Standard DatasetsObjects and Distractions 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 "Objects and Distractions Segmentation Dataset" is designed for robotics and visual entertainment sectors, featuring a range of internet-collected images with resolutions between 1365 x 2047 and 4165 x 2737 pixels. This dataset emphasizes semantic segmentation, categorizing images into five main types of interference objects, including target persons, objects, interference items, and various human body parts, facilitating the development of algorithms to distinguish between primary subjects and background distractions.
Sample
Specification
Dataset ID
MD-Image-055
Dataset Name
Objects and Distractions Segmentation Dataset
Data Type
ImageVolume
About 10.8k
Data Collection
Internet collected images. Resolution ranges from 1365*2047 to 4165*2737
Annotation
Semantic Segmentation
Annotation Notes
There are five categories of the interference object like target person, object, interference object, human body (such as hand, head, leg, back, trunk).
Application Scenarios
Robotics;Visual Entertainment
Data Collections
This dataset comprises approximately 10.8k images, each segmented to identify not only the main objects of interest but also potential distractions in the scene. The categorization into specific types of interference objects like human body parts and other objects helps in refining focus and improving object recognition algorithms in robotics, as well as enhancing the clarity and focus of visual content in entertainment applications, by effectively managing and minimizing background noise and distractions.
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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