19,997 machine learning datasets
19,997 dataset results
persian translation of K-QA dataset
The Industrial Textile Defect Detection (ITDD) dataset includes 1885 industrial textile images categorized into 4 categories: cotton fabric, dyed fabric, hemp fabric, and plaid fabric. These classes are collected from the industrial production sites of WEIQIAO Textile. ITDD is an upgraded version of WFDD that reorganizes three original classes and adds one new class.
WebGen-Bench WebGen-Bench is created to benchmark LLM-based agent's ability to generate websites from scratch. The dataset is introduced in WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch. It contains 101 instructions and 647 test cases. It also has a training set of 6667 instructions, named WebGen-Instruct.
The first and the one open dataset for Russian finger- spelling, contained 1,593 annotated phrases and over 37 thousand HD+ videos.
PubMedQA-MetaGen: Metadata-Enriched PubMedQA Corpus
Enumerate–Conjecture–Prove: Formally Solving Answer-Construction Problem in Math Competitions We release the ConstructiveBench dataset as part of our Enumerate–Conjecture–Prove (ECP) paper. It enables benchmarking automated reasoning systems on answer-construction math problems using Lean 4.
We present UniTalk, a novel dataset specifically designed for the task of active speaker detection, emphasizing challenging scenarios to enhance model generalization. Unlike previously established benchmarks such as AVA, which predominantly features old movies and thus exhibits significant domain gaps, UniTalk focuses explicitly on diverse and difficult real-world conditions. These include underrepresented languages, noisy backgrounds, and crowded scenes - such as multiple visible speakers speaking concurrently or in overlapping turns. It contains over 44.5 hours of video with frame-level active speaker annotations across 48,693 speaking identities, and spans a broad range of video types that reflect real-world conditions. Through rigorous evaluation, we show that state-of-the-art models, while achieving nearly perfect scores on AVA, fail to reach saturation on UniTalk, suggesting that the ASD task remains far from solved under realistic conditions. Nevertheless, models trained on UniT
Vehicles in the Middle East (VME) dataset, designed explicitly for vehicle detection in high-resolution satellite images from Middle Eastern countries. Sourced from Maxar, the VME dataset spans 54 cities across 12 countries, comprising over 4,000 image tiles and more than 100,000 vehicles, annotated using both manual and semi-automated methods. Also, we introduce the largest benchmark dataset for Car Detection in Satellite Imagery (CDSI), combining images from multiple sources to enhance global car detection.
We introduce SuperRS-VQA (avg. 8,376×8,376) and HighRS-VQA (avg. 2,000×1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world dialogue tasks
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https://osf.io/btjqw/?view_only=f31eda86e7b04ac886734a26cd2ce43d
https://osf.io/xney5/
https://osf.io/6y8cv/
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🏃♂️ Open-HypermotionX Dataset Open-Hypermotion is a large-scale, high-quality dataset designed for training and evaluating pose-guided human image animation models, with a special focus on complex, dynamic human motions (Hypermotion), such as flips, spins, and acrobatics.
The largest aerial-ground video dataset with 6,632 identities, 32,321 tracklets, and 9.6 million frames Multiple platforms combining aerial drones (at various altitudes: 15m, 30m, 80m, 120m), CCTV cameras, and wearable cameras Rich annotations including clothing changes and 15 soft-biometric attributes Real-world challenges including extreme viewpoint variations, scale differences, and occlusions Diverse environments with outdoor campus scenes captured over 20 days
RTP Dataset (Reactant–Transition State–Product Dataset) The RTP dataset forms the core training and evaluation set and consists of 35,087 molecular geometries sampled from 11,961 unique elementary reactions. For each reaction, three critical geometries are included: the optimized reactant, transition state (TS), and product. Each geometry is labeled with its corresponding DFT-computed potential energy, atomic forces, and Hessian matrix, calculated at the wb97xd/6-31g(d) level of theory. This dataset represents stationary points (critical points) on the potential energy surface and serves as the foundation for training the MLIPs to reproduce energies, gradients, and curvatures.
The Matador dataset is a material image dataset with hierarchical labels. The hierarchical labels are derived from a new taxonomy. For each sample of a material, we collect a local appearance image, local surface structure LiDAR scan, global context image, and record any camera motion that takes place during the capture sequence. The dataset is intended to grow over time. To date, Matador contains 57 different material categories and a total of ~7,200 images, averaging 126 samples of intraclass variance.
A collection of test sets for evaluating base and chat LLMs (incl. VLMs) on Greek generation and understanding capabilities.