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

The MNIST.SVG collection was created using Potrace(TM) - a bitmap tracing tool. Tracing means transforming a bitmap into a smooth, scalable image.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F460920%2F7415fd4fa53063d6db28aa0340eb3580%2Foutput%20(40).svg?generation=1700249999366374&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F460920%2Fbc6db1e3475c90ba0ab3f2be0adab726%2Foutput%20(27).svg?generation=1700078704492869&alt=media" alt="">

For your convenience R and Python starters: - https://www.kaggle.com/code/jacekpardyak/r-starter - https://www.kaggle.com/code/jacekpardyak/python-starter

Models trained on the data: - discriminative: https://www.kaggle.com/jacekpardyak/svg-image-classification-with-pointnet - generative: work in progress, any ideas ?

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.

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.

Get the sample copy of Vector Graphics Software Market Report 2025 (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)

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

Dataset

This dataset was created by Глеб Мехряков

Released under MIT

Contents

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

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.

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.

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

We present a challenging change detection problem in vector building footprints, which is critical when detected changes are used to update existing vector databases. We formulate our change detection into an image recognition problem, where deep learning approaches can be used. And we explore a more explainable multi-stage workflow of deep models.1．Python environment: requirements.txt2．Step-by-step instructions on reproducing the results: Instructions_on_reproducing.docx3．Code: Code_Feature-Fusion-CNN.zip4．Data: (1) bounding-box.zip, (2) fixed-window.zip, (3) displacement.zip, (4) discrepancy.zip,(5) window_size_10.zip, (6) window_size_20.zip, (7) window_size_30.zip, (8) window_size_40.zip

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

KOKLU Murat (a), UNLERSEN M. Fahri (b), OZKAN Ilker Ali (a), ASLAN M. Fatih(c), SABANCI Kadir (c) (a) Department of Computer Engineering, Selcuk University, Turkey, Konya, Turkey (b) Department of Electrical and Electronics Engineering, Necmettin Erbakan University, Konya, Turkey (c) Department of Electrical-Electronic Engineering, Karamanoglu Mehmetbey University, Karaman, Turkey DATASET: https://www.muratkoklu.com/datasets/ Citation Request : Koklu, M., Unlersen, M. F., Ozkan, I. A., Aslan, M. F., & Sabanci, K. (2022). A CNN-SVM study based on selected deep features for grapevine leaves classification. Measurement, 188, 110425. Doi:https://doi.org/10.1016/j.measurement.2021.110425 Link: https://doi.org/10.1016/j.measurement.2021.110425 DATASET: https://www.muratkoklu.com/datasets/

Highlights • Classification of five classes of grapevine leaves by MobileNetv2 CNN Model. • Classification of features using SVMs with different kernel functions. • Implementing a feature selection algorithm for high classification percentage. • Classification with highest accuracy using CNN-SVM Cubic model.

Abstract: The main product of grapevines is grapes that are consumed fresh or processed. In addition, grapevine leaves are harvested once a year as a by-product. The species of grapevine leaves are important in terms of price and taste. In this study, deep learning-based classification is conducted by using images of grapevine leaves. For this purpose, images of 500 vine leaves belonging to 5 species were taken with a special self-illuminating system. Later, this number was increased to 2500 with data augmentation methods. The classification was conducted with a state-of-art CNN model fine-tuned MobileNetv2. As the second approach, features were extracted from pre-trained MobileNetv2′s Logits layer and classification was made using various SVM kernels. As the third approach, 1000 features extracted from MobileNetv2′s Logits layer were selected by the Chi-Squares method and reduced to 250. Then, classification was made with various SVM kernels using the selected features. The most successful method was obtained by extracting features from the Logits layer and reducing the feature with the Chi-Squares method. The most successful SVM kernel was Cubic. The classification success of the system has been determined as 97.60%. It was observed that feature selection increased the classification success although the number of features used in classification decreased. Keywords: Deep learning, Transfer learning, SVM, Grapevine leaves, Leaf identification

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 author, BNB id, book, book publisher, and ISBN. The data 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.

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

Dataset

This dataset was created by FredLe09

Released under Apache 2.0

Contents