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

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Dataset

This dataset was created by Глеб Мехряков

Released under MIT

Contents

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.

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 (descending).

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

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

K denotes the size of visual vocabulary.

Context

This dataset is a repositpry of sign language images taken by the Anki Vector robot. To understand the American sign language for the English alphabet, please take a look at the following video: https://www.youtube.com/watch?v=a5BD8SjhPSg

Content

The dataset contains roughly 8500 images. Images are labelled according to the sign language, for e.g. all images with a_*.png are labels for pictures with sign for the alphabet 'a' taken by vector. All images for the background (with no sign) are labelled as background_a.

Acknowledgements

Thanks to the entire ex-Anki team for working on a fantastic robot and making the SDK available free,

Inspiration

Lets train a model to enable robots to accurately understand the human sign language.

More

More material wrt this dataset is available in my online course: 'Learn AI with a robot', available at http://robotics.thinkific.com

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)

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.

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

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

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

Dataset

This dataset was created by Pipe Runner (Old Account)

Released under Database: Open Database, Contents: Database Contents

Contents

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/).

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

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

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%.