Standard DatasetsCommon Objects 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 "Common Objects Segmentation Dataset" serves the e-commerce and visual entertainment industries with a broad collection of internet-collected images, featuring resolutions ranging from 800 × 600 to 4160 × 3120. This dataset covers a wide array of everyday scenes and objects, including people, animals, furniture, and more, annotated for both instance and semantic segmentation.
Sample
Specification
Dataset ID
MD-Image-024
Dataset Name
Common Objects Segmentation Dataset
Data Type
ImageVolume
About 140.7k
Data Collection
Internet collected images. Resolution ranges from 800 × 600 to 4160 × 3120.
Annotation
Semantic Segmentation,Instance Segmentation
Annotation Notes
It includes categories of common scenes and objects in life such as people, animals, furniture, food, beverages, clothing, buildings, natural floors, vehicles, electronic products, office supplies, kitchen supplies, toilet supplies, daily items, etc.
Application Scenarios
E-Commerce;Visual Entertainment
Data Collections
This dataset comprises around 140.7k images, each depicting various common objects and scenes from daily life, such as clothing, buildings, natural landscapes, and vehicles. The diverse categories and detailed annotations make it a versatile resource for developing sophisticated segmentation algorithms, enhancing applications in online retail, augmented reality, and content creation by providing a comprehensive understanding of everyday objects and environments.
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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