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.

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

NOTE: Currently only test set has generated labels, other sets will have them soon Find the details in our paper: VectorEdits: A Dataset and Benchmark for Instruction-Based Editing of Vector Graphics 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… See the full description on the dataset page: https://huggingface.co/datasets/mikronai/VectorEdits.

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

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

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.

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.

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

Market Research Intellect's Vector Graphics Software Market Report highlights a valuation of USD 3.1 billion in 2024 and anticipates growth to USD 5.2 billion by 2033, with a CAGR of 7.4% from 2026–2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

Image dataset from flickr website. Contains a total of 8091 images with corresponding captions. Mainly used for image caption generation.

Tasks supported:

1. Image Feature Extraction
2. Image caption generation

Yearly citation counts for the publication titled "Feature-specific vector quantization of images".

The 14 datasets used to build SVM and LS-SVM classification models of FLS.

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.

Comprehensive collection of 12 objects & tools SVG graphics and vector illustrations. Everyday objects and tools SVG icons including household items, industrial equipment, office supplies, and technology gadgets

ModelA Hugging Face Unconditional image generation Diffusion Model was used for training. [1] Unconditional image generation models are not conditioned on text or images during training. They only generate images that resemble the training data distribution. The model usually starts with a seed that generates a random noise vector. The model will then use this vector to create an output image similar to the images used to train the model. The training script initializes a UNet2DModel and uses it to train the model. [2] The training loop adds noise to the images, predicts the noise residual, calculates the loss, saves checkpoints at specified steps, and saves the generated models.Training DatasetThe RANZCR CLiP dataset was used to train the model. [3] This dataset has been created by The Royal Australian and New Zealand College of Radiologists (RANZCR) which is a not-for-profit professional organisation for clinical radiologists and radiation oncologists. The dataset has been labelled with a set of definitions to ensure consistency with labelling. The normal category includes lines that were appropriately positioned and did not require repositioning. The borderline category includes lines that would ideally require some repositioning but would in most cases still function adequately in their current position. The abnormal category included lines that required immediate repositioning. 30000 images were used during training. All training images were 512x512 in size. Computational Information Training has been conducted using RTX 6000 cards with 24GB of graphics memory. A checkpoint was created after each epoch was saved with 220 checkpoints being generated so far. Each checkpoint takes up 1GB space in memory. Generating each epoch takes around 6 hours. Machine learning libraries such as TensorFlow, PyTorch, or scikit-learn are used to run the training, along with additional libraries for data preprocessing, visualization, or deployment.Referenceshttps://huggingface.co/docs/diffusers/en/training/unconditional_training#unconditional-image-generationhttps://github.com/huggingface/diffusers/blob/096f84b05f9514fae9f185cbec0a4d38fbad9919/examples/unconditional_image_generation/train_unconditional.py#L356https://www.kaggle.com/competitions/ranzcr-clip-catheter-line-classification/data

This dataset consists of the vector version of the Land Cover Map 2015 (LCM2015) for Great Britain. The vector data set is the core LCM data set from which the full range of other LCM2015 products is derived. It provides a number of attributes including land cover at the target class level (given as an integer value and also as text), the number of pixels within the polygon classified as each land cover type and a probability value provided by the classification algorithm (for full details see the LCM2015 Dataset Documentation). The 21 target classes are based on the Joint Nature Conservation Committee (JNCC) Broad Habitats, which encompass the entire range of UK habitats. LCM2015 is a land cover map of the UK which was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. LCM2015 consists of a range of raster and vector products and users should familiarise themselves with the full range (see related records, the CEH web site and the LCM2015 Dataset documentation) to select the product most suited to their needs. LCM2015 was produced at the Centre for Ecology & Hydrology by classifying satellite images from 2014 and 2015 into 21 Broad Habitat-based classes. It is one of a series of land cover maps, produced by UKCEH since 1990. They include versions in 1990, 2000, 2007, 2015, 2017, 2018 and 2019.

This child item contains the Mathworks Matlab mat-file outputs from the scripts described in the Ancillary Scripts child item. Each file contains the results for a particular field site. See the FGDC metadata Process Steps section for more information about opening these files. The mat-files included here have a standard set of output variables and include a variable named "zzVariableDescriptions" in each mat-file which describes the contents of the file. The following variables and descriptions are included in each mat-file (extracted from the "zzVariableDescriptions" variable):

• calibration_distance: The distance between calibration points in meters.
• calibration_points: Pixel coordinates of the calibration points. Format (array): [X1,Y1; X2; Y2]
• calibration_time: Time increment between image frames in milliseconds.
• caluv: Correction factor used to convert pixel/second into meters/second.
• calxy: Pixel ground resolution in meters/pixel.
• directory: Path to folder containing images used in PIV analysis.
• filenames: Cell array of strings containing image frame filenames. Format (cellarray): 1m (m: number of frames)
• imagesLocation: Path to folder containing images used in PIV analysis.
• i: Dimensions of PIV results. Format: inumber of rows along y-axis
• j: Dimensions of PIV results. Format: jnumber of columns along x-axis
• k: Dimensions of PIV results. Format: knumber ofimages or frames in time (numbmer of images processed)
• p: PIVLab image pre-processing settings. See PIVLab documentation for information.
• pixel_resolution: Pixel ground resolution in meters. Assumes square pixels.
• r: PIVLab post-processing settings. See PIVLab documentation for information.
• resultsFileFullPath: Path to folder containing PIV results in mat-file format.
• s: PIVLab standard processing settings. See PIVLab documentation for information.
• typevector: Array (mnp) containing raw vector result type of frame (mn) for each frame (p). Format: type 1-valid PIV vector; type 0-masked vector; type 2-invalid PIV vector
• typevector_filt: Array (mnp) containing filtered vector result type of frame (mn) for each frame (p). Format: type 1-valid PIV vector; type 0-masked vector; type 2-invalid PIV vector
• u_mean: Array (mn) containing the temporal average u component of velocity in meters/second. Values are averaged for every vector for each frame (along p dimension).
• u_stack: Array (mnp) containing filtered u component velocities for each vector (mn) for each frame (p).
• v_mean: Array (mn) containing the temporal average v component of velocity in meters/second. Values are averaged for every vector for each frame (along p dimension).
• v_stack: Array (mnp) containing filtered v component velocities for each vector (mn) for each frame (p).
• x_ground: Array (mn) containing the x (horizontal) ground coordinate in meters for each PIV result vector. Origin of coordinates is the lower left corner.
• x_pixel: Array (mn) containing the x (horizontal) pixel coordinate for each PIV result vector.
• y_ground: Array (mn) containing the y (horizontal) ground coordinate in meters for each PIV result vector. Origin of coordinates is the lower left corner.
• y_pixel: Array (mn) containing the y (horizontal) pixel coordinate for each PIV result vector.
• zzVariableDescriptions: A structured array containing elements named after each variable in this dataset.

• ACR: Androscoggin River, Auburn, Maine, USA
• AFR: Agua Fria River, near Rock Springs, Arizona, USA
• CCC: Coachella Canal above All-American Canal Diversion, California, USA
• CMC: Cochiti East Side Main Channel, near Cochiti, New Mexico, USA
• GLR: Gila River near Dome, Arizona, USA
• RMC: Reservation Main Canal near Yuma, Arizona, USA
• SMC: Sile Main Canal (at head) at Cochiti, New Mexico, USA
• WMD: Wellton-Mohawk Main Outlet Drain near Yuma, Arizona, USA

The stock photography market, encompassing giants like Getty Images, Shutterstock, and Adobe Stock, alongside emerging players like Pexels and Unsplash, is a dynamic and rapidly evolving sector. While precise market sizing requires proprietary data, a reasonable estimation based on publicly available information and industry reports suggests a 2025 market value of approximately $5 billion, growing at a Compound Annual Growth Rate (CAGR) of around 8% through 2033. This growth is fueled by several key drivers: the increasing demand for high-quality visuals across various industries (marketing, publishing, web design), the proliferation of digital content creation, and the continued shift towards subscription-based models offering cost-effective access to extensive image libraries. Furthermore, the rise of user-generated content platforms and the increasing sophistication of AI-powered image generation tools are shaping market trends, creating both opportunities and challenges for established players. However, the market also faces restraints. Increasing competition from free and low-cost alternatives, concerns around copyright infringement, and the need for continuous innovation to stay ahead of technological advancements pose significant hurdles. Segmentation within the market is evident, with distinctions based on image type (e.g., photos, vectors, illustrations), licensing models (royalty-free, rights-managed), target audience (e.g., professionals, amateurs), and geographical distribution. The North American and European markets currently hold significant shares, but emerging markets in Asia and Latin America present promising growth opportunities. The future success of companies in this sector will depend on their ability to adapt to evolving user preferences, embrace new technologies, and effectively manage copyright issues to ensure sustainable growth.

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.