Datasets:
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
Error code: JobManagerExceededMaximumDurationError
Need help to make the dataset viewer work? Open a discussion for direct support.
image
image |
---|
EasyPortrait - Face Parsing and Portrait Segmentation Dataset
We introduce a large-scale image dataset EasyPortrait for portrait segmentation and face parsing. Proposed dataset can be used in several tasks, such as background removal in conference applications, teeth whitening, face skin enhancement, red eye removal or eye colorization, and so on.
EasyPortrait dataset size is about 26GB, and it contains 20 000 RGB images (~17.5K FullHD images) with high quality annotated masks. This dataset is divided into training set, validation set and test set by subject user_id
. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.
Training images were received from 5,947 unique users, while validation was from 860 and testing was from 1,570. On average, each EasyPortrait image has 254 polygon points, from which it can be concluded that the annotation is of high quality. Segmentation masks were created from polygons for each annotation.
For more information see our paper EasyPortrait β Face Parsing and Portrait Segmentation Dataset.
The model results trained on the EasyPortrait dataset
Example of the model work trained on the EasyPortrait dataset and tested on test data from a different domain:
Example of the model work trained on the EasyPortrait dataset and tested on test data with a domain:
Structure
.
βββ images.zip
β βββ train/ # Train set: 14k
β βββ val/ # Validation set: 2k
β βββ test/ # Test set: 4k
βββ annotations.zip
β βββ meta.zip # Meta-information (width, height, brightness, imhash, user_id)
β βββ train/
β βββ val/
β βββ test/
...
Annotations
Annotations are presented as 2D-arrays, images in *.png format with several classes:
Index | Class |
---|---|
0 | BACKGROUND |
1 | PERSON |
2 | SKIN |
3 | LEFT BROW |
4 | RIGHT_BROW |
5 | LEFT_EYE |
6 | RIGHT_EYE |
7 | LIPS |
8 | TEETH |
Also, we provide some additional meta-information for dataset in annotations/meta.zip
file:
attachment_id | user_id | data_hash | width | height | brightness | train | test | valid | |
---|---|---|---|---|---|---|---|---|---|
0 | de81cc1c-... | 1b... | e8f... | 1440 | 1920 | 136 | True | False | False |
1 | 3c0cec5a-... | 64... | df5... | 1440 | 1920 | 148 | False | False | True |
2 | d17ca986-... | cf... | a69... | 1920 | 1080 | 140 | False | True | False |
where:
attachment_id
- image file name without extensionuser_id
- unique anonymized user IDdata_hash
- image hash by using Perceptual hashingwidth
- image widthheight
- image heightbrightness
- image brightnesstrain
,test
,valid
are the binary columns for train / test / val subsets respectively
Authors and Credits
Links
Citation
You can cite the paper using the following BibTeX entry:
@article{EasyPortrait,
title={EasyPortrait - Face Parsing and Portrait Segmentation Dataset},
author={Kapitanov, Alexander and Kvanchiani, Karina and Kirillova Sofia},
journal={arXiv preprint arXiv:2304.13509},
year={2023}
}
License
This work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.
Please see the specific license.
- Downloads last month
- 1