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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
] |
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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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