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 3D high-fidelity synthetic dataset simulates real-world Driver Monitoring System (DMS) environments using photorealistic 3D scene modeling. It includes multi-modal sensor outputs such as camera images, videos, and point clouds, all generated through simulation. The dataset is richly annotated with object classification, detection, and segmentation labels, as well as human pose data (head, eye, arm, and leg position/orientation), camera parameters, and temporal metadata such as illumination and weather conditions. Ideal for training and evaluating models in autonomous driving, robotics, driver monitoring, computer vision, and synthetic perception tasks.

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.

28,565 People Multi-race 7 Expressions Recognition Data. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. For each person, 7 images were collected. The data diversity includes different facial postures, different expressions, different light conditions and different scenes. The data can be used for tasks such as face expression recognition.

23,110 People Multi-race and Multi-pose Face Images Data. This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks.

3,919 People Multi-pose Faces Data, 24 images and 9 videos per person. The collection environment includes indoor and outdoor scenes. This data can be used for face detection, face recognition and other tasks.

The 2,769 People CCTV Re-Identification Dataset in Europe provides high-quality pedestrian data for computer vision research. The data includes both males and females, with a racial distribution of Caucasian, Black, and Asian, and an age range from children to the elderly. The data diversity includes different age groups, different time periods, different cameras, and different body orientations and poses. Each person is annotated with rectangular bounding boxes and 15 body attributes. This data can be use for person re-identification, pedestrian tracking, surveillance analysis, and multi-camera AI models.

165 People - CCTV Re-ID Data in Outdoor Night Scenes. The data scenario is an outdoor night monitoring scenario. The data covers both males and females, with an age distribution from children to middle age, and is based on young people. The data modes include RGB and IR. In terms of annotation, annotate human body rectangular boxes and 15 types of human body attribute information. The data can be used for tasks such as Re ID.

This dataset contains 500,605 individual face images, with each person represented by a single image. The race distribution includes Asian, Black, Caucasian and Brown individuals, the age distribution is ranging from infant to the elderly, the middle-aged and young people are the majorities. Images were collected in both indoor and outdoor environments, capturing diverse facial expressions, postures, and lighting conditions. The data can be used for tasks such as face recognition, biometric authentication, and computer vision model training. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.