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Panoptic Scenes 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 "Panoptic Scenes Segmentation Dataset" is a comprehensive resource for the robotics and visual entertainment fields, consisting of a wide range of internet-collected images with resolutions from 660 x 371 to 5472 x 3648 pixels. This dataset is aimed at semantic segmentation, capturing diverse elements such as horizontal and vertical planes, buildings, people, animals, and furniture, offering a holistic view of various scenes.

Sample

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Specification

Dataset ID
MD-Image-039
Dataset Name
Panoptic Scenes Segmentation Dataset
Data Type
data-typeImage
Volume
About 21.3k
Data Collection
Internet collected images. Resolution ranges from 660 x 371 to 5472 x 3648;
Annotation
Semantic Segmentation
Annotation Notes
The dataset includes horizontal plane (desktop, ground, ceiling, etc.), vertical plane (wall, etc.), buildings, people, animals, furniture, etc.
Application Scenarios
Robotics;Visual Entertainment

Data Collections

This dataset includes about 21.3k images, each providing detailed segmentation of multiple scene elements, from the architecture to the living entities within it. The inclusion of various planes and objects makes it particularly useful for applications requiring a deep understanding of scene layout and object interaction, such as in advanced robotics for navigation and interaction, as well as in creating immersive environments in visual entertainment projects.

Quality Assurance

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.

Related products

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