Standard DatasetsIndoor Multiple Person & Object Segmentation Dataset
Advanced datasets and Generative AI are revolutionizing the fashion and e-commerce industries, providing customized experiences and immersive interactions for shoppers worldwide.Our datasets provide valuable insights into fashion trends, styles, and consumer preferences, enabling businesses to deliver personalized shopping experiences.
The "Indoor Multiple Person & Object Segmentation Dataset" is designed for the internet and media & entertainment sectors, featuring a collection of drama images set in indoor living scenarios. This dataset, with an average of 5 to 6 persons per picture, spans Asian, American, and English contexts. It supports detailed semantic segmentation tasks for human body areas, clothing and accessories, and indoor objects.
Sample
Specification
Dataset ID
MD-Image-009
Dataset Name
Indoor Multiple Person & Object Segmentation Dataset
Data Type
ImageVolume
About 7500 images
Data Collection
Internet collected drama images of indoor living scenario, with 5 to 6 persons in average in one picture, covering Asian, American and English.
Annotation
Semantic Segmentation
Annotation Notes
Human boday area semantic segmentation, clothing &accessory semantic segmentation, indoor object semantic segmentation.
Application Scenarios
Media & Entertainment;Internet
Data Collections
Comprising 7,500 images, this dataset offers a glimpse into diverse indoor settings, captured in dramas from various cultures. Each image includes multiple individuals and objects, annotated for semantic segmentation of human body areas, clothing, accessories, and indoor elements. This dataset is a valuable resource for enhancing applications in internet content and entertainment, providing detailed data for realistic and complex scene understanding.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
Fashionpedia [Learn more]
This dataset includes 48,825 clothing images annotated with segmentation masks and fine-grained attributes, built upon an expertly crafted ontology that encompasses 27 main apparel categories and 19 apparel parts. It's designed to advance tasks combining both detection and attributes classification in the fashion domain.
DeepFashion2 [Learn more]
A comprehensive fashion dataset containing 491K images of 13 popular clothing categories from both commercial and consumer sources. Each item is meticulously labeled with attributes such as scale, occlusion, viewpoint, category, and style, making it a versatile benchmark for clothing image understanding.
DeepFashion [Learn more]
This dataset boasts around 800K diverse fashion images with rich annotations, including 46 categories, 1,000 descriptive attributes, bounding boxes, and landmark information. The variety ranges from well-posed product images to consumer photos, providing a broad spectrum for fashion image analysis.
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