metadata

license: cc-by-sa-4.0
task_categories:
  - image-segmentation
task_ids:
  - semantic-segmentation
  - portrait-segmentation
  - face-parsing
size_categories:
  - 10K<n<100K
annotations_creators:
  - crowdsourced
source_datasets:
  - original
tags:
  - portrait-segmentation
  - face-parsing
  - face-beautification
pretty_name: EasyPortrait
paperswithcode_id: easyportrait
dataset_info:
  - config_name: easyportrait
    features:
      - name: image
        dtype: image
      - name: annotation
        dtype: image
      - name: face_part_category
        dtype:
          class_label:
            names:
              '0': background
              '1': person
              '2': skin
              '3': left_brow
              '4': right_brow
              '5': left_eye
              '6': right_eye
              '7': lips
              '8': teeth
    splits:
      - name: train
        num_examples: 14000
      - name: test
        num_examples: 4000
      - name: validation
        num_examples: 2000

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 – Face Parsing and Portrait Segmentation Dataset.

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

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.

Datasets:

gofixyourself
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EasyPortrait