Computer Vision Datasets

This dataset includes 3,010 images of 602 people across multiple races. The semantic segmentation area includes headphones, glasses, body and background.This dataset can be used for training AI models in human body segmentation, video conference behavior detection, and smart vision applications.

This dataset contains 5,957 outdoor scene images with fine semantic segmentation annotations, focusing on the sky region. The data diversity includes multiple scenes, different time periods. For annotation, segmentation annotation was adopted for the sky region in the images. The data can be used for fine semantic segmentation, outdoor scene understanding, and computer vision AI training.

208 Vietnamese– 2D Living Face & Anti-Spoofing Dataset – Multi-Scene, Multi-Light. The collection scenes include indoor and outdoor scenes. The data includes male and female. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The race distribution is Vietnamese. The data includes various expressions, postures, anti-spoofing samples, multiple light conditions, multiple scenes. The data can be used for tasks such as face payment, remote ID authentication, and face unlocking of mobile phone.

200 Vietnamese 3D Living Face & Anti-Spoofing Dataset – Multi-Scene, Multi-Light. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution includes under 18 years old, 18 to 45 years old, 46 to 60 years old, over 60 years old. The device includes iPhone X, iPhone XR, iPhone11, iPhone12, iPhone13. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, liveness detection, and anti-spoofing AI model.

1,417 People 3D Living Face & Anti-Spoofing Dataset – Multi-Scene, Multi-Light. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, liveness detection, and anti-spoofing AI model.

1,056 People Living Face & Anti-Spoofing Dataset – Multi-Scene, Multi-Expression. The collection scenes include indoor and outdoor scenes. The data includes male and female. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The data includes multiple postures, multiple expressions, and multiple anti-spoofing samples. The data can be used for tasks such as face payment, remote ID authentication, and face unlocking of mobile phone.

10 People - 3D&2D Living_Face & Anti_Spoofing Data. The collection scenes is indoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.

40 People – 3D&2D Living_Face & Anti_Spoofing Data. The collection scenes are indoor scenes and outdoor scenes. The dataset includes males and females, the age distribution is 18-57 years old. The device includes cellphone, camera, iPhone of multiple models (iPhone X or more advanced iPhone models). The data diversity includes multiple devices, multiple actions, multiple facial postures, multiple anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 2D Living_Face & Anti_Spoofing, 2D face recognition, 3D face recognition, 3D Living_Face & Anti_Spoofing.

This dataset includes 49,945 fashion images. The gender distribution includes female and male, the race distribution is Asian, Caucasian and black race, the age distribution is teenager, young and middle-aged. The data diversity includes multiple scenes, multiple light conditions, multiple types of costume (upper garment, lower garment, and shoes), and multiple apparel accessories (bag, glasses, accessories, etc.). In terms of annotation, semantic segmentation of 47 categories object (including background, costume and apparel accessory) was adopted. The dataset can be used for tasks such as clothing segmentation, fashion accessory detection, apparel recognition, and AI-powered fashion recommendation systems.