High-quality Computer Vision Dataset to Train Your AI Model

Description: The "CCTV Traffic Scene Semantic Segmentation Dataset" offers a unique perspective for autonomous driving development, capturing the intricacies of traffic scenes from a stationary point of view. Utilizing high-resolution CCTV footage from road monitoring cameras, with resolutions exceeding 1600 x 1200 pixels and a frame rate of over 7 fps, this dataset provides detailed instance segmentation of various elements in traffic, including humans, animals, cycling vehicles, automobiles, and road barriers. It also encompasses a range of weather conditions, offering a robust dataset for training AI systems to understand and interpret diverse traffic scenarios from a fixed vantage point.

Description: The "City Sky Contour Segmentation Dataset" is curated for the visual entertainment sector, featuring a collection of internet-collected images with a high resolution of 3000 x 4000 pixels. This dataset is dedicated to contour segmentation, focusing on capturing the sky in urban settings with elements such as buildings and plants, providing a detailed backdrop for various visual content creation.

Description: The "Dashcam Traffic Scenes Semantic Segmentation Dataset" is essential for pushing the boundaries of autonomous driving technologies. This dataset contains driving recorder images with a resolution of about 1280 x 720 pixels, segmented semantically to reflect various elements of urban and suburban traffic environments. It comprehensively categorizes 24 different objects and scenarios, including sky, people, motor vehicles, non-motorized vehicles, highways, pedestrian paths, zebra crossings, trees, buildings, and more. This detailed semantic segmentation allows autonomous driving systems to better understand and interpret the complexities of the road, enhancing navigation and safety protocols.

Description: The "Drivable Area Segmentation Dataset" is meticulously crafted to enhance the capabilities of AI in navigating autonomous vehicles through diverse driving environments. It features a wide array of high-resolution images, with resolutions ranging from 1600 x 1200 to 2592 x 1944 pixels, capturing various pavement types such as bitumen, concrete, gravel, earth, snow, and ice. This dataset is vital for training AI models to differentiate between drivable and non-drivable areas, a fundamental aspect of autonomous driving. By providing detailed semantic and binary segmentation, it aims to improve the safety and efficiency of autonomous vehicles, ensuring they can adapt to different road conditions and environments encountered in real-world scenarios.

Description: Collect images (1 Enrollment photo, 20 Historical photos per Identity) and videos (1 Indoor, 1 Outdoor) from unique identities

Description: The "Indoor Objects Segmentation Dataset" serves the advertisement, gaming, and visual entertainment sectors, offering high-resolution images ranging from 1024 × 1024 to 3024 × 4032. This dataset includes over 50 types of common indoor objects and architectural elements, such as furniture and room structures, annotated for instance, semantic, and contour segmentation.

Description: CCTV cameras Images. Resolution is over 1920 x 1080 and the number of frames per second of the video is over 30.

Description: Images of landmarks within the context of their environment

Recording Device: Mobile Camera

Recording Condition: - Daylight - Night - Overcast/Rain

Description: The "Lane Line Segmentation Dataset" is designed to accelerate advancements in autonomous driving technologies, specifically focusing on lane detection and segmentation. It includes a vast array of images from driving recorders, segmented into 35 distinct categories to cover a comprehensive range of road markings such as various solid and dashed lines in white and yellow. This dataset aims to refine the precision of AI in identifying lane boundaries, crucial for the safe navigation of autonomous vehicles.

Description: The "Lane Merging and Fork Area Segmentation Dataset" specifically addresses the complexities of lane merging and forking, critical scenarios in autonomous driving. This dataset, consisting of driving recorder images, is annotated for binary segmentation, focusing on areas where lanes merge or branch off. It includes detailed labels for lane merging areas, lane fork areas (marked by triangular inverted lines), and potential obstructions such as vehicles, trees, road signs, and pedestrians. This dataset is a vital tool for training AI models to navigate these challenging road situations, ensuring smoother and safer autonomous driving experiences.

Description: The "Multiple Scenarios And Persons Semantic Segmentation" dataset is tailored for the visual entertainment industry, comprising internet-collected images with resolutions from 1280 x 720 to 6000 x 4000. It focuses on multi-person scenes across urban, natural, and indoor settings, providing detailed annotations for human figures, accessories, and backgrounds.

Description: The "Outdoor Building Panoptic Segmentation Dataset" is curated for the visual entertainment industry, consisting of a collection of internet-collected outdoor images with high resolutions exceeding 3024 x 4032 pixels. This dataset focuses on panoptic segmentation, capturing every identifiable instance within the outdoor scenes, including buildings, roads, people, cars, and more, providing a comprehensive dataset for detailed environmental analysis and creation.

Description: The "Outdoor Objects Semantic Segmentation Dataset" is developed for applications in media & entertainment and robotics, consisting of a variety of internet-collected images with resolutions ranging from 1024 x 726 to 2358 x 1801 pixels. This dataset employs bounding box and key points annotations to segment various outdoor elements, including human body parts, natural scenery, architectural structures, pavements, transportation means, and more.

Description: 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.

Description: 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.

Description: The "Road Scene Semantic Segmentation Dataset" is specifically designed for autonomous driving applications, featuring a collection of internet-collected images with a standard resolution of 1920 x 1080 pixels. This dataset is focused on semantic segmentation, aiming to accurately segment various elements of road scenes such as the sky, buildings, lane lines, pedestrians, and more, to support the development of advanced driver-assistance systems (ADAS) and autonomous vehicle technologies.

Description: 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.

Description: Our "Sky Outline Matting Dataset" caters to the internet, media, and mobile industries with a curated selection of sky images. This dataset features diverse sky conditions including sunny, cloudy, sunrise, sunset, and more, with pixel-level fine segmentation for detailed outline extraction, suitable for various applications.

Description: The "Sky Segmentation Dataset" is meticulously curated for the visual entertainment industry, featuring manually captured images with resolutions varying from 937 × 528 to 9961 × 3000. This collection is dedicated to the segmentation of skies across different times of the day and night, providing a dynamic range of outdoor sky scenarios for comprehensive mask segmentation tasks.

Description: The "Walkway Segmentation Dataset" is crafted to enhance the safety and efficiency of autonomous driving systems by focusing on the accurate identification and segmentation of pedestrian walkways. This dataset, containing images from driving recorders, is crucial for training AI models to distinguish between drivable areas and pedestrian zones. By segmenting pedestrian walking areas through both instance and binary segmentation techniques, it provides a critical resource for developing autonomous vehicles that can safely navigate urban environments.