383 machine learning datasets
383 dataset results
OCTScenes contains 5000 tabletop scenes with a total of 15 everyday objects. Each scene is captured in 60 frames covering a 360-degree perspective.
Multi-Person Interaction Motion (MI-Motion) Dataset includes skeleton sequences of multiple individuals collected by motion capture systems and refined and synthesized using a game engine. The dataset contains 167k frames of interacting people's skeleton poses and is categorized into 5 different activity scenes.
A dataset made of 3D image data and their embeddings to test TomoSAM
The ARTE database, so far, contains 13 acoustic environments that were recorded with a purpose-built 62-channel microphone array in various locations around Sydney (Australia), and was decoded into the higher-order Ambisonics (HOA) format.
Gait3D-Parsing is a dataset for gait recognition in the wild. It is an extension of the large-scale and challenging Gait-3D dataset which is collected from an in-the-wild environment. The train set has 3,000 IDs, and the test set has 1,000 IDs. Meanwhile, 1,000 sequences in the test set are taken as the query set, and the rest of the test set is taken as the gallery set.
Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. Robot@Home2 consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022.
Synthetic dataset comprising three different environments for multi-camera dynamic novel view synthesis for soccer. This dataset is made compatible for Nerfstudio, and includes data parsers with various settings to reproduce the settings of our paper "Dynamic NeRFs for Soccer Scenes" and more.
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, many works have explored the use of NeFs as representations for downstream tasks, e.g. classifying an image based on the parameters of a NeF that has been fit to it. However, the impact of the NeF hyperparameters on their quality as downstream representation is scarcely understood and remains largely unexplored. This is partly caused by the large amount of time required to fit datasets of neural fields.
Data for Score-Based Generative Models for PET Image Reconstruction. All simuations based on BrainWeb dataset. The image simulation either taken from Georg Schramm's BrainWeb simulation in 2D, or in 3D it was simulated using BrainWeb package. The 2D measurements were simulated using pyParallelProj and 3D measurements using SIRF (with STIR backend).
Super-CLEVR-3D is a visual question answering (VQA) dataset where the questions are about the explicit 3D configuration of the objects from images (i.e. 3D poses, parts, and occlusion). It consists of objects from 5 categories: aeroplanes, buses, bicycles, cars and motorbikes. The rendered objects are from CGParts dataset, with the same setting as Super-CLEVR dataset.
This is the full dataset for the paper Fourier neural operator for real-time simulation of 3D dynamic urban microclimate. The dataset of 3D urban wind simulation data of Niigata is generated from CityFFD. A total of 1200 steps of wind simulation were executed. The dataset contains four wind directions of data. Data for the west and north winds include all 1200 simulation steps. Data for the east and south winds include the last 50 steps of the simulation. Each step of the data is a 200 * 200 * 150 array with 32-bit precision and is stored as a numpy file.
A total of 227 cross sectional images (20 x 54 mm with a resolution of 289 x 648 pixels) of hind-leg xenograft tumors from 29 mice were obtained with 1mm step-wise movement of the array mounted on a manual positioning device. The whole tumor volume was acquired using a diagnostic ultrasound system with a 10 MHz linear transducer and 50 MHz sampling.
From https://github.com/MMintLab/VIRDO/blob/master/data/dataset_readme.txt,
Dataset of paired thermal and RGB images comprising ten diverse scenes—six indoor and four outdoor scenes— for 3D scene reconstruction and novel view synthesis (e.g. with NeRF).
3D design file repository for the Stickbug Robot a 6 armed holonomic precision pollination robot
ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10km2 with close to 600 million points and features eleven distinct object categories. To guarantee the dataset's quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3D urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications.
BS-Objaverse 660k Dataset is a set of GPT4-Vision-powered multi-modal captions data. It is constructed to enhance modality alignment and fine-grained visual concept perception for describing detailed information about the shape, texture of Objaverse 3D object.
The ULS23 training dataset contains 38,693 diverse lesions from chest-abdomen-pelvis CT examinations. For the challenge, we introduced two novel 3D annotated datasets targeting lesions in the pancreas and bones, which are traditionally challenging to segment. Additionally, we aggregate 10 publicly available datasets with a lesion segmentation component into a single, easily accessible data repository.