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Datasets:
adam-narozniak
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clothing

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1
Dataset Viewer
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image
imagewidth (px)
320
1.08k
objects
sequence
width
int64
320
1.08k
height
int64
314
1.35k
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1,080
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864
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1,080
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1,080
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{ "bbox_id": [ 262 ], "category": [ 0 ], "bbox": [ [ 0, 597, 839, 1177 ] ], "area": [ 486620 ], "genre": [ "man" ] }
937
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{ "bbox_id": [ 267, 268, 269 ], "category": [ 23, 24, 6 ], "bbox": [ [ 635, 805, 751, 868 ], [ 379, 705, 527, 820 ], [ 463, 805, 660, 1070 ] ], "area": [ 7308, 17020, 52205 ], "genre": [ "man", "man", "man" ] }
1,080
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{ "bbox_id": [ 270 ], "category": [ 10 ], "bbox": [ [ 381, 776, 762, 1335 ] ], "area": [ 212979 ], "genre": [ "woman" ] }
1,080
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{ "bbox_id": [ 271, 272, 273, 274, 275 ], "category": [ 23, 23, 0, 8, 21 ], "bbox": [ [ 428, 1297, 630, 1349 ], [ 564, 1054, 740, 1244 ], [ 356, 248, 652, 527 ], [ 370, 490, 774, 874 ], [ 444, 661, 813, 1285 ] ], "area": [ 10504, 33440, 82584, 155136, 230256 ], "genre": [ "woman", "woman", "woman", "woman", "woman" ] }
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{ "bbox_id": [ 276, 277, 278, 279 ], "category": [ 23, 23, 6, 4 ], "bbox": [ [ 487, 1275, 601, 1352 ], [ 557, 1128, 648, 1316 ], [ 453, 833, 655, 1285 ], [ 303, 547, 627, 1053 ] ], "area": [ 8778, 17108, 91304, 163944 ], "genre": [ "woman", "woman", "woman", "woman" ] }
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{ "bbox_id": [ 280, 281, 282, 283 ], "category": [ 14, 4, 25, 4 ], "bbox": [ [ 129, 668, 432, 965 ], [ 10, 973, 442, 1354 ], [ 41, 893, 490, 1324 ], [ 456, 823, 992, 1353 ] ], "area": [ 89991, 164592, 193519, 284080 ], "genre": [ "woman", "woman", "woman", "man" ] }
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{ "bbox_id": [ 284, 285, 286 ], "category": [ 47, 48, 0 ], "bbox": [ [ 411, 324, 463, 358 ], [ 549, 604, 701, 671 ], [ 233, 519, 842, 1107 ] ], "area": [ 1768, 10184, 358092 ], "genre": [ "woman", "woman", "woman" ] }
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{ "bbox_id": [ 287, 288 ], "category": [ 48, 0 ], "bbox": [ [ 80, 90, 116, 117 ], [ 84, 180, 241, 312 ] ], "area": [ 972, 20724 ], "genre": [ "woman", "woman" ] }
486
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{ "bbox_id": [ 289, 290, 291, 292 ], "category": [ 23, 23, 6, 0 ], "bbox": [ [ 164, 871, 226, 938 ], [ 240, 867, 309, 935 ], [ 191, 708, 331, 885 ], [ 220, 546, 360, 727 ] ], "area": [ 4154, 4692, 24780, 25340 ], "genre": [ "man", "man", "man", "man" ] }
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{ "bbox_id": [ 293 ], "category": [ 6 ], "bbox": [ [ 635, 761, 718, 938 ] ], "area": [ 14691 ], "genre": [ "man" ] }
1,080
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{ "bbox_id": [ 294 ], "category": [ 50 ], "bbox": [ [ 255, 993, 641, 1283 ] ], "area": [ 111940 ], "genre": [ "woman" ] }
1,080
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{ "bbox_id": [ 295 ], "category": [ 14 ], "bbox": [ [ 16, 469, 424, 954 ] ], "area": [ 197880 ], "genre": [ "man" ] }
1,080
1,350
{ "bbox_id": [ 296, 297, 298, 299 ], "category": [ 23, 23, 0, 6 ], "bbox": [ [ 543, 969, 634, 1054 ], [ 442, 970, 531, 1071 ], [ 431, 353, 673, 536 ], [ 469, 516, 660, 989 ] ], "area": [ 7735, 8989, 44286, 90343 ], "genre": [ "woman", "woman", "woman", "woman" ] }
1,080
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{ "bbox_id": [ 300, 301 ], "category": [ 24, 24 ], "bbox": [ [ 745, 530, 903, 697 ], [ 397, 514, 623, 690 ] ], "area": [ 26386, 39776 ], "genre": [ null, null ] }
1,080
1,080
{ "bbox_id": [ 302 ], "category": [ 13 ], "bbox": [ [ 95, 347, 554, 548 ] ], "area": [ 92259 ], "genre": [ "woman" ] }
640
799
{ "bbox_id": [ 303, 304 ], "category": [ 14, 8 ], "bbox": [ [ 757, 601, 871, 712 ], [ 78, 745, 235, 988 ] ], "area": [ 12654, 38151 ], "genre": [ "woman", "woman" ] }
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{ "bbox_id": [ 305, 306, 307, 308 ], "category": [ 23, 23, 6, 9 ], "bbox": [ [ 571, 1169, 611, 1248 ], [ 541, 1162, 584, 1241 ], [ 516, 965, 611, 1222 ], [ 497, 761, 680, 1000 ] ], "area": [ 3160, 3397, 24415, 43737 ], "genre": [ "woman", "woman", "woman", "woman" ] }
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{ "bbox_id": [ 309 ], "category": [ 48 ], "bbox": [ [ 194, 826, 270, 879 ] ], "area": [ 4028 ], "genre": [ null ] }
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{ "bbox_id": [ 310, 311, 312 ], "category": [ 23, 6, 9 ], "bbox": [ [ 663, 759, 715, 822 ], [ 634, 585, 745, 781 ], [ 625, 430, 808, 631 ] ], "area": [ 3276, 21756, 36783 ], "genre": [ "woman", "woman", "woman" ] }
1,080
954
{ "bbox_id": [ 313, 314, 315, 316, 317 ], "category": [ 23, 23, 0, 6, 0 ], "bbox": [ [ 403, 1189, 487, 1278 ], [ 563, 1204, 639, 1308 ], [ 801, 699, 967, 857 ], [ 419, 946, 629, 1238 ], [ 412, 686, 680, 994 ] ], "area": [ 7476, 7904, 26228, 61320, 82544 ], "genre": [ "man", "man", "woman", "man", "man" ] }
1,080
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Dataset Card for Clothing Detection Dataset

This dataset is a Clothing Detection Dataset created here. However, it has English labels that correspond to the Fashionpedia. It was created to serve as an extension of the number of classes.

Supported Tasks:

  • Object detection
  • Image classification

Languages

All of the annotations were translated and adjusted from Spanish to English (and match the Fashionpedia dataset annotations).

Dataset Sources

Dataset Structure 

DatasetDict({
    train: Dataset({
        features: ['image', 'objects', 'width', 'height'],
        num_rows: 3332
    })
})

Dataset Instances

An example of the data for one image is:

{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x13E257700>, 'objects': {'bbox_id': [0, 1], 'category': [0, 4], 'bbox': [[552, 408, 623, 525], [496, 375, 691, 553]], 'area': [8307, 34710], 'genre': ['man', 'man']}, 'width': 1080, 'height': 1080}

with the type of each field defined as:

{'image': Image(decode=True, id=None), 'objects': Sequence(feature={'bbox_id': Value(dtype='int32', id=None), 'category': ClassLabel(names=['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel', 'earring', 'ring', 'bracelet', 'necklace', 'swimsuit', 'underwear', 'backpack'], id=None), 'bbox': Sequence(feature=Value(dtype='int32', id=None), length=4, id=None), 'area': Value(dtype='int32', id=None), 'genre': Value(dtype='string', id=None)}, length=-1, id=None), 'width': Value(dtype='int64', id=None), 'height': Value(dtype='int64', id=None)}

Categories

The first 46 categories correspond to the original categories from the Fashionpedia.

Translation

The original annotations were changed according to the following dictionary:

{
    "camisas": "shirt, blouse",
    "camisas ocultas debajo de la chaqueta": "shirt, blouse",
    "chaquetas": "jacket",
    "cazadoras": "jacket",
    "pantalones": "pants",
    "pantalones cortos": "shorts",
    "falda": "skirt",
    "abrigos": "coat",
    "vestidos": "dress",
    "monos": "jumpsuit",
    "gafas de sol": "glasses",
    "sombreros": "hat",
    "corbatas": "tie",
    "guantes y manopla": "glove",
    "relojes": "watch",
    "cinturones": "belt",
    "calcetines y medias": "tights, stockings",
    "zapatos": "shoe",
    "bolso": "bag, wallet",
    "carteras monederos": "bag, wallet",
    "bufandas": "scarf",
    "pendientes": "earring",
    "anillos": "ring",
    "pulseras": "bracelet",
    "collares": "necklace",
    "trajes de baño": "swimsuit",
    "ropa interior": "underwear",
    "mochilas": "backpack",
    "botas": "shoe"
}
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Size of downloaded dataset files:
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Size of the auto-converted Parquet files:
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Number of rows:
3,332