OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

Image quality assessment (IQA) databases enable researchers to evaluate the performance of IQA algorithms and contribute towards attaining the ultimate goal of objective quality assessment research - matching human perception. Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. Our newly designed and created LIVE In the Wild Image Quality Challenge Database, contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment subjective study. The LIVE In the Wild Image Quality Database has over 350,000 opinion scores on 1,162 images evaluated by over 8100 unique human observers.

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.

An open source Optical Coherence Tomography Image Database containing different retinal OCT images with different pathological conditions. Please use the following citation if you use the database: Peyman Gholami, Priyanka Roy, Mohana Kuppuswamy Parthasarathy, Vasudevan Lakshminarayanan, "OCTID: Optical Coherence Tomography Image Database", arXiv preprint arXiv:1812.07056, (2018). For more information and details about the database see: https://arxiv.org/abs/1812.07056

Dataset

This dataset was created by Yogesh Kantak

Contents

With the advent of the Internet, billions of images are now freely available online and constitute a dense sampling of the visual world. Using a variety of non-parametric methods, we explore this world with the aid of a large dataset of 79,302,017 images collected from the Internet. Motivated by psychophysical results showing the remarkable tolerance of the human visual system to degradations in image resolution, the images in the dataset are stored as 32 x 32 color images. Each image is loosely labeled with one of the 75,062 non-abstract nouns in English, as listed in the Wordnet lexical database. Hence the image database gives a comprehensive coverage of all object categories and scenes. The semantic information from Wordnet can be used in conjunction with nearest-neighbor methods to perform object classification over a range of semantic levels minimizing the effects of labeling noise. For certain classes that are particularly prevalent in the dataset, such as people, we are able to

Data set of almost 2,000 neurosurgical images using a variety of search options.

This Image Gallery is provided as a complimentary source of high-quality digital photographs available from the Agricultural Research Service information staff. Photos, (over 2,000 .jpegs) in the Image Gallery are copyright-free, public domain images unless otherwise indicated. Resources in this dataset:Resource Title: USDA ARS Image Gallery (Web page) . File Name: Web Page, url: https://www.ars.usda.gov/oc/images/image-gallery/ Over 2000 copyright-free images from ARS staff.

The WebVision dataset is designed to facilitate the research on learning visual representation from noisy web data. It is a large scale web images dataset that contains more than 2.4 million of images crawled from the Flickr website and Google Images search.

The same 1,000 concepts as the ILSVRC 2012 dataset are used for querying images, such that a bunch of existing approaches can be directly investigated and compared to the models trained from the ILSVRC 2012 dataset, and also makes it possible to study the dataset bias issue in the large scale scenario. The textual information accompanied with those images (e.g., caption, user tags, or description) are also provided as additional meta information. A validation set contains 50,000 images (50 images per category) is provided to facilitate the algorithmic development.

Dataset includes metadata and URLs of thousands of photos taken at over 90 coastal photo monitoring sites between Guilderton and Kalbarri, in Western Australia. Volunteers take regular photos at these sites, which are then uploaded to an online database. This is an export of that database. Show full description

Image Data Resource (IDR) is an online, public data repository that seeks to store, integrate and serve image datasets from published scientific studies. We have collected and are continuing to receive existing and newly created “reference image" datasets that are valuable resources for a broad community of users, either because they will be frequently accessed and cited or because they can serve as a basis for re-analysis and the development of new computational tools.

Here are a few use cases for this project:

1. Grocery Shopping Assistance: This model can help create an application that identifies and records different types of fruits and vegetables as customers add them to their shopping carts. It could also provide nutritional information such as calories.

2. Dietary Planning and Management: Nutritionists could use this model to help patients identify and track their food intake. The model could also suggest balanced diets based on specific ingredients.

3. Cooking Apps or Online Tutorials: This model could be integrated into cooking apps to help users identify necessary ingredients for a recipe or in an online cooking tutorial to help viewers identify the ingredients being used.

4. Food Sorting in Warehouses or Supermarkets: This model can be used in food warehouses or supermarkets to sort different kinds of fruits, vegetables, and other food items, aiding in inventory management.

5. Farming Assistance: Farmers could use this model to identify and categorize their produce. This could help them understand what crops are ready for harvest and estimate their yield.

Search results for images and image metadata pertaining to the keywords 'same' and 'samisk' in the collections of the Swedish National Heritage Board in the in-house image database Kulturmiljöbild. The results are part of datasets produced by Vendela Grundell Gachoud comprising images, image metadata and interview notes pertaining to collections of the Swedish National Heritage Board presented in the in-house image database Kulturmiljöbild and on the social media site Flickr Commons. The research for this dataset was conducted within the project The Politics of Metadata at the Department of Culture and Aesthetics at Stockholm University, funded by the Swedish Research Council (grant no. 2018-01068). The project leader is Anna Näslund Dahlgren. Results of this research are presented in Digital Approaches to Inclusion and Participation in Cultural Heritage (eds. Giglitto et al, Routledge 2022).

The present study introduces the Extreme Climate Event Database (EXCEED), a picture database intended to induce emotionally salient stimuli reactions in the context of natural hazards associated with global climate change and related extreme events. The creation of the database was motivated by the need to better understand the impact that the increase in natural disasters worldwide has on human emotional reactions. This new database consists of 150 pictures divided into three categories: two negative categories that depict images of floods and droughts, and a neutral category composed of inanimate objects. Affective ratings were obtained using online survey software from 50 healthy Brazilian volunteers who rated the pictures according to valence and arousal, which are two fundamental dimensions used to describe emotional experiences. Valence refers to the appraisal of pleasantness conveyed by a stimulus, and arousal involves internal emotional activation induced by a stimulus. Data from picture rating, sex difference in affective ratings and psychometric properties of the database are presented here. Together, the data validate the use of EXCEED in research related to natural hazards and human reactions.

39,993 Images – OCR Data of Internet Image. The collecting scenes of this dataset include subtitle, advertisement, cellphone screenshot, comic, emoticon, poster, magazine cover, etc. The language distribution is Chinese, English (a few). For annotation, line-level rectangular bounding box annotation and transcription for the texts were adopted for the internet images (column-level quadrilateral bounding box annotation and transcription for the texts were adopted for small amount of data). The dataset can be used for OCR tasks of internet images.

The digital photo scanning service market, valued at $367.5 million in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 5.6% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume of physical photographs held by individuals and businesses creates a significant demand for efficient and high-quality digitization services. Furthermore, the growing preference for digital storage due to its convenience, accessibility, and longevity contributes to market growth. Technological advancements, such as improved scanning technologies offering higher resolutions and faster processing speeds, also play a crucial role. The market is segmented by application (personal and enterprise) and type of service (photo correction, photo storage, and other specialized services). The personal segment currently dominates, driven by consumers' desire to preserve and share cherished memories. However, the enterprise segment is expected to witness significant growth as businesses increasingly digitize their archives for efficient management and preservation. Competitive dynamics are shaped by a blend of established players like Kodak and Staples, alongside specialized service providers such as ScanDigital and Legacybox Backup. Geographic distribution sees North America and Europe currently holding the largest market share, although growth potential in Asia-Pacific is significant due to the region’s burgeoning middle class and rising disposable incomes. The market faces certain restraints, including concerns about data security and privacy, particularly for sensitive personal information contained within photographs. Price sensitivity among consumers, especially for high-volume scanning projects, represents another challenge. However, innovative business models, such as subscription services and tiered pricing structures, are emerging to address this. The ongoing trend of cloud-based storage solutions is expected to further fuel market growth, allowing users convenient access and backup of their digitized photos. Future growth will likely hinge on the development of even more efficient and automated scanning technologies, along with the continued focus on user-friendly interfaces and transparent pricing models to attract a broader range of customers. The market’s sustained growth trajectory points towards a significant expansion in the coming years, driven by evolving consumer preferences and technological advancements in digital preservation.

Dataset

This dataset was created by imagesets8

Contents

Images by Björn Allard in the collections of the Swedish National Heritage Board, presented in the in-house image database Kulturmiljöbild and on the social media site Flickr Commons. The images are part of datasets produced by Vendela Grundell Gachoud comprising images, image metadata and interview notes pertaining to collections of the Swedish National Heritage Board presented in the in-house image database Kulturmiljöbild and on the social media site Flickr Commons. The research for this dataset was conducted within the project The Politics of Metadata at the Department of Culture and Aesthetics at Stockholm University, funded by the Swedish Research Council (grant no. 2018-01068). The project leader is Anna Näslund Dahlgren. Results of this research are presented in Digital Approaches to Inclusion and Participation in Cultural Heritage (eds. Giglitto et al, Routledge 2022).

Training dataset

The EyeOnWater app is designed to assess the ocean's water quality using images captured by regular citizens. In order to have an extra helping hand in determining whether an image meets the criteria for inclusion in the app, the YOLOv8 model for image classification is employed. With the help of this model all uploaded pictures are assessed. If the model deems a water image unsuitable, it is excluded from the app's online database. In order to train this model a training dataset containing a large pool of different images is required. The dataset contains a total of 13,766 images, categorized into three distinct classes: “water_good,” “water_bad,” and “other.” The “water_good” class includes images that meet the requirements of EyeOnWater. The “water_bad” class comprises images of water that do not fulfill these requirements. Finally, the “other” class consists of miscellaneous images that users submitted, which do not depict water. This categorization enables precise filtering and analysis of images relevant to water quality assessment.

Poster at the "The Society for Mathematical Biology Annual Meeting and Conference", Knoxville,TN (USA), July 25-28, 2012