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CIHP Dataset

38280192938
Tagsurveillance
Taskinstance segmentation
Release YearMade in 2018
LicenseMIT
Download1 GB

Introduction #

Released 2018-08-01 Β·Ke Gong, Xiaodan Liang, Yicheng Liet al.

To benchmark the more challenging multi-person human parsing task, authors build a large-scale dataset called the CIHP: Crowd Instance-level Human Parsing dataset, which has several appealing properties. First, with 38,280 diverse human images, it is the largest multi-person human parsing dataset to date. Second, CIHP is annotated with rich information on items with persons. The images in this dataset are labeled with pixel-wise annotations on 20 categories and instance-level identification. Third, the images collected from the real-world scenarios contain people appearing with challenging poses and viewpoints, heavy occlusions, various appearances, and in a wide range of resolutions. Some examples are shown in the Figure below. With the CIHP dataset, authors propose a new benchmark for instance-level human parsing together with a standard evaluation server where the test set will be kept secret to avoid overfitting.

Fig

Left: Statistics on the number of persons in one image. Right: The data distribution on 19 semantic part labels in the CIHP dataset.

The images in the CIHP are collected from unconstrained resources like Google and Bing. authors manually specify several keywords (e.g., family, couple, party, meeting, etc.) to gain a great diversity of multi-person images. The crawled images are elaborately annotated by a professional labeling organization with good quality control. The authors supervise the whole annotation process and conduct a second-round check for each annotated image. Authors remove the unusable images that are of low resolution, or image quality, or contain one or no person instance. In total, 38,280 images are kept to construct the CIHP dataset. Following random selection, authors arrive at a unique split that consists of 28,280 training and 5,000 validation images with publicly available annotations, as well as 5,000 test images with annotations withheld for benchmarking purposes.

Authors now introduce the images and categories in the CIHP dataset with more statistical details. Superior to the previous attempts with an average of one or two-person instances in an image, all images of the CIHP dataset contain two or more instances with an average of 3.4. Generally, authors follow LIP to define and annotate the semantic part labels. However, they find that the Jumpsuit label defined in LIP is infrequent compared to other labels. To parse the human more completely and precisely, authors use a more common body part label (Tosor-skin) instead. The 19 semantic part labels in the CIHP are hat, hair, sunglasses, upper-clothes, dress, coat, socks, pants, gloves, scarf, skirt, torsoskin, face, right_arm, left_arm, right_leg, left_leg, right_shoe and left_shoe.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

CIHP: Crowd Instance-level Human Parsing is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the surveillance industry.

The dataset consists of 38280 images with 768446 labeled objects belonging to 19 different classes including face, hair, torso_skin, and other: upperclothes, right_arm, left_arm, pants, coat, left_shoe, right_shoe, right_leg, left_leg, hat, dress, socks, sunglasses, skirt, scarf, and glove.

Images in the CIHP dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. There are 5000 (13% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: training (28280 images), testing (5000 images), and validation (5000 images). The dataset was released in 2018 by the Sun Yat-sen University, SenseTime Group (Limited), and CVTE Research.

Here are the visualized examples for the classes:

Explore #

CIHP dataset has 38280 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 CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
OpenSample annotation mask from CIHPSample image from CIHP
πŸ‘€
Have a look at 38280 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 19 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 19
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
faceβž”
mask
33064
104477
3.16
4.6%
hairβž”
mask
32489
99636
3.07
4.87%
torso_skinβž”
mask
32235
92365
2.87
1.54%
upperclothesβž”
mask
30400
85544
2.81
10.78%
right_armβž”
mask
29740
73282
2.46
1.91%
left_armβž”
mask
29308
70363
2.4
1.79%
pantsβž”
mask
23124
60430
2.61
5.76%
coatβž”
mask
16760
38731
2.31
15.15%
left_shoeβž”
mask
11058
29430
2.66
0.63%
right_shoeβž”
mask
11044
29437
2.67
0.64%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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 19
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
face
mask
104477
1.46%
53.62%
0%
1px
0.27%
500px
100%
53px
14.78%
1px
0.2%
429px
81.6%
hair
mask
99636
1.59%
45.81%
0%
1px
0.2%
843px
100%
65px
17.97%
1px
0.16%
569px
100%
torso_skin
mask
92365
0.54%
50.81%
0%
1px
0.17%
482px
100%
39px
10.62%
1px
0.16%
716px
100%
upperclothes
mask
85544
3.83%
47.92%
0%
1px
0.12%
639px
100%
110px
30.11%
1px
0.17%
929px
100%
right_arm
mask
73282
0.78%
22.47%
0%
1px
0.2%
610px
100%
51px
13.76%
1px
0.19%
550px
100%
left_arm
mask
70363
0.74%
30.37%
0%
1px
0.2%
601px
100%
50px
13.52%
1px
0.2%
536px
100%
pants
mask
60430
2.2%
27.66%
0%
1px
0.3%
610px
100%
83px
22.02%
1px
0.2%
500px
100%
coat
mask
38731
6.56%
53.4%
0%
4px
1%
668px
100%
143px
39.75%
2px
0.4%
626px
100%
right_shoe
mask
29437
0.24%
13.75%
0%
1px
0.3%
466px
88.11%
24px
6.12%
1px
0.2%
290px
59.16%
left_shoe
mask
29430
0.24%
7.53%
0%
1px
0.2%
453px
90.6%
24px
6.11%
1px
0.2%
234px
68.17%

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 100057 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 100057
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
0011630.jpg
500 x 375
86px
17.2%
115px
30.67%
1.31%
2βž”
hair
mask
0011630.jpg
500 x 375
76px
15.2%
64px
17.07%
1.06%
3βž”
torso_skin
mask
0011630.jpg
500 x 375
125px
25%
100px
26.67%
1.58%
4βž”
left_arm
mask
0011630.jpg
500 x 375
87px
17.4%
47px
12.53%
1.14%
5βž”
torso_skin
mask
0011630.jpg
500 x 375
36px
7.2%
45px
12%
0.44%
6βž”
face
mask
0011630.jpg
500 x 375
70px
14%
67px
17.87%
1.17%
7βž”
upperclothes
mask
0011630.jpg
500 x 375
296px
59.2%
349px
93.07%
28.41%
8βž”
face
mask
0011630.jpg
500 x 375
143px
28.6%
125px
33.33%
6.71%
9βž”
upperclothes
mask
0011630.jpg
500 x 375
273px
54.6%
62px
16.53%
2.77%
10βž”
right_arm
mask
0011630.jpg
500 x 375
72px
14.4%
93px
24.8%
1.08%

License #

CIHP: Crowd Instance-level Human Parsing is under MIT license.

Source

Citation #

If you make use of the CIHP data, please cite the following reference:

@misc{gong2018instancelevel,
  title={Instance-level Human Parsing via Part Grouping Network}, 
  author={Ke Gong and Xiaodan Liang and Yicheng Li and Yimin Chen and Ming Yang and Liang Lin},
  year={2018},
  eprint={1808.00157},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Source

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

@misc{ visualization-tools-for-cihp-dataset,
  title = { Visualization Tools for CIHP Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/cihp } },
  url = { https://datasetninja.com/cihp },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset CIHP 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='CIHP', 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.

The data in original format can be downloaded here.

. . .

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