Standard DatasetsRoad Scenes 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 "Road Scenes Panoptic Segmentation Dataset" is aimed at applications in visual entertainment and autonomous driving, featuring a collection of internet-collected road scene images with resolutions exceeding 1600 x 1200 pixels. This dataset specializes in panoptic segmentation, annotating every identifiable instance within the images, such as vehicles, roads, lane lines, vegetation, and people, providing a detailed dataset for comprehensive road scene analysis.
Sample
Specification
Dataset ID
MD-Image-088
Dataset Name
Road Scenes Panoptic Segmentation Dataset
Data Type
ImageVolume
About 1k
Data Collection
Internet collected images. Resolution is over 1600 x 1200.
Annotation
Panoptic Segmentation
Annotation Notes
This dataset contains road scenes with panoptic segmentation of all identifiable instances in images including vehicles, roads, lane lines, vegetation, people, etc.
Application Scenarios
Autonomous Driving;Visual Entertainment
Data Collections
This dataset comprises approximately 1k images, each with detailed panoptic segmentation of various elements found in road scenes. The extensive segmentation of components like vehicles, lane markings, surrounding vegetation, and pedestrians offers invaluable data for developing and enhancing autonomous driving systems by providing accurate environmental perception and object recognition capabilities. Additionally, this dataset supports the creation of realistic road environments in visual entertainment, such as video games and simulations, contributing to immersive and interactive user experiences.
Data Applitcation
Autonomous Driving: Improve vehicle perception systems with accurate segmentation of road scenes.
ADAS Development: Enhance lane detection, obstacle avoidance, and pedestrian recognition systems.
Urban Planning: Analyze road usage and traffic patterns through detailed semantic segmentation.
Traffic Surveillance: Monitor and manage road safety with precise scene understanding.
Why choose our Road Scenes Panoptic Segmentation Dataset
High-Quality Annotations: Detailed segmentation of various road scene elements for precise modeling.
High-Resolution Images: Standard 1920 x 1080 pixel images, ideal for professional use.
Versatile Applications: Suitable for a wide range of autonomous driving and ADAS projects.
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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