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7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “sim2real”
sim2real-iCub
<ul> <li>24000 camera images (with 12000 mask) from iCub simulation and real robot (at Imperial College London).</li> <li>a subset of real robot images (2000 image) in "_tiny" folders</li> </ul>
sim2real-iCub-iit
<p>Camera images (with mask) from iCub simulation and real robot (at Istituto Italiano di Tecnologia, Italy).</p> <ul> <li>4220 synthetic simulation images for training</li> <li>4220 synthetic simulation images for testing</li> <li>2000 real images for training</li> <li>2000 real images for testing</li> </ul>
sim2real-iCub-iit2
<p>Camera images (with mask) from iCub simulation and real robot (at Istituto Italiano di Tecnologia, Italy).</p> <ul> <li>4220 synthetic simulation images for training</li> <li>4220 synthetic simulation images for testing</li> <li>2239 real images for training</li> <li>2239 real images for testing</li> </ul>
Imperial-sim2real
<p>Dataset for Transferring Visuomotor Learning from Simulation to the Real World in iCub Humanoid robot. It includes:</p> <p>- Sim: simulation dataset, including stereo-vision, arm joint measurement and head joint measurement <br> - Real: real robot dataset, including stereo-vision, arm joint measurement and head joint measurement<br> - cycleGAN: dataset to train cycleGAN model to transfer simulated images to real images.</p>
ADAPT JR-Sim2Real dataset
<p>This synthetic training dataset was created specifically for the ADAPT sim2real Object Detection Challenge 2023. In order to create a comprehensive dataset, we were provided with 12 CAD models which allowed us to create a variety of data samples under different lighting and other environmental conditions. Our aim was to closely mimic real-world scenarios within the dataset. This realism was crucial for training a robust and effective object recognition model capable of accurately identifying objects in complex visual scenes. The Python package PlotOptiX v0.17.1, which uses NVIDIA's OptiX raytracing engine, was used to generate the images. The ray tracing process involved the creation of 4 camera trajectories, each characterised by varying radii and increasing heights, with additional up and down variations built in. </p>
Sim2real flower detection towards automated Calendula harvesting
<p>This dataset serves as supplementary material for the research paper titled '<em>Sim2real flower detection towards automated Calendula harvesting</em>', which was published in the October 2023 issue of Biosystems Engineering. Within this upload, you will find a collection of both authentic and computer-generated images featuring Calendula (<em>Calendula officinalis L.</em>) flowers. Additionally, we have included the resources and original data utilized in generating the synthetic images. This dataset proves instrumental in demonstrating the successful transference of a deep neural network from simulation to real-world applications.</p> <p>The contents of this upload includes:</p> <ol> <li>Original RGB and depth images of Calendula flowers captured in a natural flower field, complete with bounding box annotations.</li> <li>The test data employed in our experiments.</li> <li>Unedited RGB images that were used in the photogrammetry pipeline.</li> <li>Three-dimensional models representing Calendula flowers.</li> <li>The resulting dataset of synthetic images.</li> </ol> <p>The synthetic dataset follows the Synthetic Optimized Labeled Objects (SOLO) Dataset Schema, <a href="https://github.com/Unity-Technologies/com.unity.perception/blob/main/com.unity.perception/Documentation~/Schema/SoloSchema.md">as defined in the Unity Perception package</a>. For completeness, the data scheme is also included in the upload.</p> <p>For more comprehensive details regarding this dataset and its associated metadata, we invite you to consult the published article in Biosystems Engineering, available at the following link: <a href="https://doi.org/10.1016/j.biosystemseng.2023.08.016">https://doi.org/10.1016/j.biosystemseng.2023.08.016</a>. </p>
Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors, dataset
<p>This is the dataset used in the 'Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors.' It contains 2 folders:</p> <ol> <li><strong>train_diffusion</strong>: a set of 5,000 images used to train the diffusion model.</li> <li><strong>translated</strong>: a set of approximately 50,000 images converted via a diffusion model pre-trained on a small set of real images.</li> </ol> <p>The 'translated' folder contains a <code>labels.csv</code> file, providing the corresponding label for every image.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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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.