Dataset Ninja LogoDataset Ninja:

CCP Dataset

2098591595
Tagretail
Tasksemantic segmentation
Release YearMade in 2014
LicenseApache 2.0
Download142 MB

Introduction #

Released 2014-08-19 Β·Wei Yang, Ping Luo, Liang Lin

The CCP: Clothing Co-Parsing dataset contains 2,098 high-resolution fashion photos with diverse human/clothing variations, including superpixel-level annotations with 57 tags for over 1,000 images, along with image-level tags for the rest of the dataset, all meticulously produced by a professional team.

Authors introduce a system aimed at parsing clothing images to provide accurate pixel-wise annotations of clothing items, addressing challenges such as diverse clothing styles, human pose variations, and a large number of fine-grained clothing categories. The system comprises two sequential phases: image co-segmentation for extracting distinct clothing regions and region co-labeling for recognizing various garment items.

Clothing recognition and retrieval have huge potential in internet-based e-commerce, as the revenue of online clothing sales keeps increasing every year. The authors focused on building an engineered and applicable system to jointly parse a batch of clothing images and produce accurate pixel-wise annotation of clothing items. They consider the following challenges to build such a system:

  • The appearances of clothes and garment items are often diverse with different styles and textures, compared with other common objects. It is usually hard to segment and recognize clothes via only bottom-up image features.
  • The variations of human poses and self-occlusions are non-trivial issues for clothing recognition, although the clothing images can be in clear resolution and nearly frontal view.
  • The number of fine-grained clothes categories is very large, e.g., more than 50 categories in the Fashionista dataset; the categories are relatively fewer in existing co-segmentation systems.

To address the above issues, authors develop a system consisting of two sequential phases of inference over a set of clothing images, i.e. image co-segmentation for extracting distinguishable clothes regions, and region co-labeling for recognizing various garment items.

Fig

Illustration of the proposed clothing co-parsing framework, which consists of two sequential phases of optimization: (a) clothing co-segmentation for extracting coherent clothes regions, and (b) region co-labeling for recognizing various clothes garments. Specifically, clothing co-segmentation iterates with three steps: (a1) grouping superpixels into regions, (a2) selecting confident foreground regions to train E-SVM classifiers, and (a3) propagating segmentations by applying E-SVM templates over all images. Given the segmented regions, clothing co-labeling is achieved based on a multi-image graphical model, as illustrated in (b).

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkKaggle

Summary #

CCP: Clothing Co-Parsing is a dataset for semantic segmentation and unsupervised learning tasks. It is used in the retail industry.

The dataset consists of 2098 images with 8273 labeled objects belonging to 59 different classes including null, skin, hair, and other: shoes, bag, pants, sunglasses, dress, purse, coat, accessories, blouse, belt, shirt, skirt, jeans, sweater, hat, t-shirt, blazer, stockings, suit, bracelet, scarf, jacket, sandals, socks, shorts, and 31 more.

Images in the CCP dataset have pixel-level semantic segmentation annotations. There are 1094 (52% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, the non-labeled images contain clothing tags. The dataset was released in 2014 by the Sun Yat-sen University, The Chinese University of Hong Kong, and SYSU-CMU Shunde International Joint Research Institute.

Here is a visualized example for randomly selected sample classes:

Explore #

CCP dataset has 2098 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
OpenSample annotation mask from CCPSample image from CCP
πŸ‘€
Have a look at 2098 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 59 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 59
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
nullβž”
mask
1004
1004
1
76.9%
skinβž”
mask
1003
1003
1
2.92%
hairβž”
mask
960
960
1
1.31%
shoesβž”
mask
775
775
1
1.08%
bagβž”
mask
443
443
1
2.03%
pantsβž”
mask
302
302
1
8.02%
sunglassesβž”
mask
293
293
1
0.21%
dressβž”
mask
271
271
1
10.06%
purseβž”
mask
234
234
1
1.79%
coatβž”
mask
232
232
1
10.76%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 55
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
null
mask
1004
76.9%
100%
64.74%
801px
100%
873px
100%
828px
100%
550px
100%
550px
100%
skin
mask
1003
2.92%
10.01%
0.03%
11px
1.36%
798px
92.38%
519px
62.66%
19px
3.45%
458px
83.27%
hair
mask
960
1.31%
4.82%
0.03%
13px
1.59%
652px
79.13%
123px
14.81%
13px
2.36%
220px
40%
shoes
mask
775
1.08%
6.51%
0.03%
14px
1.7%
573px
68.3%
87px
10.56%
11px
2%
347px
63.09%
bag
mask
443
2.03%
9.02%
0.08%
38px
4.63%
563px
68.55%
185px
22.36%
21px
3.82%
358px
65.09%
pants
mask
302
8.02%
17.89%
0.73%
72px
8.65%
668px
81.27%
349px
42.11%
68px
12.36%
371px
67.45%
sunglasses
mask
293
0.21%
0.42%
0.03%
10px
1.19%
375px
44.7%
30px
3.57%
11px
2%
145px
26.36%
dress
mask
271
10.06%
25.25%
0.01%
5px
0.6%
675px
80.55%
379px
45.73%
7px
1.27%
372px
67.64%
purse
mask
234
1.79%
6.45%
0.14%
38px
4.57%
517px
63.13%
178px
21.51%
17px
3.09%
287px
52.18%
coat
mask
232
10.76%
23.58%
2.43%
150px
18.03%
609px
74.45%
373px
45%
159px
28.91%
402px
73.09%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 8273 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 8273
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
hair
mask
0214.jpg
812 x 550
140px
17.24%
123px
22.36%
1.51%
2βž”
shoes
mask
0214.jpg
812 x 550
69px
8.5%
79px
14.36%
0.81%
3βž”
dress
mask
0214.jpg
812 x 550
312px
38.42%
176px
32%
7.91%
4βž”
skin
mask
0214.jpg
812 x 550
703px
86.58%
177px
32.18%
7.96%
5βž”
null
mask
0214.jpg
812 x 550
812px
100%
550px
100%
80.59%
6βž”
accessories
mask
0214.jpg
812 x 550
30px
3.69%
171px
31.09%
0.08%
7βž”
bag
mask
0214.jpg
812 x 550
222px
27.34%
98px
17.82%
1.01%
8βž”
sunglasses
mask
0214.jpg
812 x 550
17px
2.09%
56px
10.18%
0.14%
9βž”
hair
mask
0680.jpg
820 x 550
56px
6.83%
83px
15.09%
0.43%
10βž”
shoes
mask
0680.jpg
820 x 550
89px
10.85%
69px
12.55%
0.88%

License #

CCP: Clothing Co-Parsing Dataset is under Apache 2.0 license.

Source

Citation #

If you make use of the Clothing Co-Parsing data, please cite the following reference:

@inproceedings{yang2014clothing,
  title={Clothing Co-Parsing by Joint Image Segmentation and Labeling},
  author={Yang, Wei and Luo, Ping and Lin, Liang}
  booktitle={Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on},
  year={2013},
  organization={IEEE}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-clothing-co-parsing-dataset,
  title = { Visualization Tools for CCP Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/clothing-co-parsing } },
  url = { https://datasetninja.com/clothing-co-parsing },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset CCP can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='CCP', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

. . .

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