EasyPortrait 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 with high quality annotated masks. This dataset is divided into training set, validation set and test set by hashed 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.

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

本团队荣幸地推出一款大规模图像数据集EasyPortrait，旨在实现人像分割与面部解析。该数据集适用于多种任务，例如会议应用中的背景去除、牙齿美白、面部肌肤提升、红眼消除或眼睛色彩化等。

EasyPortrait数据集容量约为26GB，内含20,000张高质量的RGB图像及其标注的掩码。数据集根据哈希后的主体用户ID划分为训练集、验证集和测试集。其中，训练集包含14,000张图像，验证集包含2,000张图像，测试集包含4,000张图像。

训练图像来源于5,947位独特的用户，验证数据来源于860位用户，测试数据来源于1,570位用户。平均而言，每张EasyPortrait图像拥有254个多边形顶点，由此可知标注质量之高。每个标注均由多边形创建分割掩码。

标注以二维数组的形式呈现，图像格式为*.png，包含多个类别：

| 索引 | 类别 |
|-----:|:-----------|
| 0 | 背景 |
| 1 | 人物 |
| 2 | 皮肤 |
| 3 | 左眉毛 |
| 4 | 右眉毛 |
| 5 | 左眼睛 |
| 6 | 右眼睛 |
| 7 | 嘴唇 |
| 8 | 牙齿 |

此外，我们在annotations/meta.zip文件中为数据集提供了额外的元信息：

| | 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 |

其中：
- attachment_id - 无扩展名的图像文件名
- user_id - 唯一匿名化用户ID
- data_hash - 使用感知哈希计算出的图像哈希
- width - 图像宽度
- height - 图像高度
- brightness - 图像亮度
- train, test, valid - 分别对应训练、测试、验证子集的二进制列|

搜集汇总

背景与挑战

背景概述

EasyPortrait Dataset是一个大规模肖像分割和面部解析数据集，包含20,000张高质量标注的RGB图像，总大小约26GB，划分为训练、验证和测试集。它提供9个类别的精细标注（包括背景、人物、皮肤、面部特征如眼睛和嘴唇），适用于背景去除、面部增强等多种计算机视觉任务。