Computer Vision Datasets

This dataset includes 2,255 images of 451 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.

This dataset includes diverse drivers across multiple ages, time periods, and lighting conditions, with annotated behaviors including dangerous driving, fatigue, and visual movement behaviors. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis, fatigue and drowsiness detection, dangerous driving recognition and driver attention and visual movement monitoring.

This dataset contains 13,757 real-world images including glass windows, glass displays, electronic screens, and other reflective surfaces. The dataset covers various subjects such as animals (cats, dogs, and other species), objects, food, and still-life scenes, with different levels of reflection intensity. The dataset is suitable for tasks including reflection detection, reflection removal, image enhancement, image restoration, object recognition, and visual perception model training.

6,502 People - Mask Face Recognition Image Data, 7 images each person, includes different races like Asian, black, brown and Caucasian, different ages like teenager, young, middle-aged and the elderly, different scenes like indoor and outdoor, different types of mask and light condition. This dataset can be used for the task like masked face recognition.

The 54,415 Videos – Micro-Expression Dataset provides 57 types of subtle facial expressions across diverse populations and environments. Including Asian, Black, Caucasian and Brown individuals; age includes under 18, 18-45, 46-60, and over 60; collection environment includes indoor scenes and outdoor scenes; it can be used in various scenes such as facial recognition, emotion recognition and nonverbal behavior analysis.

This dataset contains high-precision multi-sensor autonomous driving data collected from real vehicles operating in Japan. The dataset supports perception model development, sensor fusion, 3D object detection, multi-object tracking, lane detection, HD map construction, localization, and algorithm validation. This dataset is well suited for autonomous vehicle perception and automotive AI model training.

13,587 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.

This dataset contains 1,007 people with 200 images per subject, totaling 201,400 images. Each subject was captured under 4 kinds of light conditions, 10 kinds of occlusion cases (including non-occluded case) and 5 kinds of face poses. This data can be applied to computer vision tasks such as occluded face detection, recognition, and pose-invariant face recognition tasks.