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Standard Datasets

Indoor Multi-person Panoptic 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 Multi-person Panoptic Segmentation Dataset" is designed for the visual entertainment sector, consisting of a collection of internet-collected indoor images with resolutions exceeding 1543 x 2048 pixels. This dataset emphasizes panoptic segmentation, capturing every identifiable instance within indoor scenes, including people, furniture, tableware, food, and other elements, providing a comprehensive dataset for detailed indoor scene analysis and creation.

Sample

Specification

Dataset ID

MD-Image-087

Dataset Name

Indoor Multi-person Panoptic Segmentation Dataset

Data Type

Image

Volume

About 14kk

Data Collection

Internet collected images. Resolution is over 1543 x 2048.

Annotation

Panoptic Segmentation

annotationNotes

This dataset contains indoor scenes with panoptic segmentation of all identifiable instances in images including people, furniture, tableware and food, etc.

Application Scenarios

Visual Entertainment

Data Collections

This dataset includes approximately 14k images, each meticulously annotated to segment all identifiable instances within indoor environments. The detailed panoptic segmentation of elements ranging from individuals to various objects and furniture enhances the utility of this dataset for applications in visual entertainment, such as in the development of interactive environments for video games, detailed set designs in film production, and immersive experiences in virtual and augmented reality applications, offering rich data for creating realistic and interactive indoor scenes.

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.