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

This dataset is about books and is filtered where the book is SVG for designers : using scalable vector graphics in next-generation Web sites. It has 7 columns such as book, author, ISBN, BNB id, and language. The data is ordered by publication date.

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.

Dataset

This dataset was created by Глеб Мехряков

Released under MIT

Contents

Project to house any research materials related to the development of the Dynamic Vector Graphics system.

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.

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.

Get the sample copy of Vector Graphics Software Market Report 2024 (Global Edition) which includes data such as Market Size, Share, Growth, CAGR, Forecast, Revenue, list of Vector Graphics Software Companies (Adobe Illustrator, Sketch, CorelDRAW, Affinity, Inkscape, Snappa, Xara, DesignEvo, Artboard, Vecteezy Editor, Gravit Designer, Vector Magic), Market Segmented by Type (Cloud Based, Web Based), by Application (Large Enterprises, SMEs)

Dataset

This dataset was created by Pipe Runner (Old Account)

Released under Database: Open Database, Contents: Database Contents

Contents

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

StyleGAN3 Annotated Images

This dataset consists of a pandas table and attached images.zip file with these entries:

seed (numpy seed used to generate random vectors) path (path to the generated image obtained after unzipping images.zip) vector (generated numpy "random" vector used to create StyleGAN3 images) text (caption of each image, generated using BLIP model: Salesforce/blip-image-captioning-base)

  Usage

In order not to load the images into the memory, we… See the full description on the dataset page: https://huggingface.co/datasets/balgot/stylegan3-annotated.

Mutilation and neutral images were misclassified at similar rates (~30%), indicating that the approach is not biased to either case. (B) Intact disgust images were distinguished from neutral images with an accuracy of 70.36%.

Context

This data set includes metadata and vectors representing images in the Drinking Waste Classification data set. The metadata and image vectors are retained as a separate data set in order to save on calculation time during a workshop demo. The metadata and image vectors are generated by the Drinking Waste Data Exploration and CV Design notebook.

The image vectors are extracted by chopping the last few layers off a pretrained neural network (resnet18).

Acknowledgements

The processed data in this data set is based on the data in the Drinking Waste Classification data set.

Workshop Exercises

Suggested exercises for workshop participants are included in the Drinking Waste Data Exploration and CV Design notebook..

This is a novel dataset of images of mosquitoes belonging to three harmful species : Aedes Aegypti , Anopheles stephensi and Culex quinquefasciatus. The dataset is valuable for training machine and deep learning models for automatic species classification based on the morphological features. Automated genera / species identification of vectors is a valuable contribution to implement targeted vector control strategies.

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

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.

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.

K denotes the size of visual vocabulary.