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Shaven Head 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 "Shaven Head Segmentation Dataset" is designed for the media and entertainment industry, featuring a collection of internet-collected images with resolutions ranging from 1360 x 1656 to 2160 x 2702 pixels. This dataset specializes in semantic segmentation, providing annotations for various categories such as the background, obstructions, head, ears, and skin, focusing on individuals with shaven heads for detailed character modeling and digital content creation.

Sample

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Specification

Dataset ID
MD-Image-062
Dataset Name
Shaven Head Segmentation Dataset
Data Type
data-typeImage
Volume
About 1.0k
Data Collection
Internet collected images. Resolution ranges from 1360 x 1656 to 2160 x 2702
Annotation
Semantic Segmentation
Annotation Notes
The annotation categories include background, obstructions, head, ears and skin,etc.
Application Scenarios
Media & Entertainment

Data Collections

This dataset includes approximately 1.0k images, each segmented to identify not only the shaven head but also other relevant features like ears and skin, alongside potential obstructions and the background. The detailed segmentation facilitates the creation of realistic digital representations of individuals with shaven heads, enhancing character design, virtual makeovers, and other visual content in the entertainment sector by providing nuanced insights into head and facial features.

Data Applitcation

Media & Entertainment

Quality Assurance

Quality Assurance

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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