 312, 132, 440, 174 ]
Necessities
1,801lysoform
[ 116, 169, 215, 196 ]
Food
2,204poppycock
[ 142, 119, 319, 169 ]
Electronic
1,390auxx-1
[ 137, 263, 228, 293 ]
Leisure
1,062Hovis
[ 189, 199, 453, 282 ]
Food
424Bubbaloo
[ 72, 190, 293, 312 ]
Transportation
1,271Landwind-2
[ 21, 49, 130, 351 ]
Food
2,177pizza my heart
[ 139, 64, 373, 166 ]
Food
554Casa Dragones
[ 202, 294, 304, 424 ]
Clothes
2,049obey
[ 179, 144, 335, 210 ]
Food
1,022Highlands Coffee
[ 167, 143, 342, 234 ]
Necessities
2,924xellent swiss
[ 53, 171, 356, 348 ]
Food
2,574square one organic
[ 40, 12, 275, 210 ]
Clothes
364Bob Evans Restaurants
[ 88, 88, 149, 124 ]
Food
2,204poppycock
[ 80, 128, 378, 211 ]
Food
237Bacardi
[ 127, 284, 403, 322 ]
Transportation
2,090pakistan state oil
[ 187, 160, 333, 300 ]
Sports
2,891wiffle bat and ball
[ 259, 110, 310, 172 ]
Leisure
1,869mega bloks
[ 11, 41, 137, 351 ]
Necessities
1,441brown jordan
[ 88, 149, 476, 232 ]
Medical
2,973yuyue-2
[ 145, 109, 182, 118 ]
Sports
1,956mountainsmith
[ 3, 25, 520, 349 ]
Necessities
1,679kilner
[ 11, 62, 496, 327 ]
Clothes
2,504six deuce
[ 11, 28, 393, 206 ]
Clothes
1,221Kiton
[ 42, 448, 254, 511 ]
Food
1,980nabob
[ 147, 177, 351, 209 ]
Leisure
2,623superman stars
[ 313, 219, 420, 281 ]

Dataset Card for LogoDet-3K

LogoDet-3K dataset aims on logotype (image) detection task.

Dataset Description

LogoDet-3K consists of thousand images with brands' logotypes and their bounding boxes. This dataset aims to help train logotype detection models.

  • License: MIT

Dataset Usage

You can download this dataset by the following command (make sure that you have installed Huggingface Datasets):

from datasets import load_dataset

dataset = load_dataset("PodYapolsky/LogoDet-3K")

Company ids mapping to names and vice versa

company2id = {
    name: idx
    for idx, name in enumerate(dataset["train"].features["company_name"].names)
}
id2company = {v: k for k, v in company2id.items()}

Dataset Structure

The dataset is provided in Parquet format and contains the following attributes:

{
    "image_path": [PIL.Image],
    "industy_name": [str] Industry type company's brand belongs to,
    "company_name": [int] The company id to which brand belongs,
    "bbox": [tuple[int]] bounding box in format ('xmin', 'ymin', 'xmax', 'ymax'),
}
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