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Computer Vision Datasets

Instantly enhance AI model performance with high quality off-the-shelf datasets.

Task Type

All
117
Age
2
Clothing Detection
2
Clothing Segmentation
1
Event Detection
9
Expression
6
Face Anti-spoofing
6
Face Recognition
29
Face Segmentation
1
Facial Landmark
4
Gait Recognition
3
Garbage Classification
1
Gesture Recognition
6
Human Body Attribute
4
Human Body Detection
6
Human Body Landmark
3
Human Body Segmentation
7
Human Body Tracking
3
Human Pose
25
Image Processing
1
Infrared Face
1
Makeup
1
Object Detection and Classification
3
Occluded Face
2
Others
23
Person Re-Identification
4
Pet Recognition
2
Refined Urban Management
4
Scene Understanding
4
Skin Defects
1
Unlabeled Data
11
Vehicle Re-Identification
1
Vehicle Recognition
2
3D Face
4

Modalities

All
117
Image
86
Video
46

1,417 People – 3D Living_Face & Anti_Spoofing Data

1,417 People – 3D Living_Face & Anti_Spoofing Data. 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, 3D Living_Face & Anti_Spoofing.
3D Living_Face & Anti_Spoofing various expressions facial postures anti-spoofing samples multiple light conditions multiple scenes

1,056 People Living_Face & Anti-Spoofing Data

1,056 People Living_face & Anti-Spoofing Data. 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.
Living_face & Anti-Spoofing data face multiple races multiple postures multiple expressions multiple scenes multiple anti-spoofing samples multiple age groups

5,521 People - Re-ID Data in Surveillance Scenes

5,521 People - Re-ID Data in Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes different age groups, different time periods, different shooting angles, different human body orientations and postures, clothing for different seasons. For annotation, the rectangular bounding boxes and 15 attributes of human body were annotated. The data can be used for re-id and other tasks.
Surveillance scenes Re-ID different age groups different time periods different shooting angles different human body orientations and postures clothing for different seasons human body rectangular bounding boxes attributes annotation

23,110 People Multi-race and Multi-pose Face Images Data

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.
Multi-race Multiple light conditions Multi-pose Multi scenes Face Recognition

4,484 People Multi-race – Infrared Face Recognition Data

4,484 people multi-race – infrared face recognition data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The race distribution includes Asian, Black, Caucasian and Brown people. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is DV-DH4,044S305AD. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition. 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.
Multi-race infrared face binocular camera multiple age periods multiple facial postures multiple scenes

87,871 Images of 106 Facial Landmarks Annotation Data (complicated scenes)

87,871 Images of 106 Facial Landmarks Annotation Data (complicated scenes),this dataset includes yellow race, black race, white race and Indian people. In order to be more challenging, the data includes multiple scenes, multiple poses, different ages, light conditions and complicated expressions. This data can be used for tasks such as face detection and face recognition.
106 Facial landmarks Multi-race Multi-expression Facial attributes Complicated scenes

1,196 People Multi-race and Multi-pose Face Images & Videos Data

1,196 People Multi-race and Multi-pose Face Images & Videos Data, 22 images and 40 videos per person. The collection environment includes indoor and outdoor scenes. This data can be used for face recognition and other tasks.
Multiple poses Face recognition Multiple races data

602 People –3,010 Images Multi-Races Human Body Semantic Segmentation Data

602 People –3,010 Images Multi-Races Human Body Semantic Segmentation Data,The data diversity includes headphones, body, background,and glasses.In terms of annotation, we adpoted segmentation annotations on headphones, body, background and glasses.The data can be used for tasks such as human body segmentation and the behavior detection of Video conference.
Human body segmentation different poses different ages different races different collection backgrounds different scenes.

9,000 Images of 180 People - Driver Gesture 21 Landmarks Annotation Data

9,000 Images of 180 People - Driver Gesture 21 Landmarks Annotation Data. This data diversity includes multiple age periods, multiple time periods, multiple gestures, multiple vehicle types, multiple time periods. For annotation, the vehicle type, gesture type, person nationality, gender, age and gesture 21 landmarks (each landmark includes the attribute of visible and invisible) were annotated. This data can be used for tasks such as driver gesture recognition, gesture landmarks detection and recognition.
DMS driver gesture gesture 21 landmarks static gesture dynamic gesture driver gesture recognition gesture landmarks detection gesture landmarks recognition
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Why off-the-shelf Datasets

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    Designed and produced by AI data experts
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    Collected from a varity of real scenes
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