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Person in Context Dataset

17605144662
Taggeneral
Taskinstance segmentation
Release YearMade in 2021
Licensecustom
Download11 GB

Introduction #

Released 2021-05-24 Β·Si Liu, Zitian Wang, Yulu Gaoet al.

The authors collect a PIC: Person in Context Dataset v2.0, which contains 17, 605 high-resolution images and densely annotated entity segmentation and relations, including 141 object categories, 23 relation categories. The authors introduced a new task named human-centric relation segmentation (HRS). HRS aims to predict the relations between the human and surrounding entities and identify the relation-correlated human parts, which are represented as pixel-level masks.

Note: the authors did not furnish the capability to compare a particular object with its surrounding entities in detail. The annotations merely offer broad descriptions of the interactions within the image. Consequently, in our scenario, the dataset is solely applicable for addressing instance segmentation challenges.

Dataset description

Data collection was done with three steps:

  • Data crawling: PIC contains both indoor and outdoor images, which are crawled from Flickr website with copyrights. Query words for retrieving indoor pictures include cook, party, drink, watch tv, eat, etc., and those for outdoor ones include play ball, run, ride, outing, picnic, etc. In this way, the collected data enjoy great diversities in terms of scenario, appearance, viewpoint, light condition and occlusion.
  • Data filtering: The authors filter out the images with low resolution or without human. Then they calculate the distributions of the relations.
  • Data balancing: The authors recollect the data for relations with lower frequency to balance the data distribution.

The authors first annotate 141 kinds of things and stuff in the images. The entity categories cover a wide range of indoor and outdoor scenes, including office,
restaurant, seaside, snowfield, etc. For each entity falling into predefined categories, they label it with its class and pixel-level mask segment. The disconnected regions of stuff are viewed as different entities.

image

An example of the original image and entity segmentation.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

PIC: Person in Context Dataset v2.0 is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

The dataset consists of 17605 images with 191150 labeled objects belonging to 144 different classes including human, table, bag, and other: hat, ground, chair, door, painting/poster, sofa, building, shelf, window, grass, cabinet, vegetation, floor, guardrail, ball, book, curtain, cup, phone, bottle, toy, tree, plant, stick, instrument, and 116 more.

Images in the Person in Context 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 2975 (17% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (12653 images), test (2975 images), and val (1977 images). Alternatively, the dataset could be split into 2 location: indoor (7458 images) and outdoor (6539 images). The dataset was released in 2021 by the Beihang University, China, Academy of Science, China, and Sea AI Lab, China.

Here is a visualized example for randomly selected sample classes:

Explore #

Person in Context dataset has 17605 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 Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
OpenSample annotation mask from Person in ContextSample image from Person in Context
πŸ‘€
Have a look at 17605 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 144 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 144
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
humanβž”
mask
14629
38536
2.63
32.01%
tableβž”
mask
3268
6834
2.09
9.2%
bagβž”
mask
3235
7222
2.23
1.95%
hatβž”
mask
2799
5250
1.88
1.62%
groundβž”
mask
2412
11693
4.85
30.04%
chairβž”
mask
2288
7167
3.13
4.08%
doorβž”
mask
1683
3665
2.18
7.94%
painting/posterβž”
mask
1652
3191
1.93
6.92%
sofaβž”
mask
1637
9124
5.57
9.76%
buildingβž”
mask
1337
5679
4.25
29.24%

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.

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 141
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
human
mask
38536
12.15%
94.94%
0%
1px
0.04%
5194px
100%
654px
47.94%
1px
0.05%
5566px
100%
ground
mask
11693
6.2%
96.8%
0%
1px
0.02%
5503px
100%
323px
18%
1px
0.02%
8212px
100%
sofa
mask
9124
1.75%
57.43%
0%
1px
0.04%
4559px
99.98%
210px
18.23%
1px
0.02%
6839px
99.99%
floor
mask
7544
1.97%
85.87%
0%
1px
0.03%
3979px
99.95%
160px
14.55%
1px
0.07%
4287px
99.98%
bag
mask
7222
0.88%
30.9%
0%
2px
0.14%
2818px
98.19%
215px
16.95%
2px
0.12%
1910px
99.93%
chair
mask
7167
1.3%
31.12%
0%
1px
0.1%
4579px
99.9%
217px
17.87%
1px
0.04%
2297px
99.95%
table
mask
6834
4.4%
56.26%
0%
1px
0.04%
2852px
99.93%
220px
20.08%
1px
0.05%
4751px
100%
grass
mask
5886
6.9%
96.2%
0%
1px
0.04%
4869px
100%
322px
18.5%
1px
0.02%
5615px
100%
building
mask
5679
6.88%
90.95%
0%
1px
0.03%
4188px
100%
365px
21.15%
1px
0.02%
6622px
100%
hat
mask
5250
0.86%
23.3%
0%
5px
0.3%
1967px
87.22%
138px
11.63%
1px
0.08%
2011px
79.84%

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 101181 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 101181
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”
human
mask
indoor_00426.jpg
960 x 1452
729px
75.94%
318px
21.9%
7.56%
2βž”
human
mask
indoor_00426.jpg
960 x 1452
248px
25.83%
156px
10.74%
1.46%
3βž”
human
mask
indoor_00426.jpg
960 x 1452
645px
67.19%
459px
31.61%
8.98%
4βž”
human
mask
indoor_00426.jpg
960 x 1452
65px
6.77%
20px
1.38%
0.07%
5βž”
human
mask
indoor_00426.jpg
960 x 1452
226px
23.54%
94px
6.47%
0.64%
6βž”
electronics
mask
indoor_00426.jpg
960 x 1452
142px
14.79%
143px
9.85%
1.01%
7βž”
kitchen_island
mask
indoor_00426.jpg
960 x 1452
321px
33.44%
380px
26.17%
4.36%
8βž”
kitchen_island
mask
indoor_00426.jpg
960 x 1452
194px
20.21%
417px
28.72%
1.99%
9βž”
kitchen_island
mask
indoor_00426.jpg
960 x 1452
297px
30.94%
49px
3.37%
0.38%
10βž”
kitchen_island
mask
indoor_00426.jpg
960 x 1452
15px
1.56%
19px
1.31%
0.01%

License #

This PIC 2.0 API is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the code given that you agree to our license terms.

Source

Citation #

If you make use of the Person in Context data, please cite the following reference:

@dataset{Person in Context,
  author={Si Liu and Zitian Wang and Yulu Gao and Lejian Ren and Yue Liao and Guanghui Ren and Bo Li and Shuicheng Yan},
  title={Person in Context Dataset},
  year={2021},
  url={https://picdataset.com/challenge/task/download/}
}

Source

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

@misc{ visualization-tools-for-person-in-context-dataset,
  title = { Visualization Tools for Person in Context Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/person-in-context-2 } },
  url = { https://datasetninja.com/person-in-context-2 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Person in Context 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='Person in Context', 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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