task_categories:image-segmentation task_ids:semantic-segmentation annotations_creators:crowdsourced size_categories:10K<n<100K source_datasets:original license:cc-by-sa-4.0 portrait-segmentation face-parsing face-beautification arxiv:2304.13509 region:us
---
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](support_images/main.jpg)

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](https://arxiv.org/abs/2304.13509).

## 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](support_images/original-1.gif)
![easyportrait](support_images/example-1.gif)

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

![easyportrait](support_images/original-2.gif)
![easyportrait](support_images/example-2.gif)

## Structure
```
.
├── images.zip
│   ├── train/         # Train set: 14k
│   ├── val/           # Validation set: 2k
│   ├── test/          # Test set: 4k
├── annotations.zip
│   ├── meta.zip       # Meta-information (width, height, brightness, imhash, user_id)
│   ├── train/     
│   ├── val/       
│   ├── 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
- [Alexander Kapitanov](https://www.linkedin.com/in/hukenovs)
- [Karina Kvanchiani](https://www.linkedin.com/in/kvanchiani)
- [Sofia Kirillova](https://www.linkedin.com/in/gofixyourself/)

## Links
- [arXiv](https://arxiv.org/abs/2304.13509)
- [Paperswithcode](https://paperswithcode.com/dataset/easyportrait)
- [Kaggle](https://www.kaggle.com/datasets/kapitanov/easyportrait)
- [Habr](https://habr.com/ru/companies/sberdevices/articles/731794/)
- [Gitlab](https://gitlab.aicloud.sbercloud.ru/rndcv/easyportrait)

## 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
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a variant of <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

Please see the specific [license](https://github.com/hukenovs/easyportrait/blob/master/license/en_us.pdf).