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.

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.

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.

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

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

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

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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.

The data utilized in our experiments was obtained from the Barcode of Life Data System (BOLD), which is a cloud-based data storage and analysis platform developed at the Centre for Biodiversity Genomics in Canada. The insect_dataset.mat consists of 32424 image samples of insect species from four Insecta orders, Diptera, Coleoptera, Lepidoptera and Hymenoptera, each associated with a DNA barcode sequence of that sample. The unseen_insect_dataset.mat consists of 40050 image samples of insects from the same order, but all don't have an indicated species in the BOLD System, so they are real unclassified species (at the time of the dataset creation), having only the genus available, each one is also associated with a DNA barcode sequence of that sample.

Description of the .mat:

# Insect Dataset

* all_images: vector containing the 32424 64x64x3 images (RGB) pre normalized of the insects
* all_dnas: vector containing the 32424 DNA barcodes in one-hot encoding 658x5
* all_labels: vector containing the species label for the corresponding DNA and image
* all_boldids: vector of strings containing the id from boldsystemsv3 (https://v3.boldsystems.org/) they can be used to download from boldsystems the original DNA barcodes and the full size images and other data related to the sample
* train_loc: indices of the training samples in all_dnas, all_images, all_labels, all_boldids
* val_seen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species
* val_unseen_loc: indices of the validation samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species
* test_seen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain described(seen) species
* test_unseen_loc: indices of the test samples in all_dnas, all_images, all_labels, all_boldids that contain undescribed(unseen) species
* species2genus: the vector contains at index i the genus label of species with label i (e.g. species i has genus species2genus[i])
* described_species_labels_train: vector containing the labels of species that appear in the training set
* described_species_labels_trainval: vector containing the labels of species that appear in the training set and/or the validation set
* all_dna_features_cnn_original: vector of features extractedfrom DNA nucleotides with the method of Badirli, S., Picard, C. J., Mohler, G.,Richert, F., Akata, Z., & Dundar, M. (2023). Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution, 14,
1515-1530. https://doi.org/10.1111/2041-210X.14104
* all_image_features_resnet: vector of features extracted from the insect images with the method of the same paper as the all_dna_features_cnn_original with a pretrained resnet101
* all_dna_features_cnn_new: vector of features extracted from DNA nucleotides with our CNN
* all_image_features_gan: vector of features extracted from the insect images with out method using a ReACGAN

Description of the .mat:

# Unseen Insect Dataset

* all_images: vector containing the 40050 64x64x3 images (RGB) pre normalized of the insects
* all_dnas: vector containing the 40050 DNA barcodes in one-hot encoding 658x5

* all_string_dnas: vector containing the 40050 DNA barcodes in string format
* all_genus_labels: vector containing the species label for the corresponding DNA and image
* all_boldids: vector of strings containing the id from boldsystemsv3 (https://v3.boldsystems.org/) they can be used to download from boldsystems the original DNA barcodes and the full size images and other data related to the sample
* all_dna_features_cnn_original: vector of features extractedfrom DNA nucleotides with the method of Badirli, S., Picard, C. J., Mohler, G.,Richert, F., Akata, Z., & Dundar, M. (2023). Classifying the unknown: Insect identification with deep hierarchical Bayesian learning. Methods in Ecology and Evolution, 14,
1515-1530. https://doi.org/10.1111/2041-210X.14104
* all_image_features_resnet: vector of features extracted from the insect images with the method of the same paper as the all_dna_features_cnn_original with a pretrained resnet101
* all_dna_features_cnn_new: vector of features extracted from DNA nucleotides with our CNN
* all_image_features_gan: vector of features extracted from the insect images with out method using a ReACGAN

Note: all arrays and locs are 1-indexed like in MATLAB.

Note: the features were extracted with the same model for both the insect dataset and the unseen insect dataset.

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

Specialized collection of 0 free realistic SVG illustrations from the animals category. Photorealistic animal illustrations with detailed lion portraits, jellyfish, and lifelike animal depictions Examples include: lion portrait, jellyfish.

Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

This data accompanies work from the paper entitled:

Object Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis.

Waithe D1*,2,, Brown JM3, Reglinski K4,6,7, Diez-Sevilla I⁵, Roberts D⁵, Christian Eggeling1,4,6,8

1 Wolfson Imaging Centre Oxford and 2 MRC WIMM Centre for Computational Biology and 3 MRC Molecular Haematology Unit and 4 MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, OX3 9DS, Oxford, United Kingdom. 5 Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU.
6 Institute of Applied Optics and Biophysics, Friedrich-Schiller-University Jena, Max-Wien Platz 4, 07743 Jena, Germany.
7 University Hospital Jena (UKJ), Bachstraße 18, 07743 Jena, Germany.
8 Leibniz Institute of Photonic Technology e.V., Albert-Einstein-Straße 9, 07745 Jena, Germany.

Further details of these datasets can be found in the methods section of the above paper.

Erythroblast DAPI (+glycophorin A): erythroblast cells were stained with DAPI and for glycophorin A protein (CD235a antibody, JC159 clone, Dako) and with Alexa Fluor 488 secondary antibody (Invitrogen). DAPI staining was performed through using VectaShield Hard Set mounting solution with DAPI (Vector Lab). Num. of images used for training: 80 and testing: 80. Average number of cells per image: 4.5.

Neuroblastoma phalloidin (+DAPI): images of neuroblastoma cells (N1E115) stained with phalloidin and DAPI were acquired from the Cell Image Library [26]. Cell images in the original dataset were acquired with a larger field of view than our system and so we divided each image into four sub-images and also created ROI bounding boxes for each of the cells in the image. The images were stained for FITC-phalloidin and DAPI. Num. of images used for training: 180, testing: 180. Average number of cells per image: 11.7.

Fibroblast nucleopore: fibroblast (GM5756T) cells were stained for a nucleopore protein (anti-Nup153 mouse antibody, Abcam) and detected with anti-mouse Alexa Fluor 488. Num. of images for training: 26 and testing: 20. Average number of cells per image: 4.8.

Eukaryote DAPI: eukaryote cells were stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 40 and testing: 40. Average number of cells per image: 8.9.

C127 DAPI: C127 cells were initially treated with a technique called RASER-FISH[27], stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 30 and testing: 30. Average number of cells per image: 7.1.

HEK peroxisome All: HEK-293 cells expressing peroxisome-localized GFP-SCP2 protein. Cells were transfected with GFP-SCP2 protein, which contains the PTS-1 localization signal, which redirects the fluorescently tagged protein to the actively importing peroxisomes[28]. Cells were fixed and mounted. Num. of images for training: 55 and testing: 55. Additionally we sub-categorised the cells as ‘punctuate’ and ‘non-punctuate’, where ‘punctuate’ would represent cells that have staining where the peroxisomes are discretely visible and ‘non-punctuate’ would be diffuse staining within the cell. The ‘HEK peroxisome All’ dataset contains ROI for all the cells: average number of cells per image: 7.9. The ‘HEK peroxisome’ dataset contains only those cells with punctuate fluorescence: average number of punctuate cells per image: 3.9.

Erythroid DAPI All: Murine embryoid body-derived erythroid cells, differentiated from mES cells. Stained with DAPI and fixed and mounted in Vectashield (Vector Lab). Num. of images for training: 51 and testing: 50. Multinucleate cells are seen with this differentiation procedure. There is a variation in size of the nuclei (nuclei become smaller as differentiation proceeds). The smaller, 'late erythroid' nuclei contain heavily condensed DNA and often have heavy ‘blobs’ of heterochromatin visible. Apoptopic cells are also present, with apoptotic bodies clearly present. The ‘Erythroid DAPI All’ dataset contains ROI for all the cells in the image. Average number of cells per image: 21.5. The subset ‘Erythroid DAPI’ contains non-apoptotic cells only: average number of cells per image: 11.9

COS-7 nucleopore. Slides were acquired from GATTAquant. GATTA-Cells 1C are single color COS-7 cells stained for Nuclear pore complexes (Anti-Nup) and with Alexa Fluor 555 Fab(ab’)2 secondary stain. GATTA-Cells are embedded in ProLong Diamond. Num. of images for training: 50 and testing: 50. Average number of cells per image: 13.2

COS-7 nucleopore 40x. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 40x NA 0.6 objective. Num. of images for testing: 11. Average number of cells per image: 31.6.

COS-7 nucleopore 10x. Same GATTA-Cells 1C slides (GATTAquant) as above but imaged on Nikon microscope, with 10x NA 0.25 objective. Num. of images for testing: 20. Average number of cells per image: 24.6

Dataset Annotation

Datasets were annotated by a skilled user. These annotations represent the ground-truth of each image with bounding boxes (regions) drawn around each cell present within the staining. Annotations were produced using Fiji/ImageJ [29] ROI Manager and also through using the OMERO [30] ROI drawing interface (https://www.openmicroscopy.org/omero/). The dataset labels were then converted into a format compatible with Faster-RCNN (Pascal), YOLOv2, YOLOv3 and also RetinaNet. The scripts used to perform this conversion are documented in the repository (https://github.com/dwaithe/amca/scripts/).

LabPics 2: A newer and larger a version (But harder to use) can be found here: https://zenodo.org/record/4736111

The Vector-LabPics V1 dataset contains 2187 images of chemical experiments with materials within mostly transparent vessels in various laboratory settings and in everyday conditions such as beverage handling. Each image in the dataset has an annotation of the region of each material phase and its type. In addition, the region of each vessel and its labels, parts, and corks are also marked.

For more details see:

https://pubs.acs.org/doi/10.1021/acscentsci.0c00460

Acknowledgments

We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@SuperScienceGrl Twitter), Chemical and Engineering Science chemistry in pictures, YouTube channels dedicated to chemistry experiments: NurdRage, NileRed, DougsLab, ChemPlayer, and Koen2All. Additional sources for images include Instagram channels chemistrylover_(Joana Kulizic),Chemistry.shz (Dr.Shakerizadeh-shirazi), MinistryOfChemistry, Chemistry And Me, ChemistryLifeStyle, vacuum_distillation, and Organic_Chemistry_Lab. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support.

Images of the dataset were taken from images and videos shared on Youtube and Instagram, Twitter and Tumblr channels and other contributors; we do not have copyright for the images. Any commercial or none academic use of the images depends on acquiring permission from the owner of the images. Note that the name of each image contains the image source. For any non-academic use of the images, please contact their sources for permission. We like to thank the following channels for sharing the images used in this dataset.

We like to thank the sources of the images used for creating this dataset without them this work was not possible. These sources include Nessa Carson (@SuperScienceGrl Twitter), Chemical and Engineering Science chemistry in pictures, YouTube channels dedicated to chemistry experiments: NurdRage, NileRed, DougsLab, ChemPlayer, and Koen2All. Additional sources for images include Instagram channels chemistrylover_(Joana Kulizic),Chemistry.shz (Dr.Shakerizadeh-shirazi), MinistryOfChemistry, Chemistry And Me, ChemistryLifeStyle, vacuum_distillation, and Organic_Chemistry_Lab. We are grateful to the Defense Advanced Research Projects Agency (DARPA) for funding this project under award number W911NF-18-2-0036 from the Molecular Informatics program. A.A.-G. Thanks Anders G. Frøseth for his generous support. Images from C&EN's Chemistry in Pictures (cen.chempics.org) used here with permission from C&EN and ACS. All rights reserved. Please contact cenchempics@acs.org to inquire about republishing.

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The Aedes Mosquitoes Dataset contains 4,120 high-resolution JPEG images of Aedes aegypti and Aedes albopictus, two species globally recognized as vectors for dengue, Zika, chikungunya, and yellow fever. This dataset is designed for developing deep learning models in computer vision and public health research.