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 91.78GB, and it contains 40,000 RGB images (~38.3K FullHD images) with high quality annotated masks. This dataset is divided into training set, validation set and test set by subject

For more information see our paper EasyPortrait – Face Parsing and Portrait Segmentation Dataset.

🔥 Changelog

• 2023/11/13
  : We release EasyPortrait 2.0. ✌️
  • 40,000 RGB images (~38.3K FullHD images)
  • Added diversity by region, race, human emotions and lighting conditions
  • The data was further cleared and new ones were added
  • Train/val/test split: (30,000) 75% / (4,000) 10% / (6,000) 15% by subject
    user_id
  • Multi-gpu training and testing
  • Added new models for face parsing and portrait segmentation
  • Dataset size is 91.78GB
  • 13,705 unique persons
• 2023/02/23
  : EasyPortrait (Initial Dataset) 💪
  • Dataset size is 26GB
  • 20,000 RGB images (~17.5K FullHD images) with 9 classes annotated
  • Train/val/test split: (14,000) 70% / (2,000) 10% / (4,000) 20% by subject
    user_id
  • 8,377 unique persons

Old EasyPortrait dataset is also available into branch

Downloads

Also, you can download EasyPortrait dataset from Kaggle.

Structure

.
├── images.zip
│   ├── train/         # Train set: 30k
│   ├── val/           # Validation set: 4k
│   ├── test/          # Test set: 6k
├── annotations.zip
│   ├── train/
│   ├── val/
│   ├── test/
├── meta.zip       # Meta-information (width, height, brightness, imhash, user_id)
...

Models

We provide some pre-trained models as the baseline for portrait segmentation and face parsing. We use mean Intersection over Union (mIoU) as the main metric.

Portrait segmentation:

Face parsing:

Annotations

Annotations are presented as 2D-arrays, images in

Also, we provide some additional meta-information for dataset in

where:

• image_name
  - image file name without extension
• user_id
  - unique anonymized user ID
• height
  - image height
• width
  - image width
• brightness
  - image brightness
• set
  - "train", "test" or "val" for train / test / val subsets respectively

Images

Training, Evaluation and Testing on EasyPortrait

The code is based on MMSegmentation with 0.30.0 version.

Models were trained and evaluated on 8 NVIDIA V100 GPUs with CUDA 11.2.

For installation process follow the instructions here and use the requirements.txt file in our repository.

Authors and Credits

• Alexander Kapitanov
• Karina Kvanchiani
• Elizaveta Petrova
• Karen Efremyan
• Alexander Sautin

Links

• arXiv
• Paperswithcode
• Kaggle
• Habr
• Gitlab

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.