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55 results for “learning objects”
OLVSL_ Object-location visual statistical learning
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Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Example Data)
<p><strong>Overview </strong></p> <p>This repository contains 2 example 4D-STEM datasets format from the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The data was written to hdf5 for compatibility with the python programming language. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly.</p> <p> </p> <p><strong>Parameters</strong></p> <p>The twisted bilayer graphene dataset is simulated. The smaller file is an experimental SrTiO<sub>3</sub> dataset.</p> <table> <thead> <tr> <th scope="row"> </th> <th scope="col">Graphene</th> <th scope="col">STO</th> </tr> </thead> <tbody> <tr> <th scope="row">E0</th> <td>200kV</td> <td>300kV</td> </tr> <tr> <th scope="row">Apeture</th> <td>25 mrad</td> <td>20 mrad</td> </tr> <tr> <th scope="row">Detector Size</th> <td>2.5 Å<sup>-1</sup></td> <td>1.6671 Å<sup>-1</sup></td> </tr> <tr> <th scope="row">Dimensions</th> <td>101x101x128x128</td> <td>60x60x64x64</td> </tr> <tr> <th scope="row">Step Size</th> <td>0.2</td> <td>0.1818</td> </tr> </tbody> </table> <p><br> </p>
Outputs of the Jupyter Notebook - Detecting floating objects using Deep Learning and Sentinel-2 imagery
<p>The dataset contains the outputs of the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery" published in the ocean modelling section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Jamila Mifdal (author), European Space Agency Φ-lab, <a href="https://github.com/jmifdal">@jmifdal</a></p> </li> <li> <p>Raquel Carmo (author), European Space Agency Φ-lab, <a href="https://github.com/raquelcarmo">@raquelcarmo</a></p> </li> <li> <p>Marc Rußwurm (author), EPFL-ECEO, <a href="https://github.com/MarcCoru">@marccoru</a></p> </li> </ul>
Phase Object Reconstruction for 4D-STEM using Deep Learning, (4D-STEM Training Data)
<p><strong>Overview </strong></p> <p>This repository contains 742,688 samples of simulated Convergent Beam Electron Diffraction patterns (CBEDs); the training data for the paper <a href="https://arxiv.org/abs/2202.12611">"Phase Object Reconstruction for 4D-STEM using Deep Learning"</a>. The folder contains multiple hdf5 datasets. Each dataset has a corresponding Excel-sheet containing detailed information and simulation parameters for every datapoint, as well as a summary-report containing the parameter distributions, hdf5-infos and random number generator settings. This makes every dataset reproducible, using the simulation codes provided in <a href="https://github.com/ThFriedrich/ap_data_generation">https://github.com/ThFriedrich/ap_data_generation</a>.</p> <p><strong>Technical details</strong></p> <p>Every Datapoint consists of a 3x3 set of adjacent Convergent Beam Electron Diffraction pattern (CBEDs), the coherent exit wave phase and amplitude in real and reciprocal space, and the probe functions phase and amplitude in real space. All patterns are 64x64 pixel in 16 bit unsigned integer data format.</p> <p>Every hdf5 file has the following structure:</p> <table> <tbody> <tr> <td>Attributes</td> <td>'Seed': 6108236<br> 'State': 251786606 ...<br> 'Type': 'twister'<br> 'arch': 'glnxa64'<br> 'gpu': 'NVIDIA GeForce RTX 3080'<br> 'matlab_ver': '2021a'</td> </tr> <tr> <td>Dataset 'features'</td> <td> <p>Size: 64x64x9x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_k'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'labels_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'probe_r'</td> <td> <p>Size: 64x64x2x5000<br> Datatype: H5T_STD_U16LE (uint16)</p> </td> </tr> <tr> <td>Dataset 'meta'</td> <td> <p>Size: 19x5000<br> Datatype: H5T_IEEE_F32LE (single)</p> </td> </tr> </tbody> </table> <p>The data was written to hdf5 in matlab. When reading from these files consider possibly different storage conventions (Row major vs. column major format). Data may need to be transposed accordingly. The integer arrays were scaled to use the full range of the uint16 datatype. The scaling values are stored under "meta". To restore the original values in floating point numbers, convert the arrays like this:</p> <p>Matlab:</p> <pre><code>hdf_file = ['db_h5_b_5_Training.h5']; n = 128; % load `n` k-space exit waves x = single(h5read(hdf_file, '/labels_k', [1,1,1,1], [64,64,2,n])); % `meta` contains parameters and scaling factors for a given datapoint in following order: [E_0(keV), cond_lens_outer_aper_ang(mrad), collection angle(rA), step_size(A), scale_cbed_1 ... scale_cbed_9, scale_phase_k, scale_amp_k, scale_phase_r, scale_amp_r, scale_probe_phase_r, scale_probe_amp_r] s = h5read(hdf_file, '/meta', [14,1], [2,n]); amplitude = zeros(64,64,n); phase = zeros(64,64,n); for ix = 1:n phase(:,:,n) = (x(:,:,1,n)*s(1,ix) / 65536) - pi; amplitude(:,:,n) = (x(:,:,2,n)*s(2,ix)) / 65536; end % The 9 CBEDs correspond to a 3x3 kernel of patterns. The order in [x,y] is: %[[3, 6, 9]; % [2, 5, 8]; % [1, 4, 7]] </code></pre>
HOWS-CL-25: Household Objects Within Simulation Dataset for Continual Learning
<p>HOWS-CL-25 (Household Objects Within Simulation dataset for Continual Learning) is a synthetic dataset especially designed for object classification on mobile robots operating in a changing environment (like a household), where it is important to learn new, never seen objects on the fly.<br> This dataset can also be used for other learning use-cases, like instance segmentation or depth estimation.<br> Or where household objects or continual learning are of interest.</p> <p>Our dataset contains 150,795 unique synthetic images using 25 different household categories with 925 3D models in total. For each of those categories, we generated about 6000 RGB images. In addition, we also provide a corresponding depth, segmentation, and normal image.</p> <p>The dataset was created with BlenderProc [Denninger et al. (2019)], a procedural pipeline to generate images for deep learning.<br> This tool created a virtual room with randomly textured floors, walls, and a light source with randomly chosen light intensity and color. After that, a 3D model is placed in the resulting room. This object gets customized by randomly assigning materials, including different textures, to achieve a diverse dataset. Moreover, each object might be deformed with a random<br> displacement texture.<br> We use 774 3D models from the ShapeNet dataset [A. X. Chang et al. (2015)] and the other models from various internet sites. Please note that we had to manually fix and filter most of the models with Blender before using them in the pipeline!</p> <p>For continual learning (CL), we provide two different loading schemes:<br> - Five sequences with five categories each<br> - Twelve sequences with three categories in the first and two in the other sequences.</p> <p>In addition to the RGB, depth, segmentation, and normal images, we also provide the calculated features of the RGB images (by ResNet50) as used in our RECALL paper.<br> In those two loading schemes, ten percent of the images are used for validation, where we ensure that an object instance is either in the training or the validation set, not in both. This avoids learning to recognize certain instances by heart.</p> <p>We recommend using those loading schemes to compare your approach with others.</p> <p>Here we provide three files for download:<br> - HOWS_CL_25.zip [124GB]: This is the original dataset with the RGB, depth, segmentation, and normal images, as well as the loading schemes. It is divided into three archive parts. To open the dataset, please ensure to download all three parts.<br> - HOWS_CL_25_hdf5_features.zip [2.5GB]: This only contains the calculated features from the RGB input by a ResNet50 in a .hdf5 file. Download this if you want to use the dataset for learning and/or want to compare your approach to our RECALL approach (where we used the same features).<br> - README.md: Some additional explanation.</p> <p>For further information and code examples, please have a look at our website: https://github.com/DLR-RM/RECALL.</p>
FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing
<p>Pascal-Part-201 is the most comprehensive and challenging version of the Pascal-Part dataset for Multi-object multi-part scene parsing. The dataset is the part of the publication "FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing" published in CVPR 2022.</p>
EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping
<p>EDLO2ID: An Efficient-deep-learning-and-object-oriented Image Dataset for Large-scene Mapping </p> <p>The dataset can be unzipped and includes an image dataset and a vector dataset, which includes nine land use/land cover categories (i.e., cropland, orchard, forestland, grassland, construction land, transportation land, water body, bare land, terrace) for object-oriented remote sensing image mapping using deep learning.</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Data from: SimPLE: A visuotactile method learned in simulation to precisely pick, localize, regrasp, and place objects
<p>Existing robotic systems have a clear tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking precise generalization, i.e., the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick-and-place. In precise pick-and-place, i.e. kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose simPLE (simulation to Pick Localize and PLacE) as a solution to precise pick-and-place. simPLE learns to pick, regrasp and place objects precisely, given only the object CAD model and no prior experience. We develop three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model relies on matching real observations against a set of simulated ones through supervised learning. Finally, we compute the desired robot motion by solving a shortest path problem on a graph of hand-to-hand regrasps. On a dual-arm robot equipped with visuotactile sensing, we demonstrate pick-and-place of 15 diverse objects with simPLE. The objects span a wide range of shapes and simPLE achieves successful placements into structured arrangements with 1mm clearance over 90% of the time for 6 objects, and over 80% of the time for 11 objects.</p>
Transfer learning with generative models for object detection on limited datasets
<p>The provided datasets are used for the analysis in the work "Transfer learning with generative models for object detection on limited datasets" (https://doi.org/10.1088/2632-2153/ad65b5). The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains, for instance ranging from geophysics to biology and medicine. The provided datasets are built with the help of Gligen and the already existing NuImages, Ozfish and Deepfish datasets. The file "CarGenerated.zip" contains images generated with Gligen and with provided bounding boxes around cars in an urban environment. The file "fishes_on_bkg.zip" provides fish images generated with fishes from Deepfish inpainted with Gligen on generated backgrounds. The file "fish_text.zip" contains images completely generated with Gligen containing fishes with annotated bounding boxes. Finally, the file "oz_masked_512.zip" contains a simpler dataset of copy paste images of Deepfish fishes on Ozfish backrounds. All the files contains the images saved in different folders for training and validation, plus an index file called gt_fish.csv for the bounding boxes.</p>
Figure 8. Prototype Front-End-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>We modeled classes for each AGLO section: scenario, theory, questions, and<br> feedbacks. We modeled domain specific classes for the learned concepts like trees and graphs<br> having generative methods controllable through parameters.<br> Figure 8 depicts our prototype front-end.</p>
Figure 7. Generated Tree Example-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we give an example of a model instantiation. We describe the instantiation<br> process for all the symbols in the AGLO model. The nNoOfElements symbol was instantiated with<br> the generated value of 10. The tabKeys symbol was instantiated with the [A,B,C,D,E,F,G,H,I]<br> generated array. The tree symbol was instantiated with a ”Tree” object built with arguments equal<br> with previously instantiated symbols. Class ”Tree” is part of the auxiliary JavaScript library.The treeSVG symbol was assigned to the tree SVG string representation obtained by a method call and<br> is depicted in figure 7.</p>
Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>analyzed AGLO that is displayed to the learner for localization and selection purposes.<br> The second XML element is the scenario element containing a text description of the AGLO<br> and a set of symbols. The description is expressed in natural language and we can notice that it<br> contains four main steps:<br> i) random tree generation;<br> ii) index computation for presentation;<br> iii) parent index computation for answer validation;<br> iv) access to the first two keys for particular feedback generation.<br> In the scenario section depicted in figure 5 several symbols are defined with the following<br> semantics.</p>
Figure 6. AGLO Presentation Sections-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The seventh and eighth symbols named node1 and node2, both of type character, are<br> initialized with the first two values from the keys array. These symbols will be used later in the<br> feedback section. Their initialization is based on accessing the keys using the array indexing<br> operator.<br> Next, we discuss about the presentation sections of the AGLO depicted in figure 6.</p>
Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>Formally, the theory section (see line 11) contains only static data. Here the educator places<br> theoretical information about the learned concept in HTML format.<br> The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix<br> of static data and dynamic values referring the previously computed symbol values. The semantic of<br> these sections is to create a dynamic content to be presented to the learner. Formalisms for specific<br> details are also present, like:<br> i) correctness - because we need to know which is the correct answer if they are multiple;<br> ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and<br> showed to the learner only in certain conditions.</p>
Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The proposed model is defined using the EBNF meta-language resulting in the concise<br> grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and<br> space reasons.<br> Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question;<br> v) answers; vi) feedbacks. Next, we will explain each section in details.</p>
Figure 4. AGLO Name and Description-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we describe an AGLO model example. Figure 4 presents in details all the<br> XML elements containing AGLO metadata.</p>
Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>E-learning is a key area of research with a great influence on the developments of several<br> industries. For example, the nowadays ITC industry is in a continuous growth because of its<br> applications in almost all industrial domains. Companies tend to lack qualified human resources and<br> because of that they reject high economical value projects. In response to this lack of human<br> resource problem, universities started to develop several alternative study programs, many of them<br> are based on e-learning technology and namely on electronic learning materials. Learning objects<br> (LO) are considered to be digital resources that support learning and can be delivered across<br> networks in large or small sizes (Wiley, 2000). In order to increase the reusability and<br> interoperability of LOs, standards were developed by several organizations (IEEE Learning<br> Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
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OpenNeuro
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