19,997 machine learning datasets
19,997 dataset results
BERSt Dataset
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Smartphone cameras are ubiquitous in daily life, yet their performance can be severely impacted by dirty lenses, leading to degraded image quality. This issue is often overlooked in image restoration research, which assumes ideal or controlled lens conditions. To address this gap, we introduced SIDL (Smartphone Images with Dirty Lenses), a novel dataset designed to restore images captured through contaminated smartphone lenses. SIDL contains diverse real-world images taken under various lighting conditions and environments. These images feature a wide range of lens contaminants, including water drops, fingerprints, and dust. Each contaminated image is paired with a clean reference image, enabling supervised learning approaches for restoration tasks. To evaluate the challenge posed by SIDL, various state-of-the-art restoration models were trained and compared on this dataset. Their performances achieved some level of restoration but did not adequately address the diverse and reali
One of the central tasks in software maintenance is being able to understand and develop code changes. Thus, given a natural language description of the desired new operation of a function, an agent (human or AI) might be asked to generate the set of edits to that function to implement the desired new operation; likewise, given a set of edits to a function, an agent might be asked to generate a changed description, of that function's new workings. Thus, there is an incentive to train a neural model for change-related tasks. Motivated by this, we offer a new, "natural", large dataset of coupled changes to code and documentation mined from actual high-quality GitHub projects, where each sample represents a single commit where the code and the associated docstring were changed together. We present the methodology for gathering the dataset, and some sample, challenging (but realistic) tasks where our dataset provides opportunities for both learning and evaluation. We find that current mode
ATC-GRAPH is the most extensive ATC benchmark dataset. All drugs in the benchmarks are linked to their Mol files instead of the SMILES sequences utilized in earlier benchmarks. This shift allows for more precise and detailed modeling and learning. In terms of scale, ATC-GRAPH surpasses Chen-2012 and ATC-SMILES by 36.78% and 16.85%, respectively. Significantly, ATC-GRAPH was curated through a cross-validation process involving multiple resources such as KEGG, PubChem, ChEMBL, ChemSpider, and ChemicalBook. This results in ATC-GRAPH being distinguished by its timeliness and comprehensive coverage across all five levels and drug genres.
Existing databases usually record posed or induced human behavior in individual or dyadic settings with biased annotations in which a basic emotional class label or a Valence–Arousal pair value represents the emotional states. To address this need,We created the “Werewolf-XL” database, which contains a total of 987 h of spontaneous audio-visual recordings of 129 subjects in a group interaction of nine individuals playing a conversational role-playing game called Werewolf. We provide 133,461 individual utterance-level video clips with internal (18 non-prototypical emotional categories) and external (pleasure, arousal, and dominance) emotion annotations including 14,635 samples from speaking players. Besides, the results of annotation agreement analysis show fair reliability and validity. Furthermore, we provided extensive benchmarks of unimodal and multimodal emotional recognition results. The database (the video clips, annotations, features and protocols) is made publicly available, wh
Daily Activity Recordings for Artificial Intelligence (DARai, pronounced "Dahr-ree") is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different environments, totaling over 200 hours of data from 20 sensors including multiple camera views, depth and radar sensors, wearable inertial measurement units (IMUs), electromyography (EMG), insole pressure sensors, biomonitor sensors, and gaze tracker. To capture the complexity in human activities, DARai is annotated at three levels of hierarchy: (i) high-level activities (L1) that are independent tasks, (ii) lower-level actions (L2) that are patterns shared between activities, and (iii) fine-grained procedures (L3) that detail the exact execution steps for actions. The dataset annotations and recordings are designed so that 22.7% of L2 actions are shared between L1 activities and 14.2% of L3
Structured atmospheric data for AI/ML Long-term, pre-processed, atmospheric datasets for use in Machine Learning/AI based forecasting. Initially intended to predict AOD, however can be adapted for prediction of other atmospheric particles.
mwBTFreddy dataset is a resource developed to support flash flood damage assessment in urban Malawi, specifically focusing on the impacts of Cyclone Freddy in 2023. The dataset comprises paired pre- and post-disaster satellite images sourced from Google Earth Pro, ac- companied by JSON files containing labelled building annotations with geographic coordinates and damage levels (no damage, minor, major, or destroyed). Developed by the Kuyesera AI Lab at the Malawi University of Business and Applied Sciences, this dataset is intended to facilitate the develop- ment of machine learning models tailored to building detection and damage classification in African urban contexts. It also supports flood damage visualisation and spatial analysis to inform decisions on relocation, infrastructure planning, and emergency response in climate-vulnerable regions.
This dataset builds upon the SpaGBOL dataset - a graph-based dataset covering numerous cities across the globe for the purpose of structured city-scale Cross-View Geo-Localisation (CVGL).
The Watch Your Mouth dataset is a custom silent speech dataset consisting of depth-only recordings of users silently mouthing full English sentences, captured using consumer-grade depth cameras such as the iPhone TrueDepth sensor. Sentences were carefully curated to cover diverse visemic and phonetic patterns, supporting the development of models capable of generalizing across varied speech content. Each sentence-level utterance provides a temporally aligned depth sequence and corresponding ground truth text. Please see more details in the paper Watch Your Mouth: Silent Speech Recognition with Depth Sensing.
This dataset consists of RGB-D images captured using 12 Intel RealSense cameras. Each image depicts a calibration cube placed in various positions and orientations within a controlled environment. The dataset is specifically designed for evaluating and benchmarking multi-view RGB-D camera calibration.
GPR‑bench is an open‑source, multilingual benchmark for regression testing and reproducibility tracking in generative‑AI systems. It provides a compact yet diverse suite of prompts and reference outputs that let you verify whether a model (or prompt) change alters output quality in undesirable ways.
This is the C++ dataset used in the TASTY research paper which was published at the ICLR DL4Code (Deep Learning for Code) workshop.
The AneuX morphology database includes data from 3 different data sources: AneuX, @neurIST and Aneurisk. The AneuX data consists of two portions AneuX1 and AneuX2, which have extracted by two different teams of data curators.
CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems.
CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems.
CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems.
This dataset contains over 47,000 LEGO structures of over 28,000 unique 3D objects accompanied by detailed captions. It was used to train LegoGPT, the first approach for generating physically stable LEGO brick models from text prompts.
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