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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.

sourceHugging Faceupdated 1y agoView on Hugging Face
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Dataset Card

Dataset Info

python
import pandas as pd
pd.json_normalize(fashion_dataset.remove_columns(["image"]).to_pandas()["desc"].map(eval))

Dataset Stats For Outfit on Body Parts

python
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()
json
{'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

python
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()
json
{'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

python
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()
json
{'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}