EasyPortrait / README.md
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metadata
license: cc-by-sa-4.0
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
size_categories:
  - 10K<n<100K
annotations_creators:
  - crowdsourced
source_datasets:
  - original
tags:
  - portrait-segmentation
  - face-parsing
  - face-beautification
pretty_name: EasyPortrait
paperswithcode_id: easyportrait

EasyPortrait - Face Parsing and Portrait Segmentation Dataset

easyportrait

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 – 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:

easyportrait easyportrait

Example of the model work trained on the EasyPortrait dataset and tested on test data with a domain:

easyportrait easyportrait

Structure

.
β”œβ”€β”€ images.zip
β”‚   β”œβ”€β”€ train/         # Train set: 14k
β”‚   β”œβ”€β”€ val/           # Validation set: 2k
β”‚   β”œβ”€β”€ test/          # Test set: 4k
β”œβ”€β”€ annotations.zip
β”‚   β”œβ”€β”€ meta.zip       # Meta-information (width, height, brightness, imhash, user_id)
β”‚   β”œβ”€β”€ train/     
β”‚   β”œβ”€β”€ val/       
β”‚   β”œβ”€β”€ 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 extension
  • user_id - unique anonymized user ID
  • data_hash - image hash by using Perceptual hashing
  • width - image width
  • height - image height
  • brightness - image brightness
  • train, 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

Creative Commons License
This work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.

Please see the specific license.