This dataset comprises 80 million vector images. The resources are diverse in type, 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.

VectorEdits: A Dataset and Benchmark for Instruction-Based Editing of Vector Graphics

Paper (Soon) We introduce a large-scale dataset for instruction-guided vector image editing, consisting of over 270,000 pairs of SVG images paired with natural language edit instructions. Our dataset enables training and evaluation of models that modify vector graphics based on textual commands. We describe the data collection process, including image pairing via CLIP similarity and instruction… See the full description on the dataset page: https://huggingface.co/datasets/authoranonymous321/VectorEdits.

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 *** million U.S. dollars.

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

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

Global Vector Graphics Software comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024 - 2032. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

We created a novel database of mosquito images by sampling live mosquitoes from established colonies maintained by the Malaria Research and Reference Reagent Resource (MR4)/ Biodefense and Emerging Infections (BEI) Resources at the Centers for Disease Control and Prevention (CDC) in Atlanta, GA. Adults of both sexes were imaged from 15 species of mosquitoes from there genera, 13 Anopheles, 2 Culex and 1 Aedes. There are a total of 1,709 images. We included an additional strain of An. gambiae s.s. resulting in two categories of this species: G3 and KISUMU1. Finally, for An. stephensi we captured images of mosquitoes using the two methods of storing mosquitoes, freezing versus dried samples. Images are folders labeled by genus, species, strain, sex and storage method.

Text-Based Reasoning About Vector Graphics

🌐 Homepage • 📃 Paper • 🤗 Data (PVD-160k) • 🤗 Model (PVD-160k-Mistral-7b) • 💻 Code

We observe that current large multimodal models (LMMs) still struggle with seemingly straightforward reasoning tasks that require precise perception of low-level visual details, such as identifying spatial relations or solving simple mazes. In particular, this failure mode persists in question-answering tasks about vector graphics—images composed purely of… See the full description on the dataset page: https://huggingface.co/datasets/mikewang/PVD-160K.

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

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

The scientific illustration software market is experiencing robust growth, driven by the increasing need for high-quality visuals in scientific publications, presentations, and educational materials. The market's expansion is fueled by several key factors: the rising adoption of digital tools within research institutions and universities, the growing complexity of scientific data requiring sophisticated visualization techniques, and the increasing demand for visually engaging content to disseminate research findings effectively to broader audiences. While precise market sizing data is unavailable, a reasonable estimation based on comparable software markets and the reported CAGR would suggest a 2025 market value around $300 million, projecting to over $500 million by 2033. This growth trajectory is likely to continue, driven by the ongoing integration of AI-powered features into scientific illustration software, which streamlines workflows and enhances the creation of complex diagrams and illustrations. Furthermore, the increasing affordability and accessibility of powerful software coupled with cloud-based solutions will further democratize access and fuel wider adoption across diverse scientific disciplines. Despite the positive outlook, certain challenges might hinder the market's growth. High initial costs for premium software packages could pose a barrier for individual researchers and smaller institutions. Moreover, the need for specialized training and a certain level of technical proficiency can limit adoption among users who lack prior experience with vector graphics or specialized scientific illustration tools. However, the growing availability of free or open-source alternatives, along with user-friendly interfaces and comprehensive tutorials, are mitigating these challenges. The market is also witnessing an increasing trend towards collaborative software and the integration of scientific illustration tools into larger research platforms, further enhancing the workflow and fostering greater team synergy. The segmentation of the market across various software categories (e.g., general-purpose vector graphics, specialized tools for chemistry, biology, etc.) reflects the diverse needs of scientific illustration in various fields.

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.

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 global graphics software market is experiencing robust growth, driven by increasing demand from various sectors including design, marketing, and education. While precise market size figures for 2025 are not provided, let's assume, based on industry trends and the presence of major players like Adobe, that the market size in 2025 is approximately $15 billion USD. Considering a projected Compound Annual Growth Rate (CAGR) of, let's say, 8% (a reasonable estimate given the ongoing innovation and adoption of graphics software), the market is poised for significant expansion. This growth is fueled by several key drivers: the rising adoption of cloud-based solutions offering collaborative features and accessibility; increased demand for high-quality visual content across digital platforms; and the growing penetration of sophisticated graphic design tools in smaller businesses and individual creators. The market is segmented based on software type (vector, raster, 3D modeling, etc.), deployment (cloud, on-premise), and end-user (individuals, businesses, educational institutions). Competitive pressures exist among established players like Adobe and Corel, as well as newer, agile companies offering specialized or cost-effective alternatives. Restraints on market growth may include the high cost of professional-grade software, the learning curve associated with complex tools, and the potential for software piracy. The forecast period of 2025-2033 indicates continued strong performance, with the market size likely exceeding $25 billion by 2033, assuming the CAGR of 8% is maintained. Key trends include the integration of Artificial Intelligence (AI) for automated design tasks, increasing focus on user-friendly interfaces, and the growing popularity of subscription-based models. The regional distribution will likely see continued dominance from North America and Europe, but significant growth is expected from emerging markets in Asia-Pacific and Latin America, as digital literacy and internet penetration increase. Overall, the graphics software market presents a compelling investment opportunity due to its consistent growth trajectory and the continuously evolving demands for visual content creation.

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.

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.

This dataset is about: Images of weekly mean direction of wind vector onto a grid 1 deg in latitude and longitude for Mar 25 1996 to Jan 15 2001 from satellite ERS-2.

This vector file corresponds to the polygonization of the binary raster with a smoothing to reduce the "stairway" effect of the vectorized polygons from the pixels

Comparison with existing research in-terms of classification accuracy for MSRC-v2 image dataset.