19,997 machine learning datasets
19,997 dataset results
Node-level tasks.
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We processed 241 pairs of CXR and DES soft tissue images from the JSRT dataset by performing operations like inversion and contrast adjustment to convert these images into negative formats more frequently used in clinical settings.
This Wider-Test-200 dataset is introduced in the following paper: "Towards Unsupervised Blind Face Restoration using Diffusion Prior"
PsOCR is a large-scale synthetic dataset for Optical Character Recognition in low-resource Pashto language.
CSTS: Correlation Structures in Time Series CSTS is a comprehensive synthetic benchmarking dataset designed specifically for evaluating correlation structure discovery in time series data. The dataset systematically models known correlation structures between time series variables and enables rigorous assessment of clustering algorithms and validation methods.
ValiMath is a high-quality benchmark consisting of 2,147 carefully curated mathematical questions designed to evaluate an LLM's ability to verify the correctness of math questions based on multiple logic-based and structural criteria.
For slide editing, this benchmark dataset provide the pair of user instruction and corresponding slides (pptx).
The ObMan dataset is a a large-scale synthetic image dataset of hands grasping objects. Body poses are sampled from MoCap data, and hand poses are generated by the automatic robotic grasping software GraspIt. The realistic body model SMPL+H is rendered grasping ShapeNet object models under a large variation in pose, background, texture, and lighting. 150K images are generated, along with ground truth 3D hand and object meshes, 2D/3D hand keypoints, object and hand segmentations, and depth maps.
A dataset for pose estimation of hand when interacting with object and severe occlusions.
A dataset for pose estimation of hand when interacting with object and severe occlusions.
We introduce the first monocular motion capture method from a video that regresses 3D hand and face motions along with deformations arising from their interactions. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, hand-face contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations. Our final 3D hand and face reconstructions are realistic and more plausible co
https://proceedings.neurips.cc/paper_files/paper/2022/hash/82ad13ec01f9fe44c01cb91814fd7b8c-Abstract-Conference.html
https://arxiv.org/abs/2505.15372
This dataset provides high-resolution videos recorded from three perspectives with more than 110 hours of total playtime showing mice solving complex tasks. We provide frame-level action labels that reflect a mouse's actions (in proximity to, touch, bite, lock, unlock, touch reward) with lockbox mechanisms (lever, stick, ball, sliding door) for 13% of the data.
Role-Playing Eval (RPEval) is a benchmark dataset designed to evaluate large language models' role-playing abilities across emotional understanding, decision-making, moral alignment, and in-character consistency.
Bicycle parts based detection dataset used in "Objective Bicycle Occlusion Level Classification using a Deformable Parts-Based Model" Angelique Mangubat and Shane Gilroy.
BOOM (Benchmark of Observability Metrics) is a large-scale, real-world time series dataset designed for evaluating models on forecasting tasks in complex observability environments. Composed of real-world metrics data collected from Datadog, a leading observability platform, the benchmark captures the irregularity, structural complexity, and heavy-tailed statistics typical of production observability data. Unlike synthetic or curated benchmarks, BOOM reflects the full diversity and unpredictability of operational signals observed in distributed systems, covering infrastructure, networking, databases, security, and application-level metrics.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).