High-quality Computer Vision Dataset to Train Your AI Model

Description: Internet collected animal images in variable scenarios like indoor, outdoor, nature, gardon and so on.

Description: The "Cat & Dog Body Segmentation Supplementary Dataset" is tailored for the visual entertainment industry, comprising a variety of internet-collected images with resolutions exceeding 440 x 440 pixels. This dataset focuses on contour segmentation, specifically delineating the outlines of cats and dogs of various breeds, providing detailed data for applications requiring precise pet representations.

Description: The "Cat & Dog Segmentation Dataset" is crafted for the media & entertainment and tourism industries, featuring a broad collection of internet-collected images with resolutions varying from 367 x 288 to 3456 x 4608 pixels. This dataset focuses on contour segmentation and includes diverse annotations such as humans, cats, dogs, and environmental elements like walls, tables, grass, and water surfaces, among others.

Description: The "Human And Multi-object Panoptic Segmentation Dataset" is curated for applications in visual entertainment, featuring a wide array of internet-collected images with resolutions exceeding 1280 x 700 pixels. This comprehensive dataset integrates both instance and semantic segmentation to label a diverse range of elements found in everyday life, including natural scenery, people, buildings, and animals, offering a panoptic view of various scenes and subjects.

Description: 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.

Description: The "Indoor Multiple Person & Object Segmentation Dataset" is designed for the internet and media & entertainment sectors, featuring a collection of drama images set in indoor living scenarios. This dataset, with an average of 5 to 6 persons per picture, spans Asian, American, and English contexts. It supports detailed semantic segmentation tasks for human body areas, clothing and accessories, and indoor objects.

Description: The "Long-range Pedestrian Dataset" is curated for the visual entertainment sector, featuring a collection of outdoor-collected images with a high resolution of 3840 x 2160 pixels. This dataset is focused on long-distance pedestrian imagery, with each target pedestrian precisely labeled with a bounding box that closely fits the boundary of the pedestrian target, providing detailed data for scene composition and character placement in visual content.

Description: The "Multi-person And Appendages Segmentation Dataset" is designed for the visual entertainment sector, featuring a collection of internet-collected images with resolutions exceeding 2736 x 3648 pixels. This dataset employs both instance and semantic segmentation techniques to annotate multiple people and their appendages in various scenes. The appendages include shadows, hand-held objects, riding objects, and more, providing a comprehensive view of human interactions with their environment.

Description: The "Multi-Pet Matting Dataset" is curated for applications in visual entertainment and financial services, featuring a collection of internet-collected images with resolutions exceeding 1920 x 1280 pixels. This dataset focuses on both contour and semantic segmentation of multiple pet instances within each image, specifically limited to cats and dogs. Each pet instance is saved with an individual matting mask, with the mask granularity refined to the hair-strand level, providing detailed data for creating realistic pet representations and interactions in digital content.

Description: The "Multi-Scenario, Multi-Person Instance Segmentation Dataset" is designed for diverse applications in visual entertainment, media & entertainment, and e-commerce & retail sectors. It consists of a collection of internet-collected images with resolutions exceeding 640 x 480 pixels. This dataset is characterized by its variety, featuring different scenarios such as individuals sitting, groups seated together, people holding props, interacting with various attire like hats and bags, and making different gestures. It employs instance segmentation and bounding box annotations to facilitate comprehensive analysis of human subjects within these varied contexts.

Description: The "Old Person and Children Contour Segmentation Dataset" is crafted for the visual entertainment sector, featuring a collection of internet-collected images with resolutions ranging from 867 x 867 to 6000 x 4000 pixels. This dataset specializes in contour segmentation, focusing on delineating the outlines of elderly individuals and children, facilitating age-specific content creation and character modeling.

Description: The "Outdoor Multi-person Panoptic Segmentation Dataset" is tailored for the visual entertainment industry, featuring a collection of internet-collected outdoor images with resolutions ranging from 1543 x 2048 to 3072 x 2304 pixels. This dataset focuses on panoptic segmentation, encompassing multiple people and distinguishable objects such as those on individuals, buildings, vehicles, and plants. Each identifiable instance within the images is annotated, providing a comprehensive view of outdoor scenes.

Description: The "People and Safety Belt Semantic Segmentation Dataset" is specifically curated for industrial applications, consisting of CCTV images captured within a factory environment at a resolution of 1920 x 1080 pixels. This dataset focuses on both instance and semantic segmentation, providing annotations for people and the seat belts they are wearing, aimed at enhancing safety compliance monitoring.

Description: The "Whiskers Segmentation Dataset" is tailored for the beauty and media & entertainment sectors, consisting of internet-collected images with resolutions ranging from 1080 x 1070 to 1080 x 1350 pixels. This dataset focuses on contour segmentation, specifically targeting the segmentation of thick beard contours, aiding in applications related to grooming, virtual styling, and character design.