Dataset Ninja LogoDataset Ninja:

CelebAMask-HQ Dataset

30000181
Tagentertainment, benchmark
Tasksemantic segmentation
Release YearMade in 2019
Licensecustom
Download3 GB

Introduction #

Released 2019-04-03 Β·Cheng-Han Lee, Ziwei Liu, Lingyun Wuet al.

CelebAMask-HQ is an extensive dataset featuring 30,000 high-resolution face images. These images were carefully chosen from the CelebA dataset by adhering to the CelebA-HQ guidelines. Each image in this dataset comes with a segmentation mask outlining facial attributes that correspond to the CelebA dataset. This initiative resulted in the creation of a substantial dataset for face attribute labeling, all based on the CelebAHQ collection consisting of 30,000 high-resolution face images from CelebA.

It has several appealing properties:

  • Comprehensive Annotations. CelebAMask-HQ was precisely hand-annotated with the size of 512Γ—512 and 19 classes including all facial components and accessories such as skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, and cloth.

Some samples

  • Label Size Selection. The size of images in CelebAHQ were 1024Γ—1024. However, we chose the size of 512Γ—512 because the cost of the labeling would be quite high for labeling the face at 1024Γ—1024. Besides, we could easily extend the labels from 512Γ—512 to 1024x1024 by nearest-neighbor interpolation without introducing noticeable artifacts.

  • Quality Control. After manual labeling, we had a quality control check on every single segmentation mask. Furthermore, we asked annotators to refine all masks with several rounds of iterations.

  • Amodal Handling. For occlusion handling, if the facial component was partly occluded, we asked annotators to label the occluded parts of the components by human inferring. On the other hand, we skipped the annotations for those components that are totally occluded.

CelebAMask-HQ can be used to train and evaluate algorithms of face parsing, face recognition, and GANs for face generation and editing.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

CelebAMask-HQ is a dataset for a semantic segmentation task. It is used in the entertainment industry.

The dataset consists of 30000 images with 372766 labeled objects belonging to 18 different classes including skin, nose, l_lip, and other: u_lip, hair, r_eye, l_eye, neck, l_brow, r_brow, mouth, cloth, l_ear, r_ear, ear_r, neck_l, eye_g, and hat.

Images in the CelebAMask-HQ dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, every image contains information about visual characteristics of a person. The dataset was released in 2019 by the SenseTime Research, The Chinese University of Hong Kong, and The University of Hong Kong.

Dataset Poster

Explore #

CelebAMask-HQ dataset has 30000 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 CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
OpenSample annotation mask from CelebAMask-HQSample image from CelebAMask-HQ
πŸ‘€
Have a look at 30000 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 18 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 18
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
skinβž”
mask
30000
30000
1
32%
noseβž”
mask
29998
29998
1
2.07%
l_lipβž”
mask
29896
29896
1
0.68%
u_lipβž”
mask
29870
29870
1
0.43%
hairβž”
mask
29300
29300
1
32.67%
r_eyeβž”
mask
29260
29260
1
0.23%
l_eyeβž”
mask
29258
29258
1
0.23%
neckβž”
mask
29148
29148
1
4.28%
l_browβž”
mask
29029
29029
1
0.46%
r_browβž”
mask
28968
28968
1
0.45%

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 18
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
skin
mask
30000
32%
61.21%
1.17%
144px
14.06%
1024px
100%
774px
75.63%
110px
10.74%
838px
81.84%
nose
mask
29998
2.07%
6.6%
0.07%
44px
4.3%
562px
54.88%
213px
20.84%
28px
2.73%
562px
54.88%
l_lip
mask
29896
0.68%
4.99%
0.02%
10px
0.98%
788px
76.95%
74px
7.23%
36px
3.52%
672px
65.62%
u_lip
mask
29870
0.43%
7.05%
0.02%
8px
0.78%
354px
34.57%
40px
3.86%
38px
3.71%
530px
51.76%
hair
mask
29300
32.67%
81.95%
0.02%
38px
3.71%
1024px
100%
846px
82.66%
14px
1.37%
1024px
100%
r_eye
mask
29260
0.23%
2.91%
0.01%
6px
0.59%
346px
33.79%
37px
3.59%
10px
0.98%
674px
65.82%
l_eye
mask
29258
0.23%
2.96%
0.01%
8px
0.78%
298px
29.1%
37px
3.59%
14px
1.37%
488px
47.66%
neck
mask
29148
4.28%
15%
0.02%
18px
1.76%
630px
61.52%
261px
25.47%
18px
1.76%
1024px
100%
l_brow
mask
29029
0.46%
2.24%
0.01%
12px
1.17%
378px
36.91%
58px
5.64%
22px
2.15%
500px
48.83%
r_brow
mask
28968
0.45%
3.02%
0.02%
12px
1.17%
378px
36.91%
58px
5.66%
22px
2.15%
520px
50.78%

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 372766 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 372766
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
25638.jpg
1024 x 1024
554px
54.1%
760px
74.22%
16.41%
2βž”
l_brow
mask
25638.jpg
1024 x 1024
62px
6.05%
150px
14.65%
0.42%
3βž”
l_eye
mask
25638.jpg
1024 x 1024
28px
2.73%
90px
8.79%
0.15%
4βž”
l_lip
mask
25638.jpg
1024 x 1024
32px
3.12%
216px
21.09%
0.31%
5βž”
mouth
mask
25638.jpg
1024 x 1024
20px
1.95%
144px
14.06%
0.2%
6βž”
nose
mask
25638.jpg
1024 x 1024
200px
19.53%
188px
18.36%
2.37%
7βž”
r_brow
mask
25638.jpg
1024 x 1024
62px
6.05%
166px
16.21%
0.54%
8βž”
r_eye
mask
25638.jpg
1024 x 1024
28px
2.73%
84px
8.2%
0.15%
9βž”
skin
mask
25638.jpg
1024 x 1024
780px
76.17%
580px
56.64%
35.71%
10βž”
u_lip
mask
25638.jpg
1024 x 1024
30px
2.93%
232px
22.66%
0.36%

License #

  • The CelebAMask-HQ dataset is available for non-commercial research purposes only.
  • You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  • You agree not to further copy, publish or distribute any portion of the CelebAMask-HQ dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

Source

Citation #

If you make use of the CelebAMask-HQ data, please cite the following reference:

@inproceedings{CelebAMask-HQ,
  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
  author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}

Source

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

@misc{ visualization-tools-for-celebamask-hq-dataset,
  title = { Visualization Tools for CelebAMask-HQ Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/celebamask-hq } },
  url = { https://datasetninja.com/celebamask-hq },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-21 },
}

Download #

Dataset CelebAMask-HQ 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='CelebAMask-HQ', 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.

. . .

Disclaimer #

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