liuguoyou / easyportrait

EasyPortrait - Face Parsing and Portrait Segmentation Dataset

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.

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

Downloads

Link	Size
images	26G
annotations	235M
train set	18.1G
validation set	2.6G
test set	5.2G

Also, you can download EasyPortrait dataset from Kaggle.

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/      
...

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.

Model Name	Parameters (M)	Input shape	mIOU
LR-ASPP + MobileNet-V3	1.14	1024 × 1024	77.55
FCN + MobileNet-V2	9.71	384 × 384	74.3
FCN + MobileNet-V2	9.71	512 × 512	77.01
FCN + MobileNet-V2	9.71	1024 × 1024	81.23
FPN + ResNet-50	28.5	512 × 512	83.13
FPN + ResNet-50	28.5	1024 × 1024	85.97
BiSeNet-V2	14.79	512 × 512	77.93
BiSeNet-V2	14.79	1024 × 1024	83.53
SegFormer-B0	3.72	384 × 384	79.82
SegFormer-B0	3.72	1024 × 1024	84.27
SegFormer-B2	24.73	384 × 384	81.59
SegFormer-B2	24.73	512 × 512	83.03
SegFormer-B2	24.73	1024 × 1024	85.72
SegFormer-B5	81.97	384 × 384	81.66
SegFormer-B5	81.97	1024 × 1024	85.80
SegNeXt + MSCAN-T	4.23	384 × 384	75.01
SegNeXt + MSCAN-T	4.23	512 × 512	78.59

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

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.

Training

For single GPU mode:

python ./pipelines/tools/train.py ./pipelines/local_configs/easy_portrait_experiments/<model_dir>/<config_file>.py --gpu-id <GPU_ID>

For distributed training mode:

./pipelines/tools/dist_train.sh ./pipelines/local_configs/easy_portrait_experiments/<model_dir>/<config_file>.py <NUM_GPUS>

Evaluation

For single GPU mode:

python ./pipelines/tools/test.py <PATH_TO_MODEL_CONFIG>  <PATH_TO_CHECKPOINT> --gpu-id <GPU_ID> --eval mIoU

For distributed evaluation mode:

./pipelines/tools/dist_test.sh <PATH_TO_MODEL_CONFIG>  <PATH_TO_CHECKPOINT> <NUM_GPUS> --eval mIoU

Run demo

python ./pipelines/demo/image_demo.py <PATH_TO_IMG> <PATH_TO_MODEL_CONFIG> <PATH_TO_CHECKPOINT> --palette=easy_portrait --out-file=<PATH_TO_OUT_FILE>

Authors and Credits

• Alexander Kapitanov
• Karina Kvanchiani
• Sofia Kirillova

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.