19,997 machine learning datasets
19,997 dataset results
The ontology files, readme and statistical information can be found and browsed in the ontology library. Because many of the ontologies make use of imports, we have "localised" ontologies by parsing them, resolving and parsing all imports, merging the main and imported ontologies together, and re-serialising the ontology, all using the OWL API. The original main ontology and the imported ontologies are saved in the "sources/".
SimpEvalASSET is a dataset for learning learnable metrics using modern language models. It comprises of 12K human ratings on 2.4K simplifications of 24 systems, and SIMPEVAL_2022, a challenging simplification benchmark consisting of over 1K human ratings of 360 simplifications including generations from GPT-3.5.
FedTADBench is a federated time series anomaly detection benchmark. It covers 5 time series anomaly detection algorithms, 4 federated learning frameworks, and 3 time series anomaly detection datasets.
CAP-DATA is a large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames together with labeled fact-effect-reason-introspection description and temporal accident frame label. It can support many useful tasks for accident inference, such as accident detection and prediction (AccidentDet/Pre), causal inference of accident (Accident-Causal), accident classification (Accident-Cla), text-video based accident retrieval (Accident-Retri), and question answering in an accident (Accident-QA) of the driving scene.
A dataset of games played in the card game "Cards Against Humanity" (CAH), by human players, derived from the online CAH labs. Each round includes the cards presented to users - a "black" prompt with a blank or question and 10 "white" punchlines as possible responses, and which punchline was picked by a player each round, along with text and metadata.
lilGym is a benchmark for language-conditioned reinforcement learning in visual environment based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty.
The Autonomous-driving StreAming Perception (ASAP) benchmark is a benchmark to evaluate the online performance of vision-centric perception in autonomous driving. It extends the 2Hz annotated nuScenes dataset by generating high-frame-rate labels for the 12Hz raw images.
RegDB-C* is an evaluation set that consists of algorithmically generated corruptions applied to the RegDB test-set, and especially to both the visible and the thermal data. In comparison with the RegDB-C dataset proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.
SYSU-MM01-C* is an evaluation set that consists of algorithmically generated corruptions applied to the SYSU-MM01 test-set, and especially to both the visible and the thermal data. In comparison with the SYSU-MM01-C dataset proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.
ThermalWORLD-C* is an evaluation set that consists of algorithmically generated corruptions applied to the ThermalWORLD test-set, and especially to both the visible and the thermal data. In comparison with the corruption approach proposed by Chen et al. in "Benchmarks for Corruption Invariant Person Re-identification" paper, our dataset is used in a multimodal manner and do not consider visible data corruptions only. Used corruptions are globally the same; Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. However, corruptions are adapted to respect the thermal modality encoding, and brightness is not used to corrupt the thermal data. Five severity levels are considered per corruption.
This is the first federated quantum dataset in the literature.
There was no predefined dataset of party symbols to be usedas a benchmark. We curated a dataset from various nationaland regional websites owned by the ECI. The dataset consists of symbols (image files) of 49 National and State registered parties approved by the ECI. For each image of theoriginal party symbol, 18 different distortions and transformations were created as variations to the training data. Each image is of the dimension 180 x 180. The final labeled dataset consists of 931 images of party symbols with their corresponding party names as the labels.
Multi-agent pursuit in matrix world (pursuitMW) is a partially observable Markov game (POMG) between a swarm of pursuers and a swarm of evaders. Algorithms can be developed for the pursuers, evaders, or both of them.
This dataset contains more than 700,000 unique voltage vs. capacity curves for training Artificial Intelligence (AI) systems for lithium-ion battery diagnosis and prognosis. It was calculated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis" (Energies, under review) for more details.
LiPC (LiDAR Point Cloud Clustering Benchmark Suite) is a benchmark suite for point cloud clustering algorithms based on open-source software and open datasets. It aims to provide the community with a collection of methods and datasets that are easy to use, comparable, and that experimental results are traceable and reproducible.
EU Long-term Dataset with Multiple Sensors for Autonomous Driving was collected with a robocar, equipped with eleven heterogeneous sensors, in the downtown and suburban areas of Montbéliard in France. The vehicle speed was limited to 50 km/h following the French traffic rules. For the long-term data, the driving distance is about 5.0 km (containing a small and a big road loop for loop-closure purpose) and the length of recorded data is about 16 minutes for each collection round. For the roundabout data, the driving distance is about 4.2 km (containing 10 roundabouts with various sizes) and the length of recorded data is about 12 minutes for each collection round. In addition to enjoying the typical scenery of eastern France, users can also feel the daily and seasonal changes in the city.
L-CAS 3D Point Cloud People Dataset contains 28,002 Velodyne scan frames acquired in one of the main buildings (Minerva Building) of the University of Lincoln, UK. Total length of the recorded data is about 49 minutes. Data were grouped into two classes according to whether the robot was stationary or moving.
We created this robust and custom light field dataset in order to assist light field researchers in using SOTA machine learning algorithms for a variety of light field tasks such as depth estimation, synthetic aperture imaging, and more.
Datasets are built upon three other datasets: DISEC 2013, RVL-CDIP, RDCL 2017. Please respect their LICENSE.
This dataset for Intent classification from human speech covers 14 coarse-grained intents from the Banking domain. This work is inspired by a similar release in the Minds-14 dataset - here, we restrict ourselves to Indian English but with a much larger training set. The data was generated by 11 (Indian English) speakers and recorded over a telephony line. We also provide access to anonymized speaker information - like gender, languages spoken, and native language - to allow more structured discussions around robustness and bias in the models you train.