コンピュータビジョン
音声認識
| データセット名 | 製品タイプ | 収集内容 | データ規模 | 用途 |
|---|---|---|---|---|
| 1,000 people, Multiple Races,7 types of facial emotion recognition data | Image | Each person has seven expressions collected: normal, happy, surprised, sad, angry, disgusted, and fearful. | 1,000 people | Facial expression recognition |
| 3,000 Images-Human Face Segmentation Data | Image | Segmentation annotation of human face, facial features,body and | 3,000 images | Face segmentation |
| 3,000 Images of 106 Facial Landmarks Annotation Data (complex scenarios) | Image | 9 facial attributes and 106 facial landmarks 9 facial attributes: including gender, age, race, wearing cap/hat or not, wearing glasses or not, background, face orientation, eye status, mouth status | 3,000 images | Facial landmark location Face recognition |
| 100 People-Driver Behavior Collection Data | Image | Dangerous driving behavior, fatigue driving behavior, visual movement behavior | 100 people | Driver behavior Detection |
| 100 People-Liveness Detection Data | Image | Living body action video, lip language video, Non-living body video (anti-spoofing sample), Anti-spoofing data of lip language, Anti-spoofing data of RGB images | 100 people | liveness detection |
| 50,016 Gesture Recognition Data | Image | 18 static hand gestures and 21 keypoints of the hand landmarks | 50,016 data | Gesture recognition |
| 100 People-Human Face Recognition Data in Surveillance | Image | Human facial information in surveillance. The labels of gender and age were annotated. | 100 people | Face recognition |
| 1,000 people, 7,156 Cross-age Face Images data | Image | The age spans are 10 years, and 4 images of each person in different ages were collected at least. | 1,000 people | Face recognition |
| 1,000 People multi-race and Multi-pose Face Images Data | Image | Image quantity: 29 images per person (14 multi-pose face images of indoor scenes + 14 multi- pose face images of outdoor scenes + 1 ID photo) The labels of race, gender, age and face pose were annotated. | 1,000 people | Face recognition |
| 10 Categories-200 Groups of Refined Urban Management Data | Image | 18 subcategories such as streets, snack streets, shop entrance, corridor, community entrance, construction sites, etc., and each group of data contains 2 images from different angles | 200 groups | Refined urban management |
| 3,000 images Natural Scene OCR Data of 12 Languages | Image | Include Asian language family, European language family, and row-level quadrilateral bounding box annotation and transcription for the texts | 3,000 images | Multilingual OCR task |
| 100 People with Occlusion and Multi-pose Face Recognition Data | Image | There're 200 images includes 4 kinds of light conditions * 10 kinds of occlusion cases (including non-occluded case) * 5 kinds of face pose. For each image, the labels of face pose and occlusion were annotated. | 100 people | Face recognition |
| 100 People- 3D Liveness Detection Data | Image | Living face image data, anti-spoofing data of living face image and anti-spoofing data of mask image of three races with different skin color. Each image corresponds to a depth image, a depth information file, a camera internal parameters file | 100 people | Face recognition liveness detection |
| 100 People- Electric Bicycle Entering Elevator Data | Image | For each subject, 1 images and 4 videos were collected and the gender, race and age should be labeled. For each video, the labels of collecting scene and electric bicycle model were annotated. | 100 people | Refined urban management |
| 1,435 Images- Alpha Matte Human Body Segmentation Data( fine version) | Image | Collecting half body or full body images, and alpha matte segmentation annotation was done to the collecting human body. Label the subject’s race, gender, age, collecting scene. | 1435 images | Semantic Segmentation |
| 200 People- Gait Recognition Data in Surveillance | Image | Each subject walked in slow speed, normal speed and fast speed according to the specified walking route. Each subject should walk 9 times with 3 seasonal clothes( summer, autumn and winner) respectively. | 200 People | Gait recognition |
| 200 People- Re-ID Data in Real Surveillance Scenes | Image | Collect 8 kinds of human body orientation, and add bounding boxes and 15 attributes to human body. | 200 people | Re-ID |
| 200 People- Re-ID Data in Surveillance | Image | Add bounding boxes and 15 attributes to human body. The gender, age, race, collecting scene, category of clothes, camera number, and camera height of subject should be labeled. | 200 people | Re-ID |
| 200 Yellow People - Multi-Pose Face Images & Videos Data | Image | Face pose, head pose, nationality, gender, collecting environment and age | 200 people | Face recognition |
| データセット名 | 製品タイプ | 収集内容 | データ規模 | 用途 |
|---|---|---|---|---|
| 300 Hours, 10 Dialects Speech Data by Mobile phone | Speech | Mobile phone | 300 hours | Speech recognition Dialect recognition |
| Interspeech_ Accented English Speech Recognition Competition Data | Speech | Mobile phone | 200 hours, 528 speakers | Speech recognition Language recognition |
| 50 People- Far-field Speech Data in Home Environment | Speech | microphone array | 50 speakers | Speech enhancement Speech recognition |
| 200 Hours- 10 Foreign Languages Speech Data by Mobile phone | Speech | Mobile phone | 200 hours |
Acoustic study Language model training Algorithm research |
Note: Please apply for datasets reasonably according to the research field. The maximum number of applications for Computer Vision datasets is 6 sets.
Note: Please apply for datasets reasonably according to the research field. The maximum number of applications for speech recognition datasets is 4 sets.
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