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98 results for “image generation”
GAN-based Synthetic VIIRS-like Image Generation over India
<p>Monthly nighttime lights (NTL) can clearly depict an area's prevailing intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992 onwards). In the current study, an attempt has been made to generate synthetic monthly VIIRS-like products of 1992-2012, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992-2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done using low and high thresholds. Improved DMSP and corrected VIIRS images from April 2012 - December 2013 are used in a conditional generative adversarial network (cGAN) along with Land Use Land Cover, as auxiliary input, to generate VIIRS-like imagery from 1992-2012. The modelled imagery was aggregated annually and showed an <em>R</em><sup>2</sup> of 0.94 with the results of other annual-scale VIIRS-like imagery products of India, <em>R</em><sup>2</sup> of 0.85 w.r.t GDP and <em>R</em><sup>2</sup> of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an <em>R</em><sup>2</sup> of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an <em>R</em><sup>2 </sup>of 0.56 and 0.53 respectively. Qualitative analysis for also performed on both national and state scales. Visual analysis over 1992-2013 confirms a gradual increase in the brightness of the lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, a synthetically generated monthly VIIRS-like product is delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.</p>
Image dataset for GenerativeGI: Creating Generative Art with Genetic Improvement
<p>Full dataset of our results for our submission, "GenerativeGI: Creating Generative Art with Genetic Improvement," to the Special Issue on Genetic Improvement at the Automated Software Engineering journal. This zip archive contains the images and corresponding population data (including the genome that created each image) for all results reported in our submission.</p>
Images of daphnids (control and exposed to NMs) over multiple generations, scored by experts as toxic or non-toxic and the resulting deepDaph predictions
<p>Background</p> <p>This study showcases a pioneering application of deep learning methodologies in ecotoxicology, aimed at facilitating hazard assessment and safer design of engineered nanomaterials (ENMs). The research hinges on a high-quality dataset comprising microscopic images of Daphnia magna exposed to various ENMs, collected systematically under controlled conditions.</p> <p>The Dataset: A Cornerstone of Nanoinformatics</p> <p>Our dataset, which will be openly accessible on Zenodo, serves as a foundational resource for the ecotoxicology community. It contains high-resolution images tagged with intricate details like malformations, tail lengths, lipid concentrations, and lipid deposit shapes. Researchers can use this exhaustive dataset to train a variety of predictive models for diverse applications.</p> <p>Methodology</p> <p>We employ two different deep learning architectures to process the dataset. These architectures automatically detect malformations and assess the impact of ENMs on D. magna by classifying various biological structures based on lipid densities.</p> <p>Results and Validation</p> <p>The developed models demonstrate high statistical validation, confirming their prediction accuracy on external D. magna images. Our dataset and the associated models not only accelerate manual procedures but also pave the way for automated, high-throughput analyses in ecotoxicology.</p> <p>Future Prospects</p> <p>The dataset holds the potential to extend investigations into predicting the impacts on future generations from parental exposures, thus reducing the time and cost of multi-generational toxicity assays.</p>
Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts Data and Scripts
<p>The repository contains the data corresponding to the Paper "Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts".</p> <p>Random30, Random50, Random100, Similiar10, Similar30 and Similar50.zip contain the data sets (obj Files).</p> <p>R30_physical_images.zip and sim50_physical_images.zip contain the photos made from the physical components which are used for the evaluation.</p>
Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training
<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p> <p><strong>Format</strong>:<br> HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li> <li>scikit-image==0.12.0 implementation of cubic spline rotation</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Electrostatic OAM generator with boundary condition - Phase images of the boundary conditions
<p>Here reported is the dataset that can be used to reconstruct the phase of the electron beam (process already done for the files which have a C_P in the name) after it has interacted with the electrodes that are used as boundary condition for our electrostatic variable electron vortex beam generator that allow to improve its functioning.</p> <p> </p>
Test Image Set for Pyramid Generation
<p>A set of 7 images for a test of pyramid generation of the OMERO.server</p> <p>Varying sizes and file formats</p>
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
User Generated Content (EOL v2): image order
A starting point for images in EOL v3, based on v2 user ratings and user selections as exemplar, translated into the new media tab system. Format: order begins 0, 1, 2, 3... counting from the top of the media gallery if firstOrLast=first, counting from the bottom of the media gallery if firstOrLast=last For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): taxonomic propagation - image ratings
<p></p>https://eol-jira.bibalex.org/browse/DATA-1786 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): taxonomic propagation - exemplar images
<p></p>https://eol-jira.bibalex.org/browse/DATA-1786 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): user exemplar images
<p></p>https://eol-jira.bibalex.org/browse/DATA-1746 Data as of Oct 25, 2018 For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): user image ratings
<p></p>https://eol-jira.bibalex.org/browse/DATA-1741 Data as of Oct 25, 2018. For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
User Generated Content (EOL v2): user image cropping
From DATA-1740 Data as of Oct 25, 2018 Initial report: <p></p>https://editors.eol.org/other_files/EOL_v2_files/image_crops.txt.zip From DATA-1805. Same report, now with EOL_pk: <p></p>https://editors.eol.org/other_files/EOL_v2_files/image_crops_withEOL_pk.txt.zip For questions or use cases calling for large, multi-use aggregate data files, please visit the EOL Services forum at <p></p>http://discuss.eol.org/c/eol-services
GalSim-Hub Generative Model of COSMOS images
<p>This dataset contains trained weights for the generative model of COSMOS galaxies described in (Lanusse et al. 2020, https://arxiv.org/abs/2008.03833).</p> <p>It is meant to be used through the GalSim Hub library (https://github.com/McWilliamsCenter/galsim_hub), and accessed as:</p> <pre><code class="language-python">import galsim import galsim_hub from astropy.table import Table # Load a generative model from the online repository model = galsim_hub.GenerativeGalaxyModel('hub:Lanusse2020')</code></pre> <p> </p>
Data from "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness"
<p>This repository contains the data from the paper, "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness." </p> <p>Relevant URLs:</p> <p>https://hewlettpackard.github.io/trust-ml/</p> <p>https://github.com/HewlettPackard/trust-ml/</p> <p> </p> <p>Abstract:</p> <p>We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. The RLAB framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment. The benchmark can generate datasets at various distortion levels to assess the robustness of different image classifiers. Our results show that the adversarial samples generated by our framework with any of the image classification models, like ResNet-50, Inception-V3, and VGG-16, are effective and transferable to other models causing them to fail. These failures happen even when these models are adversarially retrained using state-of-the-art techniques, demonstrating the generalizability of our adversarial samples. Our framework also allows the creation of adversarial samples for non-ground truth classes at different levels of intensity, enabling tunable benchmarks for the evaluation of false positives. We achieve competitive performance in terms of net $L_2$ distortion compared to state-of-the-art benchmark techniques on CIFAR-10 and ImageNet; however, we demonstrate our framework achieves such results with simple distortions like Gaussian noise without introducing unnatural artifacts or color bleeds. This is made possible by a model-based reinforcement learning (RL) agent and a technique that reduces a deep tree search of the image for model sensitivity to perturbations, to a one-level analysis and action. The flexibility of choosing distortions and setting classification probability thresholds for multiple classes makes our framework suitable for algorithmic audits.</p>
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
<p>This upload contains the datasets used in <a href="https://arxiv.org/pdf/2302.11594.pdf">arXiv:2302.11594</a>. The file <em>g4-showers_950k_10x10_train_val_test.pt</em> contains the <strong>760k training</strong>, <strong>95k validation</strong> and <strong>95k test</strong> showers as well as their incident energies. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch </em></p> <p><em>list_tensors = torch.load(args.file_path)</em></p> <p><em>for (idx, tensor) in enumerate(list_tensors):</em></p> <p><em> [showers_train, showers_val, showers_test, inc_energies_train, inc_energies_val, inc_energies_test] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>The file <em>g4-showers_665k_10x10_test.pt</em> contains 665k additional showers that were used for the classifier scaling studies, in addition to the 95k test showers from the file <em>g4-showers_950k_10x10_train_val_test.pt</em>. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch</em></p> <p><em>list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") </em></p> <p><em>[showers_geant, inc_energies_geant] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>A detailed description of how the datasets were simulated can be found in the paper. </p>
Development and Translation of Generator-Produced PET Tracer for Myocardial Perfusion Imaging-Dosimetry Group
ClinicalTrials.gov study NCT05280782. IPD Sharing: NO. Countries: 1. Publications: 1.
Next Generation X-ray Imaging System
ClinicalTrials.gov study NCT04571099. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.