Premium Object & Contour Segmentation Datasets for Computer Vision

Description: Natural or generic object images with annotation are used for general object detection tasks. They contain real-world images of everyday objects labeled with bounding boxes and class names. These datasets help train and evaluate computer vision models for accurate object recognition and localization in practical applications.

Description: Stain Recognition Images Type 1- Stain Recognition Type 2- Fridge Item Detection

Description: Sets of still images taken in either daytime or nighttime settings where natural or artificial lighting create a digital artifact known as a ghost.

Recording Device: iPhone & iPad Camera

Recording Condition: - Day Time - Night Time

Description: The "Main Objects Segmentation Dataset" is designed for applications in robotics and visual entertainment, comprising a vast collection of internet-collected images with resolutions ranging from 189 x 223 to 5472 x 3648 pixels. This dataset focuses on contour and semantic segmentation of a single labeled subject in each image, providing a clear and isolated view of the primary object for detailed analysis and application.

Description: The "Multiple Objects Matting Dataset" is designed for use in robotics and visual entertainment, featuring a vast collection of internet-collected images with resolutions ranging from 1080 x 1362 to 6000 x 4000 pixels. This dataset specializes in segmentation, providing the original image, a transparent effect image, and a mask black-and-white image for the main object, enabling detailed analysis and application in various technological solutions.

Description: The "Nails Contour Segmentation Dataset" is crafted for the beauty industry, featuring a collection of offline human fingernail images, all at a uniform resolution of 1920 x 1080 pixels. This dataset specializes in semantic segmentation, with a focus on the detailed contour of fingernails, supporting applications in nail art design and virtual nail try-on technologies.

Description: The "Object Contour Matting Dataset" is a versatile collection tailored for the e-commerce, internet, and mobile sectors, encompassing a wide range of objects like clothing, accessories, merchandise, plants, and food. This dataset focuses on contour segmentation of the main object, making it a valuable resource for applications that require precise object outline extraction.

Description: The "Objects and Distractions Segmentation Dataset" is designed for robotics and visual entertainment sectors, featuring a range of internet-collected images with resolutions between 1365 x 2047 and 4165 x 2737 pixels. This dataset emphasizes semantic segmentation, categorizing images into five main types of interference objects, including target persons, objects, interference items, and various human body parts, facilitating the development of algorithms to distinguish between primary subjects and background distractions.

Description: The "Obvious Objects Segmentation Dataset" is a specialized collection aimed at the media and visual entertainment sectors, featuring internet-collected images all at a uniform resolution of 1536 x 2048 pixels. This dataset is dedicated to the segmentation of salient objects that are immediately noticeable and attract attention in an image, utilizing both semantic and contour segmentation techniques to define these objects at the pixel level.

Description: The "Pig Contour Segmentation Dataset" is tailored for the animal husbandry industry, comprised of images captured from CCTV viewpoints with a high resolution of 3072 x 2048 pixels. This dataset focuses on semantic segmentation, providing detailed annotations for the contour and center points of pigs, facilitating monitoring and management in pig farming operations.

Description: The "Single Hand Contour Segmentation Dataset" is aimed at the visual entertainment industry, featuring a collection of internet-collected images with a resolution of 1080 x 1920 pixels. This dataset focuses on contour segmentation, specifically targeting the annotation of a single hand. If small accessories are present on the hand, they are also included in the segmentation, distinguishing the hand and its adornments from the background.

Description: The "Single Nail Contour Segmentation Dataset" is curated for the visual entertainment sector, comprising a collection of internet-collected images, each with a resolution of approximately 100 x 100 pixels. This dataset focuses on contour segmentation, specifically targeting the outlines of individual fingernails, providing detailed data for applications requiring precise nail representation.

Description: The "Specified Object Contour Segmentation Dataset" is aimed at robotics and visual entertainment sectors, consisting of internet-collected images with resolutions varying from 500 x 334 to 3956 x 2319 pixels. This dataset focuses on contour segmentation, with annotations targeting specified objects and scenes, such as goldfish, frogs, piers, and volcanoes, offering detailed outlines for precise object identification and scene analysis.

Description: The "Tooth Semantic Segmentation Dataset" is tailored for the healthcare sector, comprising a collection of internet-collected images with a resolution of 256 x 256 pixels. This dataset is dedicated to semantic segmentation, focusing on labeling different parts of the teeth, including the lower row, incisors, and the upper row, from various angles to provide detailed dental imagery for analysis and educational purposes.

Description: The "Traffic Sign Relationships Dataset" is designed for applications in visual entertainment and autonomous driving, featuring a collection of internet-collected images with a resolution of 1920 x 1080 pixels. This dataset emphasizes the relationship between traffic signs and roadways, with traffic signs annotated using bounding boxes and the corresponding road sections marked with polygons to illustrate the connection between the signs and their relevant road areas.

Description: Internet collected video clips with average length around 10s, and resolution is over 1920 x 1080.

Description: The "Windows Segmentation Dataset" is specifically compiled for the manufacturing sector, focusing on the production and quality control of window units. It consists of internet-collected images with a resolution spectrum from 150 x 150 to 1160 x 2120 pixels. The dataset is designed for semantic segmentation and bounding box tasks, encompassing a variety of window designs and styles.