This image database contains 200 million high-quality images that have undergone professional review. The resources are diverse in type, featuring high resolution and clarity, excellent color accuracy, and rich detail. All materials have been legally obtained through authorized channels, with clear indications of copyright ownership and usage authorization scope. The entire collection provides commercial-grade usage rights and has been granted permission for scientific research use, ensuring clear and traceable intellectual property attribution. The vast and high-quality image resources offer robust support for a wide range of applications, including research in the field of computer vision, training of image recognition algorithms, and sourcing materials for creative design, thereby facilitating efficient progress in related areas.

High Quality Image-Text Pairs (HQITP) dataset contains 134M high-quality image-caption pairs.

CQ100 is a diverse and high-quality dataset of color images that can be used to develop, test, and compare color quantization algorithms. The dataset can also be used in other color image processing tasks, including filtering and segmentation.

If you find CQ100 useful, please cite the following publication: M. E. Celebi and M. L. Perez-Delgado, “CQ100: A High-Quality Image Dataset for Color Quantization Research,” Journal of Electronic Imaging, vol. 32, no. 3, 033019, 2023.

You may download the above publication free of charge from: https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-32/issue-3/033019/cq100--a-high-quality-image-dataset-for-color-quantization/10.1117/1.JEI.32.3.033019.full?SSO=1

A project which contains data and analysis pipelines for a set of 53 subjects in a cross-sectional Parkinsons disease (PD) study. The dataset contains diffusion-weighted images (DWI) of 27 PD patients and 26 age, sex, and education-matched control subjects. The DWIs were acquired with 120 unique gradient directions, b=1000 and b=2500 s/mm2, and isotropic 2.4 mm3 voxels. The acquisition used a twice-refocused spin echo sequence in order to avoid distortions induced by eddy currents.

PartImageNet is a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24000 images. PartImageNet offers part-level annotations on a general set of classes with non-rigid, articulated objects, while having an order of magnitude larger size compared to existing datasets. It can be utilized in multiple vision tasks including but not limited to: Part Discovery, Semantic Segmentation, Few-shot Learning.

The dataset includes monthly data of eight water quality parameters for lakes and reservoirs in China from 2000 to 2023. The data were simulated using random forest models, taking into account the impacts of climate, soil properties, and anthropogenic activities. These water quality parameters are pH, dissolved oxygen (DO; mg/L), total nitrogen (TN; mg/L), total phosphorus (TP; mg/L), permanganate index (CODMn; mg/L), turbidity (Tur; JTU), electrical conductivity (EC; S/m) and dissolved organic carbon (DOC; mg/L). The data is stored in CSV format, sorted by lake and reservoir, and each CSV file contains monthly water quality data for the lake or reservoir and corresponding coordinates.

The Mobile Icon | Mobile Screenshot Dataset is a meticulously curated collection of 9,000+ high-quality mobile screenshots, categorized across 13 diverse application types. This dataset is designed to support AI/ML researchers, UI/UX analysts, and developers in advancing mobile interface understanding, image classification, and content recognition.

Each image has been manually reviewed and verified by computer vision professionals at DataCluster Labs, ensuring high-quality and reliable data for research and development purposes.

Categories Included

• Technical Applications
• Wallpapers
• News & Magazines
• Business & Finance
• Agriculture
• Entertainment and many more.

Potential Applications:

• AI & ML model training (image classification, UI/UX analysis, OCR).
• Mobile app usability and accessibility research.
• Content recognition and recommendation systems.

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. Contact us at sales@datacluster.ai Visit www.datacluster.ai to know more.

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!

The President believes we need to equip every child with the skills and education they need to be on a clear path to a good job and the middle class. To ensure these opportunities are available to all, President Obama has put forward a comprehensive early learning proposal to build a strong foundation for success in the first five years of life. These investments will help close America's school readiness gap and ensure that America's children enter kindergarten ready to succeed.

dataset link : https://www.kaggle.com/datasets/osamahosamabdellatif/high-quality-invoice-images-for-ocr

Overview High-Quality Invoice Images for OCR is a curated dataset containing professionally scanned and digitally captured invoice documents. It is designed for training, fine-tuning, and evaluating OCR models, machine learning pipelines, and data extraction systems.

This dataset focuses on clean, structured invoices to simulate real-world scenarios in financial document automation.

What's Inside 📄 Variety of invoice templates from multiple industries (e.g., retail, manufacturing, services)

🖋️ Different currencies, tax formats, and layouts

📸 High-resolution scanned and photographed invoices

🏷️ Optional field annotations (e.g., invoice number, date, total amount, vendor name) for supervised training

Key Applications Training and fine-tuning OCR and Document AI models

Machine learning for structured and semi-structured data extraction

Intelligent Document Processing (IDP) and Robotic Process Automation (RPA)

Benchmarking table detection, key-value extraction, and layout analysis models

Why Use This Dataset? ✅ High-quality images optimized for OCR and data extraction tasks

✅ Real-world invoice variations to improve model robustness

✅ Ideal for machine learning workflows in finance, ERP, and accounting systems

✅ Supports rapid prototyping for invoice understanding models

Ideal For Researchers working on OCR and document understanding

Developers building invoice processing systems

Machine learning engineers fine-tuning models for data extraction

Startups and enterprises automating financial workflows

The data labeling market is experiencing robust growth, projected to reach $3.84 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.13% from 2025 to 2033. This expansion is fueled by the increasing demand for high-quality training data across various sectors, including healthcare, automotive, and finance, which heavily rely on machine learning and artificial intelligence (AI). The surge in AI adoption, particularly in areas like autonomous vehicles, medical image analysis, and fraud detection, necessitates vast quantities of accurately labeled data. The market is segmented by sourcing type (in-house vs. outsourced), data type (text, image, audio), labeling method (manual, automatic, semi-supervised), and end-user industry. Outsourcing is expected to dominate the sourcing segment due to cost-effectiveness and access to specialized expertise. Similarly, image data labeling is likely to hold a significant share, given the visual nature of many AI applications. The shift towards automation and semi-supervised techniques aims to improve efficiency and reduce labeling costs, though manual labeling will remain crucial for tasks requiring high accuracy and nuanced understanding. Geographical distribution shows strong potential across North America and Europe, with Asia-Pacific emerging as a key growth region driven by increasing technological advancements and digital transformation. Competition in the data labeling market is intense, with a mix of established players like Amazon Mechanical Turk and Appen, alongside emerging specialized companies. The market's future trajectory will likely be shaped by advancements in automation technologies, the development of more efficient labeling techniques, and the increasing need for specialized data labeling services catering to niche applications. Companies are focusing on improving the accuracy and speed of data labeling through innovations in AI-powered tools and techniques. Furthermore, the rise of synthetic data generation offers a promising avenue for supplementing real-world data, potentially addressing data scarcity challenges and reducing labeling costs in certain applications. This will, however, require careful attention to ensure that the synthetic data generated is representative of real-world data to maintain model accuracy. This comprehensive report provides an in-depth analysis of the global data labeling market, offering invaluable insights for businesses, investors, and researchers. The study period covers 2019-2033, with 2025 as the base and estimated year, and a forecast period of 2025-2033. We delve into market size, segmentation, growth drivers, challenges, and emerging trends, examining the impact of technological advancements and regulatory changes on this rapidly evolving sector. The market is projected to reach multi-billion dollar valuations by 2033, fueled by the increasing demand for high-quality data to train sophisticated machine learning models. Recent developments include: September 2024: The National Geospatial-Intelligence Agency (NGA) is poised to invest heavily in artificial intelligence, earmarking up to USD 700 million for data labeling services over the next five years. This initiative aims to enhance NGA's machine-learning capabilities, particularly in analyzing satellite imagery and other geospatial data. The agency has opted for a multi-vendor indefinite-delivery/indefinite-quantity (IDIQ) contract, emphasizing the importance of annotating raw data be it images or videos—to render it understandable for machine learning models. For instance, when dealing with satellite imagery, the focus could be on labeling distinct entities such as buildings, roads, or patches of vegetation.October 2023: Refuel.ai unveiled a new platform, Refuel Cloud, and a specialized large language model (LLM) for data labeling. Refuel Cloud harnesses advanced LLMs, including its proprietary model, to automate data cleaning, labeling, and enrichment at scale, catering to diverse industry use cases. Recognizing that clean data underpins modern AI and data-centric software, Refuel Cloud addresses the historical challenge of human labor bottlenecks in data production. With Refuel Cloud, enterprises can swiftly generate the expansive, precise datasets they require in mere minutes, a task that traditionally spanned weeks.. Key drivers for this market are: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Potential restraints include: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Notable trends are: Healthcare is Expected to Witness Remarkable Growth.

[Paper] https://arxiv.org/abs/2506.19848 [GitHub] https://github.com/Cooperx521/ScaleCap

  ScaleCap450k-Hyper detailed and high quality image caption





  Dataset details

This dataset contains 450k image-caption pairs, where the captions are annotated using the ScaleCap pipeline. For more details, please refer to the paper. In collecting images for our dataset, we primarily focus on two aspects: diversity and richness of image content. Given that the ShareGPT4V-100k already… See the full description on the dataset page: https://huggingface.co/datasets/long-xing1/ScaleCap-450k.

GlobalHighPM_2.5 is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

This dataset contains input data, analysis codes, and generated dataset used for the following article. If you use the GlobalHighPM_2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):

• Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM_2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3

Input Data

Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.

Code

Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.

Generated Dataset

Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM_2.5 dataset over land from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R²) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m^-3 on the daily, monthly, and annual bases, respectively.

Due to data volume limitations,

all (including daily) data for the year 2022 is accessible at: GlobalHighPM2.5 (2022)

all (including daily) data for the year 2021 is accessible at: GlobalHighPM2.5 (2021)

all (including daily) data for the year 2020 is accessible at: GlobalHighPM2.5 (2020)

all (including daily) data for the year 2019 is accessible at: GlobalHighPM2.5 (2019)

all (including daily) data for the year 2018 is accessible at: GlobalHighPM2.5 (2018)

all (including daily) data for the year 2017 is accessible at: GlobalHighPM2.5 (2017)

continuously updated...

More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

United States - 61.5-Year High Quality Market (HQM) Corporate Bond Spot Rate was 5.95% in March of 2025, according to the United States Federal Reserve. Historically, United States - 61.5-Year High Quality Market (HQM) Corporate Bond Spot Rate reached a record high of 12.50 in June of 1984 and a record low of 3.09 in December of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 61.5-Year High Quality Market (HQM) Corporate Bond Spot Rate - last updated from the United States Federal Reserve on May of 2025.

High-Quality Wetland points displayed in the DNR Watershed Restoration and Protection Viewer. These are unique wetlands and those wetlands with least disturbed or reference conditions. Points represent a generalized area, for legal and privacy reasons. All points are in HUCs that fall mostly within Wisconsin.

United States - 77-Year High Quality Market (HQM) Corporate Bond Spot Rate was 6.38% in May of 2025, according to the United States Federal Reserve. Historically, United States - 77-Year High Quality Market (HQM) Corporate Bond Spot Rate reached a record high of 12.47 in June of 1984 and a record low of 3.09 in November of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 77-Year High Quality Market (HQM) Corporate Bond Spot Rate - last updated from the United States Federal Reserve on July of 2025.

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

United States - 53.5-Year High Quality Market (HQM) Corporate Bond Spot Rate was 6.32% in May of 2025, according to the United States Federal Reserve. Historically, United States - 53.5-Year High Quality Market (HQM) Corporate Bond Spot Rate reached a record high of 12.53 in June of 1984 and a record low of 3.07 in December of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 53.5-Year High Quality Market (HQM) Corporate Bond Spot Rate - last updated from the United States Federal Reserve on June of 2025.

United States - 30-Year High Quality Market (HQM) Corporate Bond Par Yield was 5.87% in May of 2025, according to the United States Federal Reserve. Historically, United States - 30-Year High Quality Market (HQM) Corporate Bond Par Yield reached a record high of 13.28 in June of 1984 and a record low of 2.79 in July of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 30-Year High Quality Market (HQM) Corporate Bond Par Yield - last updated from the United States Federal Reserve on June of 2025.

Aesthetic-4K Dataset

We introduce Aesthetic-4K, a high-quality dataset for ultra-high-resolution image generation, featuring carefully selected images and captions generated by GPT-4o. Additionally, we have meticulously filtered out low-quality images through manual inspection, excluding those with motion blur, focus issues, or mismatched text prompts. For more details, please refer to our paper:

Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models (CVPR… See the full description on the dataset page: https://huggingface.co/datasets/zhang0jhon/Aesthetic-4K.