svjack/larry_lai_Theme_Outfits_Image_Desc
Dataset Info import pandas as pd pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval)) Dataset Stats For Outfit on Body Parts pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))["Tags.97"].explode().dropna().map( lambda x: x["label_name"] ).value_counts().to_dict() {'上装': 16567, '裤装': 8676, '鞋类': 8248, '裙装': 5251, '包类': 2309, '饰品(专属)': 202, '平底': 10} Dataset… See the full description on the dataset page: https://huggingface.co/datasets/svjack/larry_lai_Theme_Outfits_Image_Desc.
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Dataset Info
import pandas as pd
pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))Dataset Stats For Outfit on Body Parts
pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))["Tags.97"].explode().dropna().map(
lambda x: x["label_name"]
).value_counts().to_dict(){'上装': 16567,
'裤装': 8676,
'鞋类': 8248,
'裙装': 5251,
'包类': 2309,
'饰品(专属)': 202,
'平底': 10}Dataset Stats For Different Male Outfits
pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))["Tags.99"].explode().dropna().map(
lambda x: x["label_name"]
).value_counts().to_dict(){'男休闲裤': 2545,
'男牛仔裤': 1541,
'男T恤': 1522,
'休闲鞋男': 1371,
'男衬衫': 852,
'男卫衣': 779,
'男毛衣': 740,
'男夹克': 669,
'男羽绒服': 485,
'板鞋男': 482,
'男外套': 363,
'男大衣': 264,
'运动鞋男': 218,
'男运动裤': 210,
'男polo衫': 188,
'帆布鞋男': 171,
'男风衣': 146,
'男西装': 129,
'商务休闲鞋男': 125,
'男正装裤': 119,
'男开衫': 56,
'正装鞋': 53,
'配饰': 47,
'双肩包男': 34,
'凉鞋男': 33,
'男背心': 22,
'套装男': 20,
'男马甲': 19,
'工装鞋': 19,
'公文包': 8,
'男靴': 7,
'单肩/斜挎包': 7,
'男士手包': 7,
'拖鞋男': 5,
'人字拖男': 3,
'运动包男': 2,
'男士钱包': 1,
'邮差包': 1,
'电脑包': 1}Dataset Stats For Different Female Outfits
pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))["Tags.98"].explode().dropna().map(
lambda x: x["label_name"]
).value_counts().to_dict(){'女连衣裙': 2663,
'女半身裙': 2563,
'休闲裤': 2097,
'女衬衫': 1583,
'女T恤': 1549,
'凉鞋': 1547,
'女毛衣': 1498,
'休闲鞋': 1415,
'牛仔裤': 1276,
'高跟鞋': 1161,
'单鞋': 1142,
'女外套': 1059,
'小方包': 943,
'女大衣': 908,
'女打底衫': 850,
'女羽绒服': 781,
'女卫衣': 624,
'阔腿裤': 516,
'链条包': 433,
'手提包': 405,
'女风衣': 368,
'雪纺衫': 297,
'套装女': 219,
'女西装': 208,
'女开衫': 182,
'女背心': 160,
'水桶包': 146,
'饰品': 142,
'穆勒鞋': 131,
'帆布鞋': 124,
'双肩包': 109,
'女马甲': 102,
'贝壳包': 100,
'乐福鞋': 97,
'喇叭裤': 80,
'连体裤': 76,
'运动裤': 63,
'女靴': 62,
'松糕鞋': 58,
'女夹克': 46,
'托特包': 45,
'女背带裤': 43,
'手拿包': 32,
'鱼嘴鞋': 19,
'斜挎包': 16,
'踝靴': 14,
'豆豆鞋': 9,
'切尔西靴': 9,
'马丁靴': 7,
'坡跟鞋': 7,
'钱包': 6,
'帆布包': 3,
'人字拖': 3,
'女休闲裤': 2,
'单肩包': 1,
'雪地靴': 1,
'罗马鞋': 1,
'女斗篷': 1}