Dataset for upcomming paper "Evaluating Consistency of Image Generation Models with Vector Similarity"

The statistic shows the computer graphics software market value in the vector graphics segment from 2009 to 2013. In 2010, there was a market value of 200 million U.S. dollars.

Vector graphics and illustration : a master class in digital image-making is a book. It was written by Jack Harris and published by RotoVision in 2008.

Global Vector Graphics Software market size 2025 was XX Million. Vector Graphics Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Dataset

This dataset was created by Глеб Мехряков

Released under MIT

Contents

Classification accuracy and size of feature vector comparison while using RSSCN image dataset.

Explore the historical Whois records related to free-vector-art.com (Domain). Get insights into ownership history and changes over time.

This dataset provides browse images of the NASA Scatterometer (NSCAT) Level 3 daily gridded ocean wind vectors, which are provided at 0.5 degree spatial resolution for ascending and descending passes; wind vectors are averaged at points where adjacent passes overlap. This is the most up-to-date version, which designates the final phase of calibration, validation and science data processing, which was completed in November of 1998, on behalf of the JPL NSCAT Project; wind vectors are processed using the NSCAT-2 geophysical model function. Information and access to the Level 3 source data used to generate these browse images may be accessed at: http://podaac.jpl.nasa.gov/dataset/NSCAT%20LEVEL%203.

A dataset of 3000 images collected on a public roadway for front seat vehicle occupancy detection.

This dataset is about book subjects and is filtered where the books includes Illustrator foundations : the art of vector graphics, design, and illustration in Illustrator, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

VLDBench: Vision Language Models Disinformation Detection Benchmark

  Dataset Summary

VLDBench is a multimodal dataset for news disinformation detection, containing text, images, and metadata extracted from various news sources. The dataset includes headline, article text, image descriptions, and images stored as byte arrays, ensuring compatibility with Hugging Face's dataset viewer.

  Features

Text: News articles and headlines Images: Associated images stored in… See the full description on the dataset page: https://huggingface.co/datasets/vector-institute/VLDBench.

Vector graphic documents present multiple visual elements, such as images, shapes, and texts. Choosing appropriate colors for multiple visual elements is a difficult but crucial task for both amateurs and professional designers.

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

Files are labeled using the filenames. The file names are shown as: genus_species_sex_strain_imagenumber.jpg

Collection of Sentinel-2 satellite scenes employed in the workshop "Introduction to Geospatial Raster and Vector Data with Python". Metadata is provided following the SpatioTemporal Asset Catalog (STAC) specification.

This dataset is about books and is filtered where the book is SVG for designers : using scalable vector graphics in next-generation Web sites, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

Background

The Anki Vector robot (assets currently owned by Digital Dream Labs LLC which bought Anki assets in 2019) was first introduced in 2018. In my opinion, the Vector robot has been the cheapest fully functional autonomous robot that has ever been built. The Vector robot can be trained to recognize people; however Vector does not have the ability to recognize another Vector. This dataset has been designed to allow one to train a model which can detect a Vector robot in the camera feed of another Vector robot.

Details Pictures were taken with Vector’s camera with another Vector facing it and had this other Vector could move freely. This allowed pictures to be captured from different angles. These pictures were then labeled by marking the rectangular regions around Vector in all the images with the help of a free Linux utility called labelImg. Different backgrounds and lighting conditions were used to take the pictures. There is also a collection of pictures without Vector.

Example An example use case is available in my Google Colab notebook, a version of which can be found in my Git.

More More details are available in this article on my blog. If you are new to Computer Vision/ Deep Learning/ AI, you can consider my course on 'Learn AI with a Robot' which attempts to teach AI based on the AI4K12.org curriculum. There are more details available in this post.

The still images market is experiencing robust growth, driven by the increasing demand for high-quality visuals across diverse sectors. The surge in digital content creation, fueled by social media, e-commerce, and marketing initiatives, is a primary catalyst. Furthermore, the proliferation of smartphones with advanced camera capabilities has democratized image capture, leading to a vast influx of readily available still images. Advancements in AI-powered image editing and search technologies further enhance market expansion by facilitating efficient content management and discovery. While copyright and licensing complexities present challenges, the market is adapting with innovative solutions like subscription-based platforms and royalty-free image offerings. The market is segmented by image type (e.g., photographs, illustrations, vector graphics) and application (e.g., advertising, publishing, web design). Key players are continuously innovating to offer diverse portfolios, advanced search functionalities, and efficient licensing options to cater to evolving user needs. We estimate the market size in 2025 to be approximately $15 billion, with a compound annual growth rate (CAGR) of around 8% projected through 2033. This growth is anticipated across all regions, with North America and Asia Pacific emerging as major contributors due to their significant digital economies and robust technological infrastructure. The competitive landscape is characterized by both established giants and emerging players. Large companies like Adobe, Getty Images, and Shutterstock leverage their extensive image libraries and established brand recognition to maintain market leadership. Meanwhile, smaller companies are focusing on niche markets or specialized services, such as AI-powered image generation or specific content categories. Future market dynamics will likely involve further consolidation, increased competition from AI-generated imagery, and a continued focus on providing seamless integration with content management systems and design software. The market’s expansion will heavily depend on maintaining transparent licensing practices and addressing copyright concerns effectively to ensure sustainable growth. Regional variations in digital adoption rates and economic growth will also influence the distribution of market share in the coming years.

Classification accuracy and size of feature vector comparison while using SIRI-WHU dataset.