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: Arabic & Thai & Vietnamese & Hindi & English & Chinese Language Dataset

Description: The Arabic Text Dataset contains a collection of text samples written in Arabic. It includes various forms of content, such as news articles, social media posts, literature, and dialogue, spanning different topics and writing styles. This dataset is used for tasks such as natural language processing (NLP), text classification, sentiment analysis, and machine translation in Arabic language applications.

Description: The "Asian Face Occlusion Dataset" is tailored for the visual entertainment industry, comprising a vast collection of internet-collected images, each with a resolution exceeding 2736 x 3648 pixels. This dataset focuses on instance and semantic segmentation of Asian faces, specifically targeting individuals aged between 18 and 50 with a male-to-female ratio of 3:7. The unique aspect of this dataset is the inclusion of various face-covering items, providing a diverse range of occlusion scenarios.

Description: The "Asian Single ID Photo Matting Dataset" is curated for the visual entertainment and social networking service (SNS) sectors, featuring a collection of internet-collected Asian face ID photos, all with a high resolution of 6720 x 4480 pixels. This dataset focuses on contour segmentation, offering pixel-level segmentation specifically tailored to Asian facial features in ID photos, facilitating precise face recognition and editing applications.

Description: The "Asian Student Classroom Emotions Dataset" is specifically designed for educational applications, featuring internet-collected images of Asian students in classroom settings, all at a uniform resolution of 1280 x 720 pixels. This dataset employs bounding box annotations and classification techniques to identify and categorize students' emotional and performance states in the classroom, aiming to enhance educational methodologies and student engagement strategies.

Description: The "Asian Style Gestures Dataset" is curated for the visual entertainment industry, featuring a collection of internet-collected images with resolutions ranging from 530 x 360 to 2973 x 3968 pixels. This dataset specializes in annotations of hands displaying Asian style gestures, such as nods, hearts, rock, OK, putting hands together, clasping hands, etc., utilizing bounding boxes and tags for precise identification.

Description: The Bank Cheque Dataset (Document AI): Synthetic bank cheques consists of artificially generated cheque images designed to replicate the appearance and content of real cheques. It includes various elements such as payee names, amounts, dates, signatures, and cheque numbers. This dataset is used for training and evaluating Document AI systems in tasks like optical character recognition (OCR), cheque processing, and automated data extraction, providing a controlled environment for model development without the privacy concerns of real cheques.

Recording Condition: - Clicked Images - Scanned - Web scrapper

Description: The Bank Statement Dataset (Document AI): Synthetic bank statements includes artificially generated bank statements designed to simulate real financial documents. It features various transaction records, dates, amounts, and account details, structured to mirror real-world formats and content. This dataset is used for training and evaluating Document AI systems in tasks such as optical character recognition (OCR), data extraction, and document analysis, offering a controlled environment without the privacy issues of actual financial data.

Recording Condition: - Scanned - Bank_Statement - Web scrapper

Description: Barcode Tye: Code128, UPC/EAN, DataMatrix, PDF417, Aztec, Multi-code

Recording Device: Honor 9A, Huawei mate 10 pro, iPad, iPhone (6S, 7 Plus, SE, X, 11, 12, 12 mini, 12 Pro Max), Moto (E4, onepower), One plus (6T, 7T, One), Oppo A3s, Real Me, Samsung (A20, A30, A32, M12, M31), Vivo z1pro, Xiaomi Mi10T+

Recording Condition: - Bright_Indoor - Low_Indoor - Low_Outdoor - Normal - Sunny

Description: The "Blur Area Segmentation Dataset" is designed for use in robotics and visual entertainment, composed of internet-collected images with resolutions ranging from 960 x 720 to 1024 x 768 pixels. This dataset focuses on semantic segmentation, specifically targeting blue areas within images. Each blue area is annotated at the pixel level, providing valuable data for applications requiring color-based segmentation or analysis.

Description: The "Car Key Point Identification Dataset" is designed for applications in visual entertainment and autonomous driving, featuring a collection of internet-collected images with a resolution of 640 x 512 pixels. This dataset employs bounding boxes to identify target cars and annotates 14 key points on each vehicle, including the four top points, the four lights, the four wheels, and the glass areas on the front and left side, providing detailed data for car modeling and recognition tasks.

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 "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 "Characters Contour Segmentation Dataset" is specifically designed for Optical Character Recognition (OCR) applications, featuring a collection of internet-collected images with resolutions ranging from 461 x 169 to 1080 x 1350 pixels. This dataset is centered around contour segmentation, focusing on the precise delineation of OCR optical characters to facilitate accurate character recognition and text extraction processes.

Description: The "Characters Relationship Segmentation Dataset" is designed for the robotics and visual entertainment industries, featuring a wide range of internet-collected images with resolutions spanning from 1280 × 720 to 4608 × 3456. This unique dataset focuses on the relationships between humans, and between humans and objects, providing valuable insights for interaction dynamics.

Description: Chinese & English & Tibetan & Uyghur Language Dataset

Description: The Chinese and English Menu Dataset contains images or text samples of restaurant menus that feature both Chinese and English languages. It includes various fonts, layouts, and menu structures, presenting bilingual dish names, descriptions, and prices. This dataset is useful for tasks such as optical character recognition (OCR), machine translation, and menu digitization in multilingual settings.

Description: The Chinese Bills Dataset includes images or text samples of various types of bills, such as invoices, receipts, and statements, written in Chinese. It features diverse formats and content, including item descriptions, amounts, and dates. This dataset is used for tasks like optical character recognition (OCR), financial document processing, and automated data extraction.

Description: The Chinese Handwritten Composition Dataset contains samples of handwritten Chinese text, including compositions, essays, and other long-form text. It features various handwriting styles and levels of complexity, and is used for tasks such as handwriting recognition, text analysis, and machine learning model training.

Description: The Chinese WIFI Prompt Dataset consists of text samples found in WIFI prompts and login screens written in Chinese. It typically includes various prompts, instructions, and error messages related to connecting to or managing WIFI networks. This dataset is used for tasks like text recognition, natural language processing, and improving user interfaces for network connectivity.

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 "Clothes Segmentation Dataset" is crafted for the e-commerce, fashion, and visual entertainment sectors, incorporating a wide array of internet-collected images with resolutions ranging from 183 x 275 to 3024 x 4032 pixels. This dataset specializes in contour and semantic segmentation, featuring around 30 target categories including clothing items, accessories, and body parts, facilitating detailed analysis and application in fashion technology.

Description: The "Clothing Classification Dataset" is an essential resource for the fashion, e-commerce, and digital marketing industries, aiming to streamline the online shopping experience. This dataset encompasses a wide array of clothing items collected from the internet, covering various scenarios such as e-commerce websites, fashion shows, social media platforms, and offline user-generated content. It's designed to support the development of sophisticated algorithms for clothing classification, trend analysis, and personalized recommendation systems.

Description: The "Clothing Keypoints Dataset" aims to enhance the precision of fashion-related AI applications by providing a large-scale collection of images for keypoint detection tasks. This dataset includes internet-collected images that span a wide array of scenarios, including e-commerce platforms, fashion shows, social media, and offline user-generated content. It is meticulously annotated to identify keypoints on clothing items, facilitating the development of algorithms for pose estimation, size fitting, style matching, and interactive shopping experiences. The dataset includes classified labels, bounding boxes, and keypoints for 80 different clothing types, making it a comprehensive resource for improving the accuracy and reliability of fashion AI systems.

Description: The "Clothing Pattern Classification Dataset" is specifically designed to address the needs of the fashion industry, focusing on the classification of various clothing patterns. This dataset gathers internet-collected images that showcase clothing from different scenarios such as e-commerce platforms, fashion shows, social media, and offline user-generated content. It aims to facilitate the development of AI models that can accurately recognize and classify over 30 common clothing patterns, enhancing online shopping experiences and supporting trend analysis.

Description: The "Clothing Segmentation and Fabrics Classification Dataset" merges the complexity of clothing segmentation with the specificity of fabric classification, offering a dual-purpose dataset for the fashion industry. It includes internet-collected images from a variety of sources such as e-commerce websites, fashion shows, social media, and offline user-generated content. The dataset is structured to support the development of AI models that can perform both detailed segmentation of clothing items and classify them into 11 common fabric categories, encompassing 80 distinct clothing types. This dual approach aims to enhance online shopping experiences by providing detailed insights into the type of clothing and fabric, facilitating better inventory management and personalized shopping recommendations.

Description: The "Clothing Segmentation Dataset" is designed to propel the capabilities of AI in the fashion industry by providing a comprehensive collection of images for semantic segmentation tasks. This dataset encompasses internet-collected images from various scenarios such as e-commerce platforms, fashion shows, social media, and offline user-generated content. It focuses on enabling precise segmentation of clothing items, including main human parts, clothing pieces, and accessories, to support the development of advanced AI models for automated image analysis and product categorization.

Description: The "Cloudy Day City Road Dash Cam Video Dataset" is crafted to address the challenges autonomous driving systems face in overcast weather conditions. Captured with driving recorders at a resolution exceeding 1920 x 1080 pixels and a frame rate of over 31 fps, this dataset ensures detailed visibility even under the diffused lighting of cloudy skies. It includes bounding boxes and tags for more than 10 object categories commonly encountered in urban settings, such as humans, cars, electric bicycles, vans, and trucks. This dataset aims to refine AI models' ability to navigate and make informed decisions in less-than-ideal weather conditions, enhancing safety and reliability.

Description: The "Cloudy Day Crossroad Dash Cam Video Dataset" specifically captures the intricate dynamics of crossroad navigation under cloudy weather conditions. This dataset is filmed with high-resolution driving recorders, boasting resolutions over 1920 x 1080 pixels and a frame rate of more than 32 fps, to ensure clarity and detail even in subdued lighting. It annotates more than 10 typical urban object categories, including humans, cars, electric bicycles, vans, and trucks, amidst the unique challenges presented at crossroads during cloudy days. The dataset is an essential resource for developing autonomous driving systems capable of understanding and reacting appropriately to complex urban intersections, especially when visibility is affected by overcast skies.

Description: The "Common Objects Segmentation Dataset" serves the e-commerce and visual entertainment industries with a broad collection of internet-collected images, featuring resolutions ranging from 800 × 600 to 4160 × 3120. This dataset covers a wide array of everyday scenes and objects, including people, animals, furniture, and more, annotated for both instance and semantic segmentation.

Description: The "Damaged Board Parts Segmentation Dataset" is a niche collection tailored for the manufacturing sector, especially in wood and board production. It features internet-collected images with high resolutions ranging from 3024 x 4032 to 2048 x 5750 pixels. This dataset focuses on semantic segmentation of various types of board damage, including cracks, insect damage, and decay, aiding in quality control and manufacturing processes.

Description: 360 degrees walk around videos of cars with damages at a normal, steady pace with top and bottom always visible Damage: a scratch, dent, ding, or crack that is larger than a golf ball in length Outer Panel Damage: bumpers, fenders, quarter panels, doors, hoods, and trunks Location: Asia, US, Canada, and Europe

Recording Device: Mobile Camera

Recording Condition: Mixed Lighting Conditions

Description: 490+ cars and 3958 car photos with annotated images (along with metadata) of damaged cars. Covers all sides of the car (8 photos for each car) - Insurance Claim Process Use Cases.

Recording Device: Mobile Camera

Recording Condition: Mixed Lighting Conditions

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: The "E-commerce Product Dataset" is a comprehensive collection tailored for the e-commerce sector, featuring a wide range of products from 16 main categories including shoes, hats, bags, furniture, digital products, jewelry, and more. With over 200k SKUs, this dataset is equipped with bounding boxes and category tags, making it a pivotal resource for product classification and inventory management.

Description: Our "Eastern Asia Single-person Portrait Matting Dataset" targets the nuanced requirements of the fashion, internet, and entertainment sectors, featuring single-person portraits from Eastern Asia in a variety of settings including indoor, outdoor, street, and sport. This dataset is specially curated for pixel-level fine segmentation tasks, capturing diverse postures and scenarios.

Description: The English & Chinese Handwriting Dataset contains handwritten samples in both English and Chinese, showcasing various writing styles and character complexities. It is typically used for training and evaluating handwriting recognition models, supporting multilingual text analysis, and other related research. The dataset includes a diverse range of characters, digits, words, and sentences in both languages.

Description: The English & Chinese Shopsign Dataset includes images of shop signs that feature both English and Chinese text. It captures various signage elements such as store names, advertisements, promotions, and directions, displayed in diverse fonts, styles, and formats. This dataset is used for tasks like text detection and recognition, multilingual scene understanding, and improving computer vision models for interpreting bilingual signage.

Description: The English & Chinese Special Angle Text Dataset contains images of text displayed at various angles and orientations in both English and Chinese. It includes text from sources like signs, advertisements, and documents that are not presented in standard horizontal formats. This dataset is used for training and evaluating text detection and recognition models, particularly those capable of handling text in non-traditional orientations and perspectives.

Description: The English Menu Dataset includes images or text samples of restaurant menus written in English. It features a variety of fonts, layouts, and formatting styles, with content ranging from dish names to descriptions and prices. This dataset is often used for tasks like optical character recognition (OCR), text extraction, and menu digitization in food-related applications.

Description: The English Scenes Text Dataset consists of images containing natural scenes with embedded English text. The text appears in various forms, such as signs, billboards, and posters, often in diverse fonts, sizes, and orientations. This dataset is commonly used for training and testing models in text detection, recognition, and scene understanding tasks.

Description: The "Escalator Face Bounding Dataset" is specifically designed for use in government and security sectors, featuring a collection of outdoor-collected images with resolutions exceeding 960 x 540 pixels. This dataset employs bounding boxes to annotate the head, face, and entire body of individuals captured in escalator settings. The annotations are meticulously drawn to encompass the entire face, including any masks that might be worn, ensuring comprehensive facial recognition capabilities even in partially obscured conditions.

Description: The "Human Body Semantic Segmentation Dataset" serves the fashion, internet, and entertainment sectors with a diverse collection of human body images. This dataset, featuring an even distribution across genders and ages from various countries, is ideal for applications requiring detailed analysis of human postures, hairstyles, and different scenarios. With fine labeling of 19 human body areas, it facilitates advanced semantic segmentation tasks.

Description: The "Facial 17 Parts Segmentation Dataset" is specifically compiled for the visual entertainment industry, featuring a range of internet-collected facial images with resolutions exceeding 1024 x 682 pixels. This dataset is dedicated to semantic segmentation, delineating 17 facial categories such as eyebrows, lips, eye pupils, and more. It also includes a selection of portrait images with occlusions, adding complexity and diversity to the dataset for more realistic application scenarios.

Description: The "Facial Color Segmentation Dataset" is tailored for the beauty and visual entertainment sectors, consisting of internet-collected images with resolutions from 1028 x 1028 to 6016 x 4016 pixels. This dataset focuses on semantic segmentation of facial skin colors, including black, yellow, white, and brown, facilitating diverse applications in cosmetics, virtual makeovers, and inclusive digital content.

Description: The "Facial Parts Semantic Segmentation Dataset" supports the beauty and media & entertainment sectors, with a collection of images sourced both online and offline. Resolutions vary from 300 x 300 to 4480 x 6720, covering comprehensive facial area categories such as eyes, eyebrows, nose, mouth, hair, and accessories, each meticulously annotated for semantic segmentation and bounding box tasks.

Description: Facial recognition datasets consist solely of images of faces, with no additional annotations. They include diverse examples of facial features, poses, and lighting conditions, and are used to train and evaluate facial recognition systems for tasks like face detection and recognition.

Recording Condition: Lighting Condition: - Bright Light Or Sunlight - Shade Or Overcast - Night Or Dim Light

Description: The "Flying Wire Segmentation Dataset" is specifically developed for the visual entertainment industry, comprising internet-collected images with resolutions exceeding 1024 x 638 pixels. This dataset is focused on instance segmentation, with a primary emphasis on annotating ropes or wires that span between buildings, offering valuable data for creating realistic urban environments in digital content.

Description: Our "Food Contour Matting Dataset" enriches the culinary and visual content domains, featuring ~200 food types from global cuisines. It's designed for businesses in catering, tourism, and entertainment, offering personalized experiences through detailed segmentation annotations.

Description: The "Food Segmentation Dataset" serves the tourism and visual entertainment sectors, consisting of a curated selection of internet-collected images with resolutions from 256 x 256 to 1024 x 768 pixels. This dataset is dedicated to contour segmentation, focusing on common foods and their accompanying plates or bowls, facilitating detailed analysis and representation in various applications.

Description: The "Full Body Clothing Classification Dataset" is specifically curated to support the advancement of AI in recognizing and classifying full-body clothing from a wide range of internet-collected images. With a focus on high-resolution images, specifically 768 x 1024 pixels, this dataset aims to enhance the precision in classifying full-body attire into major categories such as tops, pants, and skirts, further delineating into 30 sub-categories including jackets, sportswear, baseball uniforms, sweaters, sweatpants, jeans, and half skirts, among others. This dataset is designed to facilitate the development of sophisticated AI models that can accurately classify complex clothing types in full-body images, thereby improving the efficiency and user experience of online fashion retail.

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 "Glasses Segmentation Dataset" is aimed at the apparel and visual entertainment sectors, incorporating a diverse array of internet-collected images with resolutions from 165 x 126 to 1250 x 1458 pixels. This dataset focuses on semantic segmentation of various types of eyewear, including pure transparent glasses, sunglasses, and translucent glasses, providing detailed annotations for each category.

Description: The "Hair Semantic Segmentation Dataset" serves the apparel and media & entertainment industries, featuring a curated collection of internet-collected images with resolutions varying from 343 x 358 to 2316 x 3088 pixels. This dataset specializes in high-precision contour and semantic segmentation of hair, offering detailed annotations for a wide range of hairstyles and textures.

Description: The "Hand Key Point Skeleton Dataset" is designed for applications in visual entertainment and augmented/virtual reality (AR/VR), featuring a collection of indoor-collected images with a high resolution of 3024 x 4032 pixels. This dataset focuses on labeling 21 key points of the hand skeleton, capturing specific single-handed or two-handed poses such as forming a heart shape, placing a hand on the cheek, stretching, and more.

Description: Live Photos with Handwritten text for Japanese, Korean & Russian

Recording Device: iPhone & iPad Camera

Recording Condition: - Aggressive Lighting/Glare - Camera Flash On - Colored Light - Low Light, No Camera Flash - Normal

Description: The "Head and Neck Semantic Segmentation Dataset" is designed for the e-commerce & retail and media & entertainment sectors, featuring a collection of AI-generated cartoon images with resolutions above 1024 x 1024 pixels. This dataset focuses on semantic segmentation, specifically targeting the main character's head, including face, hair, and any accessories, as well as the neck area up to the collarbone, with an allowance for small, unsegmented parts on the edges.

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

Description: The "Human And Accessories Segmentation Dataset" is a valuable resource for the apparel, e-commerce, and media & entertainment industries, featuring internet-collected images with resolutions ranging from 584 x 429 to 3744 x 5616. This dataset is rich in diversity, encompassing a wide array of accessories like mobile phones, suitcases, skateboards, and animals, all annotated for semantic segmentation.

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 "Human Body High Precision Segmentation Dataset" is a comprehensive collection aimed at the apparel, e-commerce, and visual entertainment sectors, combining manually shot and internet-collected images with resolutions from 316 × 600 to 6601 × 9900. It focuses on high-precision segmentation of the human body, capturing intricate details of limbs, clothing, facial features, skin, and accessories.

Description: Images are from internet. Resolution ranges from 105 x 251 to 319 x 951.

Description: The "Portrait Matting Dataset" caters to the apparel and media & entertainment sectors, featuring a diverse collection of live screenshot images with resolutions varying from 138 × 189 to 6000 × 4000. This dataset is comprehensive, including single individuals, groups, and their accessories, and is annotated for contour, semantic, and instance segmentation tasks.

Description: The "Human Body Semantic Segmentation Dataset" serves the fashion, internet, and entertainment sectors with a diverse collection of human body images. This dataset, featuring an even distribution across genders and ages from various countries, is ideal for applications requiring detailed analysis of human postures, hairstyles, and different scenarios. With fine labeling of 19 human body areas, it facilitates advanced semantic segmentation tasks.

Description: The "Human Contour Segmentation And Keypoints Dataset" is aimed at the apparel and visual entertainment industries, featuring a collection of internet-collected images with resolutions ranging from 103 x 237 to 329 x 669 pixels. This dataset is focused on contour segmentation and key points annotation, covering comprehensive human body keypoints including facial features, limbs, and extremities, facilitating detailed human posture and movement analysis.

Description: Images are from internet. Resolution ranges from 1280 x 720 to 2048 x 1080.

Description: The "Human Posture Classification Dataset" is designed for applications in visual entertainment and robotics, consisting of a collection of indoor-collected images with high resolutions exceeding 3024 x 4032 pixels. This dataset emphasizes bounding box annotations and tagging to identify half-body portraits and classify them into 14 distinct types of poses, such as crossed hands, hands around the head, and one hand on the cheek, among others.

Description: The "Indoor Facial 130 Expressions Dataset" is designed for applications in media & entertainment and mobile sectors, featuring a collection of internet-collected indoor facial images with resolutions ranging from 443 x 443 to 1127 x 1080 pixels. This dataset specializes in key points annotation, providing 130 key points for each facial expression, offering a detailed foundation for emotion recognition, facial animation, and interactive applications.

Description: The "Indoor Facial 182 Keypoints Dataset" is a specialized resource for the internet, media, entertainment, and mobile industries, focusing on detailed facial analysis. It includes images of 50 individuals in indoor settings, with a balanced gender distribution and ages ranging from 18 to 50. Each face is annotated with 182 key points, facilitating precise facial feature tracking and analysis.

Description: The "Indoor Facial 75 Expressions Dataset" enriches the internet, media, entertainment, and mobile sectors with an in-depth exploration of human emotions. It features 60 individuals in indoor settings, showcasing a balanced gender representation and varied postures, with 75 distinct facial expressions per person. This dataset is tagged with facial expression categories, making it an invaluable tool for emotion recognition and interactive applications.

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 "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: The "Industrial Metal Smelting Flame Classification Dataset" is designed for the industry sector, featuring a collection of internet-collected images of metal smelting flames, all with a resolution of 350 x 350 pixels. This dataset is dedicated to the classification of flame images into 10 categories, including overexposure, black smoke, fire mass, sparks, and various intensities of slag jumping and spatter, providing crucial data for monitoring and optimizing smelting processes.

Description: The Japanese & Korean Language Dataset includes text samples in both Japanese and Korean. It features a range of content such as sentences, phrases, and words, encompassing various contexts and styles. This dataset is used for tasks like natural language processing (NLP), machine translation, and text analysis in multilingual applications.

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 "Glasses Segmentation Dataset" is aimed at the apparel and visual entertainment sectors, incorporating a diverse array of internet-collected images with resolutions from 165 x 126 to 1250 x 1458 pixels. This dataset focuses on semantic segmentation of various types of eyewear, including pure transparent glasses, sunglasses, and translucent glasses, providing detailed annotations for each category.

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 "Low Lighting Dash Cam Video Dataset" is tailored for autonomous driving systems to navigate through low-light conditions, a crucial capability for safe driving during night-time or in poorly lit environments. Captured with driving recorders at resolutions exceeding 1920 x 1080 pixels and a frame rate of more than 30 fps, this dataset focuses on low lighting scenarios across various settings such as crossroads, avenues, and paths. It encompasses bounding boxes and tags for common urban objects like humans, cars, electric bicycles, vans, and trucks, providing a comprehensive view of the challenges faced by autonomous vehicles in reduced visibility.

Description: The "Machine Part Defects Segmentation Dataset" is designed for the manufacturing industry, consisting of internet-collected images, all with a resolution of 1000 x 1000 pixels. This dataset focuses on binary segmentation to identify white defects on machine parts, providing clear annotations that highlight areas of concern for quality control and inspection processes.

Description: The "Machine Parts Segmentation Dataset" is tailored for the manufacturing sector, featuring a collection of internet-collected images with a resolution of 2048 x 1536 pixels. This dataset is specialized in semantic segmentation, polygon, and key points annotations, focusing on contour annotation of machining positions within X-ray images of machine parts, facilitating precise analysis and inspection in manufacturing processes.

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 "Model Clothing Segmentation Dataset" is curated for the e-commerce & retail sector, featuring a collection of internet-collected images with a resolution of 816 x 1224 pixels. This dataset focuses on semantic segmentation of high-resolution images showcasing models in various outfits, encompassing male, female, and children's wear, to accurately reflect real human silhouettes. The annotations include detailed segmentation of the clothing worn by the models, such as hats, shoes, tops, and bottoms.

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 "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 "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 "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 "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 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 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 "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 Pay Slips Dataset (Document AI): Synthetic Pay Slips consists of images of artificially generated pay slips without any annotations. It features various pay slip formats and details such as employee names, salaries, and dates, used for training and testing Document AI systems in tasks like OCR and document processing.

Recording Condition: - Scanned - Web scrapper

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 "Person And Clothes Semantic Segmentation Dataset" is designed for the e-commerce, fashion, and media & entertainment industries, featuring a diverse range of internet-collected images with resolutions spanning from 92 x 153 to 3024 x 5381 pixels. This dataset offers detailed instance and semantic segmentation of clothing items and body parts, including new categories like hats, gloves, and shoes, supporting various applications in online retail and fashion technology.

Description: Type 1: videos of people immediately outside of homes at front doors - Person walks toward/past the front door/home - Person walks away from the door/home - One or more person doing an extended activity (standing, looking around, talking) 6-20ft from doorbell. Type 2: videos of people inside the home engaging in certain actions - Sitting and eating, Working at desk, Reading, Sleeping, waking up and getting out of bed, Exercising / Dancing, Falling down, lying hurt on the floor

Recording Condition: Low Light: 20% - Ambient Indoor/Outdoor Lighting - Twilight/Golden Hour Regular Light: 40% - Normal Indoor/Outdoor - Uniformly Lit - Not Overly Saturated/Harsh Bright Light: 40% - Outdoor, Mid-Afternoon, Clear Sky - Indoor Natural Light, Or Brightly Lit - Avoid Over-Saturation Or Blown-Out Scenes

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 "Portrait Matting Dataset" caters to the apparel and media & entertainment sectors, featuring a diverse collection of live screenshot images with resolutions varying from 138 × 189 to 6000 × 4000. This dataset is comprehensive, including single individuals, groups, and their accessories, and is annotated for contour, semantic, and instance segmentation tasks.

Description: Live Photos with Handwritten text for Japanese, Korean & Russian

Recording Device: iPhone & iPad Camera

Recording Condition: - Aggressive Lighting/Glare - Camera Flash On - Colored Light - Low Light, No Camera Flash - Normal

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 "Pupils Segmentation Dataset" is tailored for applications in the beauty and media & entertainment industries, consisting of internet-collected images with resolutions varying from 90 x 89 to 419 x 419 pixels. This dataset focuses on semantic segmentation, providing subdivision annotations specifically for pupil locations to enhance detailed eye-related features in digital content.

Description: The "Rail Line Labeling Dataset" is tailored for industrial applications, featuring a collection of internet-collected images with a resolution of 1920 x 1080 pixels. This dataset specializes in the detailed labeling of rail lines, including their turns and merges, using polygon annotations. Additionally, trains within these images are labeled with bounding boxes. The dataset specifically focuses on rail networks collected from Wuhan, providing a localized context for rail line analysis and train detection.

Description: The "Rainy Dash Cam Video Dataset" is specifically developed for autonomous driving systems to accurately function under rainy conditions, which pose significant visibility and surface traction challenges. Captured with driving recorders at resolutions exceeding 1920 x 1080 pixels and a frame rate of more than 30 fps, this dataset focuses on rainy day scenarios in urban settings, including crossroads, avenues, and paths. It features bounding boxes and tags for over 10 common urban categories such as humans, cars, electric bicycles, vans, and trucks, under the variable and often difficult lighting conditions that accompany rainy weather.

Description: The "Remote Sensing Changes Detection Dataset" is a pivotal resource for the remote sensing field, featuring internet-collected images at a uniform resolution of 1024 x 1024 pixels. This dataset is specifically annotated for mask segmentation, distinguishing between front-phase and back-phase building labels, to facilitate the detection of changes in urban and rural landscapes.

Description: The "Remote Sensing Object Segmentation Dataset" is a key asset for the remote sensing field, combining images from the DOTA open dataset and additional internet sources. With resolutions ranging from 451 × 839 to 6573 × 3727 pixels for standard images and up to 25574 × 15342 pixels for uncut large images, this dataset includes diverse categories like playgrounds, vehicles, and sports courts, all annotated for instance and semantic segmentation.

Description: The "Remote Sensing Scenes Segmentation Dataset" is a specialized collection for the remote sensing domain, comprised of high-resolution satellite images sourced from the internet, with dimensions ranging from 10752 x 10240 to 12470 x 13650 pixels. This dataset is designed for semantic segmentation, with annotations covering various natural and man-made features such as buildings, forests, water bodies, roads, and farmland.

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: The "Satellite Components Segmentation Dataset" caters to the manufacturing sector, particularly in aerospace and satellite production, featuring internet-collected images with resolutions ranging from 960 x 720 to 1537 x 1018 pixels. This dataset is aimed at semantic segmentation and polygon annotations, covering a wide array of satellite components such as sailboards, antennas, nozzles, and more, to support precision manufacturing and assembly processes.

Description: The "Satellite Ships Segmentation Dataset" is a specialized collection for remote sensing applications, derived from high-resolution satellite imagery with dimensions ranging from 14,722 x 20,949 to 38,133 x 14,604 pixels. This dataset is focused on semantic segmentation, featuring annotations for ships including Automatic Identification System (AIS) information and satellite icon notes, facilitating detailed maritime monitoring and analysis.

Description: The "Satellite Vehicle Bounding Box Dataset" is designed for applications in visual entertainment and autonomous driving, consisting of satellite imagery with pixel resolutions exceeding 5000 x 6000. For annotation purposes, these high-resolution images are segmented into uniform sizes. This dataset specializes in the use of bounding boxes to outline vehicle contours within infrared imagery, focusing solely on the broad category of "CAR" for annotation.

Description: The "Scarf Segmentation Dataset" is curated for the apparel and fashion industries, comprising internet-collected images with resolutions ranging from 504 x 678 to 192 x 2880 pixels. This dataset is dedicated to contour segmentation, focusing on the high-precision delineation of scarf areas, supporting detailed analysis and design applications in fashion technology.

Description: The "Segmentation and Key Points of Human Body Dataset" is designed for the apparel and visual entertainment sectors, featuring a collection of internet-collected images with resolutions ranging from 1280 x 960 to 5184 x 3456 pixels. This dataset is comprehensive, including instance and semantic segmentation of 27 categories of body parts along with 24 key points annotations, providing detailed data for human body analysis and applications.

Description: The "Shaven Head Segmentation Dataset" is designed for the media and entertainment industry, featuring a collection of internet-collected images with resolutions ranging from 1360 x 1656 to 2160 x 2702 pixels. This dataset specializes in semantic segmentation, providing annotations for various categories such as the background, obstructions, head, ears, and skin, focusing on individuals with shaven heads for detailed character modeling and digital content creation.

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: Our "Single-person Portrait Matting Dataset" is a pivotal resource for the fashion, media, and social media industries, providing finely labeled portrait images that capture a wide range of postures and hairstyles from various countries. With a focus on high-resolution images exceeding 1080 x 1080 pixels, this dataset is tailored for applications requiring detailed segmentation, including hair, ears, fingers, and other intricate portrait features.

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 "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 "Sunny Day City Road Dash Cam Video Dataset" captures the vibrant dynamics of city roads under sunny conditions, essential for autonomous driving systems' development. This dataset is recorded with high-resolution driving recorders, boasting over 1920 x 1080 resolution and a frame rate of over 33 fps, ensuring crystal-clear imagery and fluid motion capture. It includes bounding boxes and tags for more than 10 typical urban object categories, including humans, cars, electric bicycles, vans, trucks, and more, providing a rich training ground for AI to recognize and respond to various elements in sunlit urban environments.

Description: The "Sunny Day Crossroads Dash Cam Video Dataset" offers a focused glimpse into the dynamic interactions at crossroads under sunny conditions, a critical aspect of urban driving for autonomous vehicles. Captured with high-resolution driving recorders at over 1920 x 1080 pixels and a frame rate surpassing 34 fps, this dataset provides clear and fluid visuals necessary for detailed analysis and AI training. It encompasses bounding boxes and tags for over 10 typical urban object categories, including humans, cars, electric bicycles, vans, trucks, and more. This rich dataset aims to enhance the decision-making algorithms of self-driving cars at busy intersections, where the complexity of vehicle and pedestrian movements is significantly heightened.

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: The "UAV Aerial Photography Multi-Object Dataset" is designed for smart transportation applications, featuring a collection of internet-collected UAV (Unmanned Aerial Vehicle) aerial photography images with a resolution of 1920 x 1080 pixels. This dataset predominantly covers large-scale scenes such as parking lots and highways, with each image containing over 200 vehicles. Every object within these images is meticulously annotated with a bounding box that aligns with the object's orientation, ensuring precise vehicle detection and tracking.

Description: The "Upper Eyelid Segmentation Dataset" is designed for the beauty and visual entertainment industries, incorporating internet-collected images with resolutions from 100 x 100 to 400 x 400 pixels. This focused dataset is dedicated to semantic segmentation of the upper eyelid, with annotations covering both eyes, facilitating detailed eye makeup applications and character modeling.

Description: Internet collected video clips with average length around 10s, and resolution is over 720 x 1280.

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

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.

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.

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.