Glasses 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 "Glasses Segmentation Dataset" is aimed at the apparel and visual entertainment sectors, incorporating a diverse array of internet-collected images with resolutions from 165 x 126 to 1250 x 1458 pixels. This dataset focuses on semantic segmentation of various types of eyewear, including pure transparent glasses, sunglasses, and translucent glasses, providing detailed annotations for each category.
Sample
Specification
Data Collections
This dataset comprises approximately 13.9k images, each segmented to identify different types of glasses. The detailed semantic segmentation covers a range of eyewear styles, from transparent to sunglasses and translucent options. The dataset is a valuable resource for applications in fashion technology, virtual try-ons, and enhancing digital characters in visual entertainment, offering precise data for realistic eyewear representation and design.
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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