Head and Neck Semantic Segmentation Dataset
The progress of video and photo editing applications relies heavily on high-quality datasets for machine learning models training. Our carefully selected datasets play a crucial role in enhancing the abilities of such applications, by offering accurate segmentation and recognition of different elements within images and videos.
The "Head and Neck Semantic Segmentation Dataset" is designed for the e-commerce & retail and media & entertainment sectors, featuring a collection of AI-generated cartoon images with resolutions above 1024 x 1024 pixels. This dataset focuses on semantic segmentation, specifically targeting the main character's head, including face, hair, and any accessories, as well as the neck area up to the collarbone, with an allowance for small, unsegmented parts on the edges.
Sample
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Specification
Dataset ID
MD-Image-092
Dataset Name
Head and Neck Semantic Segmentation Dataset
Data Type
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Volume
About About 14k
Data Collection
AI Generated Images. Resolution is above1024 x 1024.
Annotation
Semantic Segmentation
Annotation Notes
In cartoon images, we mark the main character's head (with face, hair, and accessories) and neck up to the collarbone. Small parts on the edges can be ignored.
Application Scenarios
Media & Entertainment;E-Commerce
Data Collections
This dataset comprises approximately 14k images, each providing detailed segmentation of the cartoon character's head and neck areas. The precise segmentation of these elements, including intricate details like accessories and hair, makes this dataset particularly valuable for applications in digital media, entertainment, and e-commerce, such as character design, virtual try-ons for accessories, and animated content creation, enhancing the visual quality and interaction capabilities of digital characters and products.
Quality Assurance
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Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
FASSEG Repository [Learn more]
This collection offers datasets for frontal face segmentation (Frontal01 and Frontal02) and a dataset for faces in multiple poses (Multipose01).These datasets can be valuable for training models to perform face segmentation in different orientations and conditions.
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