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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
] |
End of preview. Expand
in Data Studio
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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