19,997 machine learning datasets
19,997 dataset results
This dataset is developed to estimate bearing loads under various operating conditions (rotational speed, axial and radial loads) using data from temperature and vibration sensors. These sensor modalities provide complementary information: vibration signals indicate the magnitude of the load, while temperature measurements reveal the spatial distribution of the load within the bearing. The dataset emulates a real-world deployment scenario of a virtual sensor, mirroring scenarios where a physical sensor's operational life is limited, such as when a sensor roller collecting field data experiences premature battery depletion. It contains 55 unique operating conditions, defined by axial load (Fx), radial load (Fy), and rotational speed.
Enriched Voxceleb speakers' data of 1,715 celebrities with height gathered from Wikidata
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Votranh DREAM_LOG is a poetic, philosophical dataset generated by the self-evolving AI system Votranh V8. Rather than typical NLP benchmarks, this dataset contains narrative dreams, emotional reflections, and meditative monologues written autonomously by the AI.
The 2007 BP TTI Velocity-Analysis Benchmark dataset was created by Hemang Shah and provided courtesy of BP Exploration Operation Company Limited ("BP"). It was originally used at the velocity-analysis workshop at the 2010 EAGE meeting in Barcelona, Spain.
A dataset containing four sets of playing card images. Each set contains 10,000 images and has a series of attributes. Cards are randomly rotated, flipped and scaled (within limits).
This split was introduced in TEMP (BMVC 2023)
Source code to obfuscated code dataset in C, C++, Go, Java, Python, Rust and TypeScript.
LLaVA-Rad MIMIC-CXR features more accurate section extractions from MIMIC-CXR free-text radiology reports. Traditionally, rule-based methods were used to extract sections such as the reason for exam, findings, and impression. However, these approaches often fail due to inconsistencies in report structure and clinical language. In this work, we leverage GPT-4 to extract these sections more reliably, adding 237,073 image-text pairs to the training split and 1,952 pairs to the validation split. This enhancement afforded the development and fine-tuning of LLaVA-Rad, a multimodal large language model (LLM) tailored for radiology applications, achieving improved performance on report generation tasks.
The CASTLE Benchmark is a comprehensive dataset and a scoring method for evaluating single or combinations of static analyzers with a focus on security. It consists of a hand-crafted dataset of 250 micro-benchmark programs (almost 11,000 lines of C code), covering 25 common CWEs. We also introduce the novel CASTLE Score metric to enable fair and reliable comparisons, considering factors such as true positive and false positive rates, as well as the tools' ability to find more common issues. This dataset enables a comparison of single tools, as well as the effectiveness of tool combinations.
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The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types.
There are 537 RGB jpg images of cracks and corresponding png binary segmentation masks of crack: a training set with 300 images and a testing set with 237 images. All of the images are of a fixed size of 544 × 384 pixels. Images diversity: 1. asphalt 22%, concrete 78% 2. dirty 22.4%, rough 40%, bare 37,6% 3 The width of cracks is in ranges from 1 pixel to 180 ones in the database.
Novel benchmark adapted from the MedQA, with confounding statements introduced within the question regarding an irrelevant clinical term used in a nonclinical context (e.g., The patient's Zodiac sign is Cancer).
Novel benchmark adapted from the MedQA, with confounding statements introduced within the question regarding an irrelevant third party.
Alex-20: contains ~1.3M general inorganic materials curated from the Alexandria database, with energy above the convex hull less than 0.1 eV/atom and no more than 20 atoms in unit cell.
SUDO is a benchmark of 50 real-world malicious tasks designed to evaluate LLM-based computer agents in live desktop and web environments. It covers critical risk domains—including system security, content safety, societal harms, and privacy violations—based on the AirBench taxonomy. The dataset supports fine-grained evaluation using task-specific checklists and can be used to assess model misuse potential, build safer agents, or guide alignment research.
The Building TimeSeries (BTS) dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardised in the formed of knowledge graph using the Brick schema.
AnnoPage Dataset contains pages of mostly historical documents and annotations of non-textual objects, such as images, diagrams, symbols, initials, etc. The dataset contains data mostly from czech written documents downloaded from Digital Library, but it also contains annotations for pages from other already published datasets which are listed below together with links. The provided ZIP archive includes a text file referencing the pages from these datasets. The complete list of the annotated object categories is also listed below. The dataset contains a split into training and testing subsets, the creation of the validation subset is up to the users.
We compiled a new dataset (the PERO layout dataset) that contains 683 images from various sources and historical periods with complete manual text block, text line polygon and baseline annotations. The included documents range from handwritten letters to historic printed books and newspapers and contain various languages including Arabic and Russian. Part of the PERO dataset was collected from existing datasets and extended with additional layout annotations (cBAD, IMPACT and BADAM). The dataset is split into 456 training and 227 testing images.