Standard DatasetsPUBG Game 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 "PUBG Game Scenes Segmentation Dataset" is specifically designed for gaming applications, featuring screenshots from the popular game PUBG with resolutions of 1920 × 886, 1280 × 720, and 1480 × 720 pixels. It encompasses 17 categories for instance and semantic segmentation, including characters, vehicles, landscapes, and in-game items, providing a rich resource for game development and analysis.
Sample
Specification
Dataset ID
MD-Image-023
Dataset Name
PUBG Game Scenes Segmentation Dataset
Data Type
ImageVolume
About 11.2k
Data Collection
Game screenshot, with image resolution of 1920 × 886, 1280 × 720 and 1480 × 720 pixels.
Annotation
Semantic Segmentation,Instance Segmentation
Annotation Notes
There are 17 categories including battlefield heroes, teammates, enemies, flat land, indoor ground, slope, sky, water area, hills, trees, obstacles, man-made buildings, bridges, and large vehicles such as supply bags, guns, medicine bags, Collectible items, cars, doors, windows/indoor.
Application Scenarios
Game
Data Collections
This dataset includes approximately 11.2k images, capturing diverse scenes from PUBG, such as battlefields, indoor settings, and natural landscapes. Each image is annotated across 17 categories, from players and terrain types to man-made structures and collectible items, offering detailed insights into game environment segmentation. This dataset supports advancements in game design, AI-driven gameplay analysis, and interactive entertainment experiences.
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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