Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

This dataset is a curated collection of fashion product images paired with their titles and descriptions, designed for training and fine-tuning multimodal AI models. Originally derived from Param Aggraval's "Fashion Product Images Dataset," it has undergone extensive preprocessing to improve usability and efficiency.

Preprocessing steps include:
1. Resize all images to a size of 256 X 256 px, preserving their original aspect ratio.
2. Streamlining the reference CSV file to retain only essential fields: image file name, display name, product description, and category.
3. Removing redundant style JSON files to minimize dataset complexity.

These optimizations have reduced the dataset size by 95%, making it lighter and faster to use without compromising data quality. This refined dataset is ideal for research and applications in multimodal AI, including tasks like product recommendation, image-text matching, and domain-specific fine-tuning.

Elevate your AI and machine learning projects with our comprehensive fashion image dataset, carefully curated to meet the needs of cutting-edge applications in e-commerce, product recommendation systems, and fashion trend analysis.

Our fashion product images dataset includes over 111,000+ high-resolution JPG images featuring labeled data for clothing, accessories, styles, and more. These images have been sourced from multiple platforms, ensuring diverse and representative content for your projects.

Why Choose Our Fashion Dataset?

• Extensive Image Collection: Gain access to a vast library of 111K+ fashion images, perfect for training machine learning models with precision.
• Detailed Labels: The dataset includes annotated images for garments, accessories, and various fashion styles to enhance model accuracy.
• Versatile Applications: Ideal for e-commerce platforms, AI-based fashion assistants, trend analysis, and product personalization.
• Quality You Can Trust: Download a sample dataset to evaluate the quality and compatibility before diving into the complete collection.

Whether you're building a product recommendation engine, a virtual stylist, or conducting advanced research in fashion AI, this dataset is your go-to resource.

Download and Explore the Fashion Dataset Today!

Get started now and unlock the potential of your AI projects with our reliable and diverse fashion images dataset. Perfect for professionals and researchers alike.

What’s Included

Introducing the Bahasa Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Bahasa language.

Containing a total of 2000 images, this Bahasa OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Bahasa text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native Bahasa people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Bahasa text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Bahasa crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Bahasa language. Your journey to enhanced language understanding and processing starts here.

This dataset features over 200,000 high-quality images of jewelry sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of flower imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions focused on flower photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as particular flower species or geographic regions to be met efficiently.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, ensuring a vast array of flower species, colors, and environmental settings. The images feature varied contexts, including natural habitats, gardens, bouquets, and urban landscapes, providing an unparalleled level of diversity.

3. High-Quality Imagery: the dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and practical perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. I-Ready Design: this dataset is optimized for AI applications, making it ideal for training models in tasks such as image recognition, classification, and segmentation. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance: the dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases 1. Training AI systems for plant recognition and classification. 2. Enhancing agricultural AI models for plant health assessment and species identification. 3. Building datasets for educational tools and augmented reality applications. 4. Supporting biodiversity and conservation research through AI-powered analysis.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

Description:

👉 Download the dataset here

This dataset is specifically designed for the classification of e-commerce products based on their images, forming a critical part of an experimental study aimed at improving product categorization using computer vision techniques. Accurate categorization is essential for e-commerce platforms as it directly influences customer satisfaction, enhances user experience, and optimizes sales by ensuring that products are presented in the correct categories.

Data Collection and Sources

The dataset comprises a comprehensive collection of e-commerce product images gathered from a diverse range of sources, including prominent online marketplaces such as Amazon, Walmart, and Google, as well as additional resources obtained through web scraping. Additionally, the Amazon Berkeley Objects (ABO) project has been utilized to enhance the dataset in certain categories, though its contribution is limited to specific classes.

Download Dataset

Dataset Composition and Structure

The dataset is organized into 9 distinct classes, primarily reflecting major product categories prevalent on Amazon. These categories were chosen based on a balance between representation and practicality, ensuring sufficient diversity and relevance for training and testing computer vision models. The dataset's structure includes:

18,175 images: Resized to 224x224 pixels, suitable for use in various pretrained CNN architectures.

9 Classes: Representing major e-commerce product categories, offering a broad spectrum of items typically found on online retail platforms.

Train-Val-Check Sets: The dataset is split into training, validation, and check sets. The training and validation sets are designated for model training and hyperparameter tuning, while a smaller check set is reserved for model deployment, providing a visual evaluation of the model's performance in a real-world scenario.

Application and Relevance

E-commerce platforms face significant challenges in product categorization due to the vast number of categories, the variety of products, and the need for precise classification. This dataset addresses these challenges by offering a well-balanced collection of images across multiple categories, allowing for robust model training and evaluation.

This dataset is sourced from kaggle.

The rapid adoption of AI technologies across various industries, including healthcare, finance, and autonomous vehicles, is driving the demand for high-quality training datasets essential for developing accurate AI models. According to the analyst from Verified Market Research, the AI Training Dataset Market surpassed the market size of USD 1555.58 Million valued in 2024 to reach a valuation of USD 7564.52 Million by 2032.

The expanding scope of AI applications beyond traditional sectors is fueling growth in the AI Training Dataset Market. This increased demand for Inventory Tags the market to grow at a CAGR of 21.86% from 2026 to 2032.

AI Training Dataset Market: Definition/ Overview

An AI training dataset is defined as a comprehensive collection of data that has been meticulously curated and annotated to train artificial intelligence algorithms and machine learning models. These datasets are fundamental for AI systems as they enable the recognition of patterns.

This dataset features over 750,000 high-quality images of furniture sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of flower imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions focused on flower photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as particular flower species or geographic regions to be met efficiently.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, ensuring a vast array of flower species, colors, and environmental settings. The images feature varied contexts, including natural habitats, gardens, bouquets, and urban landscapes, providing an unparalleled level of diversity.

3. High-Quality Imagery: the dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and practical perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. I-Ready Design: this dataset is optimized for AI applications, making it ideal for training models in tasks such as image recognition, classification, and segmentation. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance: the dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases 1. Training AI systems for plant recognition and classification. 2. Enhancing agricultural AI models for plant health assessment and species identification. 3. Building datasets for educational tools and augmented reality applications. 4. Supporting biodiversity and conservation research through AI-powered analysis.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

This dataset features over 750,000 high-quality images of cars sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of flower imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities: the images are collected through a proprietary gamified platform for photographers. Competitions focused on flower photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as particular flower species or geographic regions to be met efficiently.

2. Global Diversity: photographs have been sourced from contributors in over 100 countries, ensuring a vast array of flower species, colors, and environmental settings. The images feature varied contexts, including natural habitats, gardens, bouquets, and urban landscapes, providing an unparalleled level of diversity.

3. High-Quality Imagery: the dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and practical perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. I-Ready Design: this dataset is optimized for AI applications, making it ideal for training models in tasks such as image recognition, classification, and segmentation. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance: the dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases 1. Training AI systems for plant recognition and classification. 2. Enhancing agricultural AI models for plant health assessment and species identification. 3. Building datasets for educational tools and augmented reality applications. 4. Supporting biodiversity and conservation research through AI-powered analysis.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

A comprehensive dataset of 340K+ jewelry images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification & segmentation

A comprehensive dataset of 15M+ images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, this dataset is ideal for AI model training in image recognition, classification, and segmentation.

What’s Included

Introducing the Finnish Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Finnish language.

Containing a total of 2000 images, this Finnish OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Finnish text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native Finnish people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Finnish text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Finnish crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Finnish language. Your journey to enhanced language understanding and processing starts here.

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

A dataset of 80K+ construction site images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, the dataset is ideal for AI model training in image recognition, classification, and segmentation.

A dataset of 20K+ parking lot images sourced globally, featuring full EXIF data, including camera settings and photography details. Enriched with object and scene detection metadata, the dataset is ideal for AI model training in image recognition, classification, and segmentation.

What’s Included

Introducing the German Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the German language.

Containing a total of 2000 images, this German OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible German text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native German people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of German text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native German crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the German language. Your journey to enhanced language understanding and processing starts here.

1) Data Introduction • The Egg Image Dataset is constructed by collecting images of eggs captured in real-world environments, classified based on whether the eggs are damaged or not damaged.

2) Data Utilization (1) Characteristics of the Egg Image Dataset: • It includes images collected from various real-world settings such as kitchens, farms, and markets, making it highly effective for model training and improving data generalization. • The dataset provides a clear distinction between damaged and undamaged eggs, making it suitable for solving problems related to object recognition and quality inspection.

(2) Applications of the Egg Image Dataset: • Development of Object Recognition and Quality Classification Models: It can be used to train AI models to automatically detect and classify eggs based on their damage status. • Utilization in Research and Development (R&D): The dataset can be applied to various R&D projects, including product quality management and the development of automated inspection systems.

AI Training Dataset Market, by Type (Text, Audio, Image & Video), End-use Industry, and Geography - Global Forecast to 2029

1. Specifications Product : Biometric Data

Data size : 200,000 ID

Race distribution : black people, Caucasian people, brown(Mexican) people, Indian people and Asian people

Gender distribution : gender balance

Age distribution : young, midlife and senior

Collecting environment : including indoor and outdoor scenes

Data diversity : different face poses, races, ages, light conditions and scenes Device : cellphone

Data format : .jpg/png

Accuracy : the accuracy of labels of face pose, race, gender and age are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go Biometric Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

1. Specifications Data size : 60,000 ID

Population distribution : the race distribution is Asians, Caucasians and black people, the gender distribution is male and female, the age distribution is from children to the elderly

Collecting environment : including indoor and outdoor scenes (such as supermarket, mall and residential area, etc.)

Data diversity : different ages, different time periods, different cameras, different human body orientations and postures, different ages collecting environment

Device : surveillance cameras, the image resolution is not less than 1,9201,080

Data format : the image data format is .jpg, the annotation file format is .json

Annotation content : human body rectangular bounding boxes, 15 human body attributes

Quality Requirements : A rectangular bounding box of human body is qualified when the deviation is not more than 3 pixels, and the qualified rate of the bounding boxes shall not be lower than 97%;Annotation accuracy of attributes is over 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data.These ready-to-go Identity Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade