Dataset - DeepFashion2 数据集

Author： AIHGF
发布时间：June 19, 2019
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Categories：数据分析

论文：DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images - CVPR2019
作者：Yuying Ge, Ruimao Zhang, Xiaogang Wang, Xiaoou Tang, Ping Luo
团队：The Chinese University of Hong Kong
开源：Github - witchablenorms/DeepFashion2

DeepFashion2 数据集是一个更加综合的服装数据集，其包含了适用于服装检测、服装姿态估计、服装分割以及服装检索等场景的图片与标注数据.

1. DeepFashion2 数据集

1.1. DeepFashion2 数据集概况

DeepFashion2 共包含 491K 张图片，13 个常见服装类目，收集自电商和用户.

DeepFashion2 共包含 801K 个服装主体，图片中每个服装主体的标注信息包括：

[1] - scale 尺度:

根据服装主体相对于图像尺寸的比例，包含三种尺度：small(< 10%), moderate(10% ∼ 40%), large(> 40%) .

[2] - occlusion 遮挡

遮挡表示的是，如果服装主体的区域被头发、肢体、配饰或者其它物体所遮挡，导致的服装主体有一定的不完整.

每个服装主体根据其关键点缺失的数量进行归类为：partial occlusion(< 20% occluded keypoints), heavy
occlusion(> 50% occluded keypoints), medium occlusion(otherwise).

注：服装主体超出图片不适于 occlusion.

[3] - zoom-in 放大

服装主体被标注为 zoom-in，表示其区域超出了图片. 根据超出图片的关键点数量进行归类为：no, large(> 30%, medium.

[4] - viewpoint 视角

数据集中的服装主体被归类为 4 中视角：7% clothes that are not on people, 78% clothes on people from frontal viewpoint, 15% clothes on people from side, back viewpoint.

[5] - category 类目

根据对 DeepFashion 的 50 个类目进行归组，得到 13 个类目：short sleeve top, long sleeve top, short sleeve outwear, long sleeve outwear, vest, sling(吊带), shorts, trousers, skirt, short sleeve dress, long sleeve dress, vest dress, sling dress.

[6] - bounding box 边界框

[7] - dense landmarks 关键点

[8] - per-pixel mask 像素级 mask

[9] - style 风格

DeepFashion2 还包含了 873K 组 Commercial-Consumer 服装搭配组.

DeepFashion2 中，training 数据集 391K 张图片，validation 数据集 34K 张图片，test 数据集 67K 张图片.

例图，如：

1.2. DeepFashion2 数据集下载

谷歌Drive：https://drive.google.com/drive/folders/125F48fsMBz2EF0Cpqk6aaHet5VH399Ok?usp=sharing

解压密码：需要填写表格申请，链接 - https://docs.google.com/forms/d/e/1FAIpQLSeIoGaFfCQILrtIZPykkr8q_h9qQ5BoTYbjvf95aXbid0v2Bw/viewform?usp=sf_link

Second Workshop on Computer Vision for Fashion, Art and Design

1.3. DeepFashion2 数据集统计

Train	Validation	Test	Overall
images	390,884	33,669	67,342	491,895
bboxes	636,624	54,910	109,198	800,732
landmarks	636,624	54,910	109,198	800,732
masks	636,624	54,910	109,198	800,732
pairs	685,584	query: 12,550 gallery: 37183	query: 24,402 gallery: 75,347	873,234

2. DeepFashion2 数据组织形式

每张图片的图片名是由六位数字组成，如 000001.jpg. 其对应的 json 标注文件为 000001.json.

每个 json 标注数据的组织形式为：

|---- source，string，表示图片是来自于电商(shop)还是用户(user).

|---- pair_id，number. 同一家 shop 的图片和对应的用户所购买的图片，具有相同的 pair id.

|-------- item 1

|------------ category_name，string，服装类目名

|------------ category_id，number，对应与服装类目名.

|------------ style，number，用于区分具有相同 pair id 的图片的服装主体.具有相同 pair id 的图片的服装主体的 style numbers 不同时，其 style 是不同的，如 color, printing, logo 等.

|------------ bounding_box，[x1, y1, x2, y2]，依次为边界框的左下(lower left) 和右上(upper right) 点的坐标值.

|------------ landmarks，[x1, y1, v1, ..., xn, yn, vn]，其中 v 表示可见性：v=2 visible; v=1 occlusion; v=0 not labeled.

|------------ segmentation，[[x1, y1, ..., xn, yn], []]，其中，[x1, y1, xn, yn] 表示多边形标注，单一服装主体可能包含多个多边形(polygon)标注.

|------------ scale，number，1-small; 2-modest; 3-large.

|------------ occlusion，number，1-slight occlusion(no occlusion); 2-medium occlusion; 3-heavy occlusion.

|------------ zoom_in，number，1-no zoom-in; 2-medium zoom-in; 3-large zoom-in.

|------------ viewpoint，number，1-no wear; 2-front viewpoint; 3-side of back viewpoint.

|-------- item 2

|-------- item n

注：pair_id 和 source 是图片级的标注. 同一张图片的所有服装主体具有相同的 pair_id 和 source.

2.1. 服装类目名与对应id

category_name 和 category_id 的对应关系如下：

1 - short sleeve top
2 - long sleeve top
3 - short sleeve outwear
4 - long sleeve outwear
5 - vest
6 - sling
7 - shorts
8 - trousers
9 - skirt
10 - short sleeve dress
11 - long sleeve dress
12 - vest dress
13 -sling dress

2.2. 服装 landmarks 次序

13 种服装类目的 landmarks 和 skeletons 的表示如图.

图中的数字表示在标注文件中，每个服装类目的 landmarks 标注的词序.

13 种服装类目共定义了 294 个 landmarks.

2.3. 服装 pairs 说明

DeepFashion2 数据集中，图片是以连续的 paird_id 进行组织的，其同时包括用户和商店来源的图片. 例如：

000001.jpg(pair_id:1; from consumer), 
000002.jpg(pair_id:1; from shop),
000003.jpg(pair_id:2; from consumer),
000004.jpg(pair_id:2; from consumer),
000005.jpg(pair_id:2; from consumer), 
000006.jpg(pair_id:2; from consumer),
000007.jpg(pair_id:2; from shop),
000008.jpg(pair_id:2; from shop)
...

对于 shop 图片和 consumer 图片的两个服装主体，如果其具有相同的 style number(大于0)，且其来自具有相同 pair_id 的图片，则这两个服装主体是 positive commerical-consumer pair；否则，这两个服装主体shi negative commerical-consumer pairs. 据此，可以构建实例级的 positive pairs 和 negative pairs，以用于训练.

例如下图中：前三张图片来自 consumers，最后两张来自于 shops. 这五张图片具有相同的 pair_id. 橙色框的服装主体具有相同的 style: 1；绿色框的服装主体具有相同的 style: 2. 其它未画出边界框的服装主体的 style 是 0，其不能用于构建 positive commerical-consumer pairs. 一组 positive commerical-consumer pair 是在第一张图片中标注的 short sleeve top 和最后一张图片中标注的 short sleeve top.

因此，DeepFashion2 数据集可以很灵活的用于构建实例级(instance-level)的pairs.

2.4. DeepFashion2 数据集划分

下载后的数据集组织形式为：

|---- train

|-------- image 训练图片数据

|-------- annos 训练标注数据

|---- validation

|-------- image 验证图片数据

|-------- annos 验证标注数据

|---- test

|-------- image 测试图片数据

[1] - DeepFashion2 数据集中，每张图片的图片名是由六位数字组成，如 000001.jpg；其对应的 json 标注文件为 000001.json.

[2] - DeepFashion2 数据集提供了将数据组织为 COCO 格式的脚本 - deepfashion2_to_coco.py.

[3] - DeepFashion2 的 validation 数据集提供了图片级的数据信息 - keypoints_val_information.json，retrieval_val_consumer_information.json 和 retrieval_val_shop_information.json(前 10844 张图片来自 consumers ，后 20681 张图片来自 shops). 对于服装检测任务和语义分割任务，也可以采用 keypoints_val_information.json.

[4] - DeepFashion2 数据集还提供了 keypoints_val_vis.json, keypoints_val_vis_and_occ.json, val_query.json 和 val_gallery.json，以用于 validation 数据集的评测.

DeepFashion2 - Evaluation

[5] - DeepFashion2 的 test 数据集提供了图片集的数据信息 - keypoints_test_information.json, retrieval_test_consumer_information.json 和 retrieval_test_shop_information.json(前 20681 张图片来自 consumers，后 41984 张图片来自于 shops).

2.5. deepfashion2_to_coco.py

deepfashion2_to_coco.py

import json
from PIL import Image
import numpy as np


dataset = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

dataset['categories'].append({
    'id': 1,
    'name': "short_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 2,
    'name': "long_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 3,
    'name': "short_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 4,
    'name': "long_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 5,
    'name': "vest",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 6,
    'name': "sling",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 7,
    'name': "shorts",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 8,
    'name': "trousers",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 9,
    'name': "skirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 10,
    'name': "short_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 11,
    'name': "long_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 12,
    'name': "vest_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 13,
    'name': "sling_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})

sub_index = 0 # the index of ground truth instance
for num in range(1,num_images+1):
    json_name = '/path/to/val_annos/' + str(num).zfill(6)+'.json'
    image_name = '/path/to/val/' + str(num).zfill(6)+'.jpg'

    if (num>=0):
        imag = Image.open(image_name)
        width, height = imag.size
        with open(json_name, 'r') as f:
            temp = json.loads(f.read())
            pair_id = temp['pair_id']

            dataset['images'].append({
                'coco_url': '',
                'date_captured': '',
                'file_name': str(num).zfill(6) + '.jpg',
                'flickr_url': '',
                'id': num,
                'license': 0,
                'width': width,
                'height': height
            })
            for i in temp:
                if i == 'source' or i=='pair_id':
                    continue
                else:
                    points = np.zeros(294 * 3)
                    sub_index = sub_index + 1
                    box = temp[i]['bounding_box']
                    w = box[2]-box[0]
                    h = box[3]-box[1]
                    x_1 = box[0]
                    y_1 = box[1]
                    bbox=[x_1,y_1,w,h]
                    cat = temp[i]['category_id']
                    style = temp[i]['style']
                    seg = temp[i]['segmentation']
                    landmarks = temp[i]['landmarks']

                    points_x = landmarks[0::3]
                    points_y = landmarks[1::3]
                    points_v = landmarks[2::3]
                    points_x = np.array(points_x)
                    points_y = np.array(points_y)
                    points_v = np.array(points_v)

                    if cat == 1:
                        for n in range(0, 25):
                            points[3 * n] = points_x[n]
                            points[3 * n + 1] = points_y[n]
                            points[3 * n + 2] = points_v[n]
                    elif cat ==2:
                        for n in range(25, 58):
                            points[3 * n] = points_x[n - 25]
                            points[3 * n + 1] = points_y[n - 25]
                            points[3 * n + 2] = points_v[n - 25]
                    elif cat ==3:
                        for n in range(58, 89):
                            points[3 * n] = points_x[n - 58]
                            points[3 * n + 1] = points_y[n - 58]
                            points[3 * n + 2] = points_v[n - 58]
                    elif cat == 4:
                        for n in range(89, 128):
                            points[3 * n] = points_x[n - 89]
                            points[3 * n + 1] = points_y[n - 89]
                            points[3 * n + 2] = points_v[n - 89]
                    elif cat == 5:
                        for n in range(128, 143):
                            points[3 * n] = points_x[n - 128]
                            points[3 * n + 1] = points_y[n - 128]
                            points[3 * n + 2] = points_v[n - 128]
                    elif cat == 6:
                        for n in range(143, 158):
                            points[3 * n] = points_x[n - 143]
                            points[3 * n + 1] = points_y[n - 143]
                            points[3 * n + 2] = points_v[n - 143]
                    elif cat == 7:
                        for n in range(158, 168):
                            points[3 * n] = points_x[n - 158]
                            points[3 * n + 1] = points_y[n - 158]
                            points[3 * n + 2] = points_v[n - 158]
                    elif cat == 8:
                        for n in range(168, 182):
                            points[3 * n] = points_x[n - 168]
                            points[3 * n + 1] = points_y[n - 168]
                            points[3 * n + 2] = points_v[n - 168]
                    elif cat == 9:
                        for n in range(182, 190):
                            points[3 * n] = points_x[n - 182]
                            points[3 * n + 1] = points_y[n - 182]
                            points[3 * n + 2] = points_v[n - 182]
                    elif cat == 10:
                        for n in range(190, 219):
                            points[3 * n] = points_x[n - 190]
                            points[3 * n + 1] = points_y[n - 190]
                            points[3 * n + 2] = points_v[n - 190]
                    elif cat == 11:
                        for n in range(219, 256):
                            points[3 * n] = points_x[n - 219]
                            points[3 * n + 1] = points_y[n - 219]
                            points[3 * n + 2] = points_v[n - 219]
                    elif cat == 12:
                        for n in range(256, 275):
                            points[3 * n] = points_x[n - 256]
                            points[3 * n + 1] = points_y[n - 256]
                            points[3 * n + 2] = points_v[n - 256]
                    elif cat == 13:
                        for n in range(275, 294):
                            points[3 * n] = points_x[n - 275]
                            points[3 * n + 1] = points_y[n - 275]
                            points[3 * n + 2] = points_v[n - 275]
                    num_points = len(np.where(points_v > 0)[0])

                    dataset['annotations'].append({
                        'area': w*h,
                        'bbox': bbox,
                        'category_id': cat,
                        'id': sub_index,
                        'pair_id': pair_id,
                        'image_id': num,
                        'iscrowd': 0,
                        'style': style,
                        'num_keypoints':num_points,
                        'keypoints':points.tolist(),
                        'segmentation': seg,
                    })


json_name = '/path/to/deepfashion2.json'
with open(json_name, 'w') as f:
  json.dump(dataset, f)

3. DeepFashion2 数据集对比

[1] - Dataset - DeepFashion 服装数据集 - AIUAI
[2] - ModaNet 基于多边形标注的大规模街拍服装数据集 - AIUAI

[1] - 对比 DeepFashion 数据集：

图：(a) DeepFashion 数据集，每张图片只标注了单个服装主体，一般是 4-8 个关键点. 边界框是根据标注的关键点估计得到，噪声比较多. (b) DeepFashion2 数据集，每张图片最少标注一个服装主体，最多 7 个主体. 每个服装主体手工标注了边界框、mask、关键点(每个主体平均 20 个关键点) 以及 commercial-customer 图片对.

[2] - 对比多个服装相关数据集

DeepFashion2 数据集的标注至少是 DeepFashion 的 3.5X 倍，是ModaNet 的 6.7X 倍，是FashionAI 的 8X 倍.

Last modification：June 20th, 2019 at 11:06 am

114 comments

ooochen
April 13th, 2023 at 11:31 am

求数据集密码，感谢

Reply
ys
February 9th, 2023 at 04:18 pm

670667626@qq.com解压密码谢谢

Reply
stephenlee
January 28th, 2022 at 10:49 am

求解压密码，871826082@qq.com, 感谢。

Reply
harry_koney
January 4th, 2022 at 11:48 am

train数据只有191961，好像没有39w，请问大家也是这样么？另外valid数据是32153，test数据是62629

Reply
骄傲的贫僧
January 4th, 2022 at 11:45 am

收不到官方密码，求解压密码~感谢~
jxgu1016 AT gmail.com

Reply
harry_koney
December 25th, 2021 at 10:08 am

楼主，求数据集的解压密码＞﹏＜，感谢！！！

Reply
busyboay
September 14th, 2021 at 07:59 pm

大佬，求解压密码，583515094@qq.com

Reply
AS
August 24th, 2021 at 11:04 pm

869293@qq.com 大哥，请告知解压密码！！！！

Reply
yy
July 16th, 2021 at 03:22 pm

求密码,表格老是填不上

Reply
小有名气的道士
July 6th, 2021 at 12:07 pm

研究生搞人体姿态生成，求发送到邮箱解压密码。~多谢

Reply
Koi
June 22nd, 2021 at 10:18 am

求大佬给份解压密码

Reply
diaosisi
June 8th, 2021 at 02:02 pm

跪求数据集密码,27626305@qq.com，谢谢

Reply
zion
March 30th, 2021 at 02:34 pm

跪求求数据集密码，非常感谢

Reply
ideaplat
March 4th, 2021 at 09:50 pm

同需求数据集密码，非常感谢

Reply
1. AIHGF
  March 5th, 2021 at 08:51 am
  
  @ideaplat
  
  已发送到邮箱.
  
  Reply
姜姜姜
February 2nd, 2021 at 04:08 pm

求数据集密码。谢谢

Reply
Sam
January 28th, 2021 at 09:35 am

您好，可以把解压密码发到邮箱里面吗？谢谢啦

Reply
1. AIHGF
  January 28th, 2021 at 06:42 pm
  
  @Sam
  
  已发送到邮箱.
  
  Reply
林铎
January 21st, 2021 at 07:56 pm

求密码，博主

Reply
1. AIHGF
  January 22nd, 2021 at 09:29 am
  
  @林铎
  
  已发送到邮箱
  
  Reply
Janus
January 21st, 2021 at 02:16 pm

求数据集密码感谢

Reply
1. AIHGF
  January 21st, 2021 at 06:38 pm
  
  @Janus
  
  已发送到邮箱.
  
  Reply
XinBinup
January 9th, 2021 at 12:45 am

博主您好可以分享下数据集解压密码吗万分感谢！

Reply
1. AIHGF
  January 9th, 2021 at 09:39 am
  
  @XinBinup
  
  已发送到邮箱.
  
  Reply
  1. XinBinup
    January 9th, 2021 at 10:59 am
    
    @AIHGF
    
    谢谢博主！
    
    Reply
DerryL
January 6th, 2021 at 11:35 am

求解压密码，感谢！

Reply
1. AIHGF
  January 6th, 2021 at 03:34 pm
  
  @DerryL
  
  已发送到邮箱.
  
  Reply
leon
January 4th, 2021 at 07:12 pm

楼主求解压密码

Reply
1. leon
  January 4th, 2021 at 08:05 pm
  
  @leon
  
  邮箱patrick.phzhou@gmail.com, 谢谢楼主！！
  
  Reply
  1. AIHGF
    January 5th, 2021 at 09:24 am
    
    @leon
    
    已发送到邮箱.
    
    Reply
Heaven
December 23rd, 2020 at 12:47 pm

求解压密码谢谢，582720969@qq.com

Reply
1. AIHGF
  December 23rd, 2020 at 03:10 pm
  
  @Heaven
  
  已发送到邮箱.
  
  Reply
  1. Heaven
    December 23rd, 2020 at 04:30 pm
    
    @AIHGF
    
    非常谢谢您
    
    Reply
Wastoon
December 13th, 2020 at 05:47 pm

感谢(☆ω☆)

Reply
Wastoon
December 13th, 2020 at 03:51 pm

您好，可以提供下解压密码给我吗，谢谢啦

Reply
1. AIHGF
  December 13th, 2020 at 05:29 pm
  
  @Wastoon
  
  已发送到邮箱.
  
  Reply
zyd
November 22nd, 2020 at 04:26 pm

学习，解压密码是否分享一下？

Reply
1. AIHGF
  November 23rd, 2020 at 08:43 am
  
  @zyd
  
  已发送到邮箱.
  
  Reply
susan
November 11th, 2020 at 02:56 pm

求解压密码，谢谢

Reply
1. AIHGF
  November 11th, 2020 at 03:54 pm
  
  @susan
  
  已发送到邮箱.
  
  Reply
gumei
October 1st, 2020 at 06:05 pm

求DeepFashion2数据集密码，万分感谢！

Reply
1. AIHGF
  October 1st, 2020 at 09:03 pm
  
  @gumei
  
  已发送到邮箱.
  
  Reply
  1. huangyan
    December 1st, 2020 at 08:52 am
    
    @AIHGF
    
    博主您好，能够给一下解压密码么~~谢谢
    
    Reply
    
    AIHGF
    December 1st, 2020 at 09:31 am
    
    @huangyan
    
    已发送到邮箱.
    
    Reply
    
    huangyan
    December 1st, 2020 at 10:09 am
    
    @AIHGF
    
    已收到，谢谢
    
    Reply
菜姬
September 22nd, 2020 at 09:10 pm

楼主可以分享一下解压密码么？用于本科课程学习蟹蟹~

Reply
1. AIHGF
  September 23rd, 2020 at 09:17 am
  
  @菜姬
  
  已发送到邮箱.
  
  Reply
showery
September 8th, 2020 at 11:38 am

求数据解压密码:
yfhall@163.com

Reply
1. AIHGF
  September 8th, 2020 at 09:51 pm
  
  @showery
  
  已发送到邮箱.
  
  Reply
summerwang
September 5th, 2020 at 04:38 pm

求数据集解压密码感谢！

Reply
1. AIHGF
  September 6th, 2020 at 08:22 pm
  
  @summerwang
  
  已发送到邮箱.
  
  Reply
mcrep
August 28th, 2020 at 02:46 pm

求数据集的解压密码＞﹏＜

Reply
1. AIHGF
  August 28th, 2020 at 09:20 pm
  
  @mcrep
  
  已发送到邮箱.
  
  Reply
丫丫
August 16th, 2020 at 01:45 pm

楼主能给个解压密码吗

Reply
1. AIHGF
  August 17th, 2020 at 09:30 am
  
  @丫丫
  
  已发送到邮箱.
  
  Reply
CodingP
August 11th, 2020 at 03:39 pm

求一个密码博主！感谢！ chengmeng0115@gmail.com

Reply
1. AIHGF
  August 11th, 2020 at 04:27 pm
  
  @CodingP
  
  已发送到邮箱.
  
  Reply
liushengzhong
August 7th, 2020 at 05:54 pm

求数据集的解压密码，1500560975@qq.com

Reply
1. AIHGF
  August 8th, 2020 at 04:38 pm
  
  @liushengzhong
  
  已发送到邮箱.
  
  Reply
zhou
July 22nd, 2020 at 11:49 am

求解压密码：qq: 994329541@qq.com

Reply
1. AIHGF
  July 22nd, 2020 at 01:12 pm
  
  @zhou
  
  已发送到邮箱.
  
  Reply
bxwx123
July 14th, 2020 at 05:36 pm

求解压密码，谢谢 497316113@qq.com

Reply
1. AIHGF
  July 14th, 2020 at 05:45 pm
  
  @bxwx123
  
  已发送到邮箱.
  
  Reply
飞哥传书
July 8th, 2020 at 04:41 pm

求解压密码，感谢大佬

Reply
1. AIHGF
  July 8th, 2020 at 05:19 pm
  
  @飞哥传书
  
  已发送到邮箱.
  
  Reply
vitowzy
July 7th, 2020 at 11:04 am

博主大佬，求解压密码

Reply
1. AIHGF
  July 7th, 2020 at 01:24 pm
  
  @vitowzy
  
  已发送到邮箱.
  
  Reply
  1. vitowzy
    July 7th, 2020 at 03:14 pm
    
    @AIHGF
    
    已收到，谢谢
    
    Reply
chenheipi
July 2nd, 2020 at 09:30 pm

博主您好，能够给一下解压密码么~~谢谢chenhehong@126.com

Reply
1. AIHGF
  July 2nd, 2020 at 09:59 pm
  
  @chenheipi
  
  已发送到邮箱.
  
  Reply
  1. chenheipi
    July 3rd, 2020 at 12:29 am
    
    @AIHGF
    
    收到了，十分感谢！！！
    
    Reply
安徒生
June 30th, 2020 at 12:05 am

求解压密码，万分感谢！！！OωO

Reply
1. AIHGF
  June 30th, 2020 at 09:05 am
  
  @安徒生
  
  已发送到邮箱.
  
  Reply
hugh
June 28th, 2020 at 11:41 am

求数据的解压密码，谢谢！

Reply
1. AIHGF
  June 28th, 2020 at 12:27 pm
  
  @hugh
  
  已发送到邮箱.
  
  Reply
xdker
June 27th, 2020 at 03:23 pm

求密码谢谢

Reply
1. AIHGF
  June 28th, 2020 at 09:20 am
  
  @xdker
  
  已发送到邮箱.
  
  Reply
anna
June 23rd, 2020 at 05:41 pm

@AIHGF
同求数据集密码，非常感谢!

Reply
1. AIHGF
  June 23rd, 2020 at 06:22 pm
  
  @anna
  
  已发送到邮箱.
  
  Reply
loyiar
June 19th, 2020 at 07:51 pm

你好，可以发一下数据集密码吗？谢谢。

Reply
1. AIHGF
  June 20th, 2020 at 08:59 am
  
  @loyiar
  
  已发送到邮箱.
  
  Reply
  1. loyiar
    June 20th, 2020 at 09:34 am
    
    @AIHGF
    
    已经收到，谢谢。
    
    Reply
wyllmein2000
June 17th, 2020 at 04:55 pm

同求数据集密码，十分感谢！

Reply
1. AIHGF
  June 18th, 2020 at 09:50 am
  
  @wyllmein2000
  
  已发送到邮箱.
  
  Reply
  1. wyllmein2000
    June 18th, 2020 at 10:53 am
    
    @AIHGF
    
    多谢多谢！！！
    
    Reply
赵子龙
May 29th, 2020 at 04:55 pm

谁复现了训练的代码,能分享一下么?

Reply
哈哈
May 4th, 2020 at 04:54 pm

博主，求数据集密码

Reply
1. AIHGF
  May 4th, 2020 at 08:40 pm
  
  @哈哈
  
  已发送到邮箱
  
  Reply
chenchen
April 25th, 2020 at 10:37 pm

您好，可以把解压密码发到邮箱里面吗？谢谢啦

Reply
asia
April 16th, 2020 at 08:49 pm

收不到官方密码，求解压密码~感谢~

Reply
1. AIHGF
  April 17th, 2020 at 09:35 pm
  
  @asia
  
  已发送到邮箱.
  
  Reply
ym
April 11th, 2020 at 07:30 pm

一个星期还没有发密码，请问能发一下数据集密码吗？

Reply
1. AIHGF
  April 11th, 2020 at 07:47 pm
  
  @ym
  
  已发送到邮箱.
  
  Reply
哎呀咋回事啊
March 24th, 2020 at 10:36 am

您好请问可以发下数据集的密码吗？？？万分感谢

Reply
1. AIHGF
  March 24th, 2020 at 12:37 pm
  
  @哎呀咋回事啊
  
  已发送到邮箱.
  
  Reply
ainovice
March 2nd, 2020 at 06:23 pm

求deep fashion2数据集，可以分享一下吗？

Reply
1. AIHGF
  March 2nd, 2020 at 09:25 pm
  
  @ainovice
  
  链接: https://pan.baidu.com/s/10gITxlDA1tQQStiwAXtYwA 提取码: arja
  
  Reply
  1. 赵子龙
    May 29th, 2020 at 10:46 am
    
    @AIHGF
    
    博主你好,非常感谢你的分享精神,我是深入学习刚刚入门,接到了一个衣物分割分类的任务,能够发送一下deepfashion的数据以及您作训练的推理的代码,帮助我入门.万分感谢
    
    Reply
    
    阿龙
    June 2nd, 2020 at 08:51 pm
    
    @赵子龙
    
    同求训练模块的复现代码
    
    Reply
    
    AIHGF
    May 29th, 2020 at 09:29 pm
    
    @赵子龙
    
    deepfashion 数据集已发送到邮箱.
    
    Reply
  2. ainovice
    March 17th, 2020 at 09:36 pm
    
    @AIHGF
    
    你在deepfashion2上做过训练么？可以加个微信不？
    
    Reply
    
    AIHGF
    March 18th, 2020 at 08:57 am
    
    @ainovice
    
    训练过.
    
    Reply
  3. ainovice
    March 4th, 2020 at 08:33 am
    
    @AIHGF
    
    多谢分享~博主有ICCV2019deepfashion2workshop的论文吗？找不到，求分享~
    
    Reply
大头鸭
February 22nd, 2020 at 03:33 pm

噗，应该是博主~

Reply
1. Andy
  June 20th, 2020 at 11:16 am
  
  @大头鸭
  
  求数据集密码，非常感谢！
  
  Reply
  1. AIHGF
    June 20th, 2020 at 12:09 pm
    
    @Andy
    
    已发送到邮箱.
    
    Reply
2. AIHGF
  February 22nd, 2020 at 04:46 pm
  
  @大头鸭
  
  已发送到邮箱.
  
  Reply
Summer
December 13th, 2019 at 10:38 am

获取密码的网址打不开⌇●﹏●⌇，求数据集密码，非常感谢

Reply
1. AIHGF
  December 13th, 2019 at 11:19 am
  
  @Summer
  
  已发送到邮箱.
  
  Reply
  1. ZEX
    February 18th, 2020 at 12:13 pm
    
    @AIHGF
    
    同需求数据集密码，非常感谢
    
    Reply
    
    AIHGF
    February 18th, 2020 at 07:56 pm
    
    @ZEX
    
    已发送到邮箱.
    
    Reply
    
    大头鸭
    February 22nd, 2020 at 03:32 pm
    
    @AIHGF
    
    博士也请帮忙给我发一下数据集密码，表格网站打不开.....非常感谢(･ω･)ﾉ
    
    Reply
ranjiewen
November 24th, 2019 at 05:52 pm

train数据只有191962,剩下的呢OωO

Reply
alex
July 13th, 2019 at 07:55 pm

刚要转DL的初学者，求数据集密码，感谢

Reply

Dataset - DeepFashion2 数据集

AIHGF • 2019 年 06 月 19 日

论文：DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images - CVPR2019
作者：Yuying Ge, Ruimao Zhang, Xiaogang Wang, Xiaoou Tang, Ping Luo
团队：The Chinese University of Hong Kong
开源：Github - witchablenorms/DeepFashion2

DeepFashion2 数据集是一个更加综合的服装数据集，其包含了适用于服装检测、服装姿态估计、服装分割以及服装检索等场景的图片与标注数据.

1. DeepFashion2 数据集

1.1. DeepFashion2 数据集概况

DeepFashion2 共包含 491K 张图片，13 个常见服装类目，收集自电商和用户.

DeepFashion2 共包含 801K 个服装主体，图片中每个服装主体的标注信息包括：

[1] - scale 尺度:

根据服装主体相对于图像尺寸的比例，包含三种尺度：small(< 10%), moderate(10% ∼ 40%), large(> 40%) .

[2] - occlusion 遮挡

遮挡表示的是，如果服装主体的区域被头发、肢体、配饰或者其它物体所遮挡，导致的服装主体有一定的不完整.

每个服装主体根据其关键点缺失的数量进行归类为：partial occlusion(< 20% occluded keypoints), heavy
occlusion(> 50% occluded keypoints), medium occlusion(otherwise).

注：服装主体超出图片不适于 occlusion.

[3] - zoom-in 放大

服装主体被标注为 zoom-in，表示其区域超出了图片. 根据超出图片的关键点数量进行归类为：no, large(> 30%, medium.

[4] - viewpoint 视角

数据集中的服装主体被归类为 4 中视角：7% clothes that are not on people, 78% clothes on people from frontal viewpoint, 15% clothes on people from side, back viewpoint.

[5] - category 类目

[6] - bounding box 边界框

[7] - dense landmarks 关键点

[8] - per-pixel mask 像素级 mask

[9] - style 风格

DeepFashion2 还包含了 873K 组 Commercial-Consumer 服装搭配组.

DeepFashion2 中，training 数据集 391K 张图片，validation 数据集 34K 张图片，test 数据集 67K 张图片.

例图，如：

1.2. DeepFashion2 数据集下载

谷歌Drive：https://drive.google.com/drive/folders/125F48fsMBz2EF0Cpqk6aaHet5VH399Ok?usp=sharing

解压密码：需要填写表格申请，链接 - https://docs.google.com/forms/d/e/1FAIpQLSeIoGaFfCQILrtIZPykkr8q_h9qQ5BoTYbjvf95aXbid0v2Bw/viewform?usp=sf_link

Second Workshop on Computer Vision for Fashion, Art and Design

1.3. DeepFashion2 数据集统计

Train	Validation	Test	Overall
images	390,884	33,669	67,342	491,895
bboxes	636,624	54,910	109,198	800,732
landmarks	636,624	54,910	109,198	800,732
masks	636,624	54,910	109,198	800,732
pairs	685,584	query: 12,550 gallery: 37183	query: 24,402 gallery: 75,347	873,234

2. DeepFashion2 数据组织形式

每张图片的图片名是由六位数字组成，如 000001.jpg. 其对应的 json 标注文件为 000001.json.

每个 json 标注数据的组织形式为：

|---- source，string，表示图片是来自于电商(shop)还是用户(user).

|---- pair_id，number. 同一家 shop 的图片和对应的用户所购买的图片，具有相同的 pair id.

|-------- item 1

|------------ category_name，string，服装类目名

|------------ category_id，number，对应与服装类目名.

|------------ bounding_box，[x1, y1, x2, y2]，依次为边界框的左下(lower left) 和右上(upper right) 点的坐标值.

|------------ landmarks，[x1, y1, v1, ..., xn, yn, vn]，其中 v 表示可见性：v=2 visible; v=1 occlusion; v=0 not labeled.

|------------ segmentation，[[x1, y1, ..., xn, yn], []]，其中，[x1, y1, xn, yn] 表示多边形标注，单一服装主体可能包含多个多边形(polygon)标注.

|------------ scale，number，1-small; 2-modest; 3-large.

|------------ occlusion，number，1-slight occlusion(no occlusion); 2-medium occlusion; 3-heavy occlusion.

|------------ zoom_in，number，1-no zoom-in; 2-medium zoom-in; 3-large zoom-in.

|------------ viewpoint，number，1-no wear; 2-front viewpoint; 3-side of back viewpoint.

|-------- item 2

|-------- item n

注：pair_id 和 source 是图片级的标注. 同一张图片的所有服装主体具有相同的 pair_id 和 source.

2.1. 服装类目名与对应id

category_name 和 category_id 的对应关系如下：

1 - short sleeve top
2 - long sleeve top
3 - short sleeve outwear
4 - long sleeve outwear
5 - vest
6 - sling
7 - shorts
8 - trousers
9 - skirt
10 - short sleeve dress
11 - long sleeve dress
12 - vest dress
13 -sling dress

2.2. 服装 landmarks 次序

13 种服装类目的 landmarks 和 skeletons 的表示如图.

图中的数字表示在标注文件中，每个服装类目的 landmarks 标注的词序.

13 种服装类目共定义了 294 个 landmarks.

2.3. 服装 pairs 说明

DeepFashion2 数据集中，图片是以连续的 paird_id 进行组织的，其同时包括用户和商店来源的图片. 例如：

000001.jpg(pair_id:1; from consumer), 
000002.jpg(pair_id:1; from shop),
000003.jpg(pair_id:2; from consumer),
000004.jpg(pair_id:2; from consumer),
000005.jpg(pair_id:2; from consumer), 
000006.jpg(pair_id:2; from consumer),
000007.jpg(pair_id:2; from shop),
000008.jpg(pair_id:2; from shop)
...

因此，DeepFashion2 数据集可以很灵活的用于构建实例级(instance-level)的pairs.

2.4. DeepFashion2 数据集划分

下载后的数据集组织形式为：

|---- train

|-------- image 训练图片数据

|-------- annos 训练标注数据

|---- validation

|-------- image 验证图片数据

|-------- annos 验证标注数据

|---- test

|-------- image 测试图片数据

[1] - DeepFashion2 数据集中，每张图片的图片名是由六位数字组成，如 000001.jpg；其对应的 json 标注文件为 000001.json.

[2] - DeepFashion2 数据集提供了将数据组织为 COCO 格式的脚本 - deepfashion2_to_coco.py.

[4] - DeepFashion2 数据集还提供了 keypoints_val_vis.json, keypoints_val_vis_and_occ.json, val_query.json 和 val_gallery.json，以用于 validation 数据集的评测.

DeepFashion2 - Evaluation

2.5. deepfashion2_to_coco.py

deepfashion2_to_coco.py

import json
from PIL import Image
import numpy as np


dataset = {
    "info": {},
    "licenses": [],
    "images": [],
    "annotations": [],
    "categories": []
}

dataset['categories'].append({
    'id': 1,
    'name': "short_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 2,
    'name': "long_sleeved_shirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 3,
    'name': "short_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 4,
    'name': "long_sleeved_outwear",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 5,
    'name': "vest",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 6,
    'name': "sling",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 7,
    'name': "shorts",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 8,
    'name': "trousers",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 9,
    'name': "skirt",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 10,
    'name': "short_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 11,
    'name': "long_sleeved_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 12,
    'name': "vest_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})
dataset['categories'].append({
    'id': 13,
    'name': "sling_dress",
    'supercategory': "clothes",
    'keypoints': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61', '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73', '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85', '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97', '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108', '109', '110', '111', '112', '113', '114', '115', '116', '117', '118', '119', '120', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '142', '143', '144', '145', '146', '147', '148', '149', '150', '151', '152', '153', '154', '155', '156', '157', '158', '159', '160', '161', '162', '163', '164', '165', '166', '167', '168', '169', '170', '171', '172', '173', '174', '175', '176', '177', '178', '179', '180', '181', '182', '183', '184', '185', '186', '187', '188', '189', '190', '191', '192', '193', '194', '195', '196', '197', '198', '199', '200', '201', '202', '203', '204', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '221', '222', '223', '224', '225', '226', '227', '228', '229', '230', '231', '232', '233', '234', '235', '236', '237', '238', '239', '240', '241', '242', '243', '244', '245', '246', '247', '248', '249', '250', '251', '252', '253', '254', '255', '256', '257', '258', '259', '260', '261', '262', '263', '264', '265', '266', '267', '268', '269', '270', '271', '272', '273', '274', '275', '276', '277', '278', '279', '280', '281', '282', '283', '284', '285', '286', '287', '288', '289', '290', '291', '292', '293', '294'],
    'skeleton': []
})

sub_index = 0 # the index of ground truth instance
for num in range(1,num_images+1):
    json_name = '/path/to/val_annos/' + str(num).zfill(6)+'.json'
    image_name = '/path/to/val/' + str(num).zfill(6)+'.jpg'

    if (num>=0):
        imag = Image.open(image_name)
        width, height = imag.size
        with open(json_name, 'r') as f:
            temp = json.loads(f.read())
            pair_id = temp['pair_id']

            dataset['images'].append({
                'coco_url': '',
                'date_captured': '',
                'file_name': str(num).zfill(6) + '.jpg',
                'flickr_url': '',
                'id': num,
                'license': 0,
                'width': width,
                'height': height
            })
            for i in temp:
                if i == 'source' or i=='pair_id':
                    continue
                else:
                    points = np.zeros(294 * 3)
                    sub_index = sub_index + 1
                    box = temp[i]['bounding_box']
                    w = box[2]-box[0]
                    h = box[3]-box[1]
                    x_1 = box[0]
                    y_1 = box[1]
                    bbox=[x_1,y_1,w,h]
                    cat = temp[i]['category_id']
                    style = temp[i]['style']
                    seg = temp[i]['segmentation']
                    landmarks = temp[i]['landmarks']

                    points_x = landmarks[0::3]
                    points_y = landmarks[1::3]
                    points_v = landmarks[2::3]
                    points_x = np.array(points_x)
                    points_y = np.array(points_y)
                    points_v = np.array(points_v)

                    if cat == 1:
                        for n in range(0, 25):
                            points[3 * n] = points_x[n]
                            points[3 * n + 1] = points_y[n]
                            points[3 * n + 2] = points_v[n]
                    elif cat ==2:
                        for n in range(25, 58):
                            points[3 * n] = points_x[n - 25]
                            points[3 * n + 1] = points_y[n - 25]
                            points[3 * n + 2] = points_v[n - 25]
                    elif cat ==3:
                        for n in range(58, 89):
                            points[3 * n] = points_x[n - 58]
                            points[3 * n + 1] = points_y[n - 58]
                            points[3 * n + 2] = points_v[n - 58]
                    elif cat == 4:
                        for n in range(89, 128):
                            points[3 * n] = points_x[n - 89]
                            points[3 * n + 1] = points_y[n - 89]
                            points[3 * n + 2] = points_v[n - 89]
                    elif cat == 5:
                        for n in range(128, 143):
                            points[3 * n] = points_x[n - 128]
                            points[3 * n + 1] = points_y[n - 128]
                            points[3 * n + 2] = points_v[n - 128]
                    elif cat == 6:
                        for n in range(143, 158):
                            points[3 * n] = points_x[n - 143]
                            points[3 * n + 1] = points_y[n - 143]
                            points[3 * n + 2] = points_v[n - 143]
                    elif cat == 7:
                        for n in range(158, 168):
                            points[3 * n] = points_x[n - 158]
                            points[3 * n + 1] = points_y[n - 158]
                            points[3 * n + 2] = points_v[n - 158]
                    elif cat == 8:
                        for n in range(168, 182):
                            points[3 * n] = points_x[n - 168]
                            points[3 * n + 1] = points_y[n - 168]
                            points[3 * n + 2] = points_v[n - 168]
                    elif cat == 9:
                        for n in range(182, 190):
                            points[3 * n] = points_x[n - 182]
                            points[3 * n + 1] = points_y[n - 182]
                            points[3 * n + 2] = points_v[n - 182]
                    elif cat == 10:
                        for n in range(190, 219):
                            points[3 * n] = points_x[n - 190]
                            points[3 * n + 1] = points_y[n - 190]
                            points[3 * n + 2] = points_v[n - 190]
                    elif cat == 11:
                        for n in range(219, 256):
                            points[3 * n] = points_x[n - 219]
                            points[3 * n + 1] = points_y[n - 219]
                            points[3 * n + 2] = points_v[n - 219]
                    elif cat == 12:
                        for n in range(256, 275):
                            points[3 * n] = points_x[n - 256]
                            points[3 * n + 1] = points_y[n - 256]
                            points[3 * n + 2] = points_v[n - 256]
                    elif cat == 13:
                        for n in range(275, 294):
                            points[3 * n] = points_x[n - 275]
                            points[3 * n + 1] = points_y[n - 275]
                            points[3 * n + 2] = points_v[n - 275]
                    num_points = len(np.where(points_v > 0)[0])

                    dataset['annotations'].append({
                        'area': w*h,
                        'bbox': bbox,
                        'category_id': cat,
                        'id': sub_index,
                        'pair_id': pair_id,
                        'image_id': num,
                        'iscrowd': 0,
                        'style': style,
                        'num_keypoints':num_points,
                        'keypoints':points.tolist(),
                        'segmentation': seg,
                    })


json_name = '/path/to/deepfashion2.json'
with open(json_name, 'w') as f:
  json.dump(dataset, f)

3. DeepFashion2 数据集对比

[1] - Dataset - DeepFashion 服装数据集 - AIUAI
[2] - ModaNet 基于多边形标注的大规模街拍服装数据集 - AIUAI

[1] - 对比 DeepFashion 数据集：

[2] - 对比多个服装相关数据集

DeepFashion2 数据集的标注至少是 DeepFashion 的 3.5X 倍，是ModaNet 的 6.7X 倍，是FashionAI 的 8X 倍.

Dataset - DeepFashion2 数据集

1. DeepFashion2 数据集

1.1. DeepFashion2 数据集概况

1.2. DeepFashion2 数据集下载

1.3. DeepFashion2 数据集统计

2. DeepFashion2 数据组织形式

2.1. 服装类目名与对应id

2.2. 服装 landmarks 次序

2.3. 服装 pairs 说明

2.4. DeepFashion2 数据集划分

2.5. deepfashion2_to_coco.py

3. DeepFashion2 数据集对比

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Dataset - DeepFashion2 数据集

1. DeepFashion2 数据集

1.1. DeepFashion2 数据集概况

1.2. DeepFashion2 数据集下载

1.3. DeepFashion2 数据集统计

2. DeepFashion2 数据组织形式

2.1. 服装类目名与对应id

2.2. 服装 landmarks 次序

2.3. 服装 pairs 说明

2.4. DeepFashion2 数据集划分

2.5. deepfashion2_to_coco.py

3. DeepFashion2 数据集对比