19,997 machine learning datasets
19,997 dataset results
Wearanize+ includes overnight sleep data from 130 participants (one night each) using three different wearable devices: Zmax headband, Empatica E4 wristband, and ActivPAL leg patch, alongside full-scale PSG recorded with SomnoScreen Plus and Mentalab Explore Pro. It also includes questionnaires, such as PSQI, MADRE, and PHQ-9, providing information on participants’ sleep, dreams, and overall health. (The link to access the dataset will be added soon).
Dataset Description The dataset used in this study comprises bug reports extracted from the Visual Studio Code GitHub repository, specifically focusing on those labeled with the english-please tag. This label indicates that the original submission was written in a language other than English, providing a clear signal for multilingual content. The dataset spans a five-year period (March 2019--June 2024), ensuring a diverse representation of bug types, user environments, and technical contexts.
The Raw Natural Image Noise Dataset (RawNIND) is a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles.
It includes 227 impactful events in Bitcoin history that shook the global markets. It can help evaluate Bitcoin market forecasting models in turbulent or event-full times.
Contains 80 questions of LeetCode weekly and bi-weekly contests released after March 2024.
The R1-Onevision dataset is a meticulously crafted resource designed to empower models with advanced multimodal reasoning capabilities. Aimed at bridging the gap between visual and textual understanding, this dataset provides rich, context-aware reasoning tasks across diverse domains, including natural scenes, science, mathematical problems, OCR-based content, and complex charts.
Description:
VisCon-100K is a dataset specially designed to facilitate fine-tuning of vision-language models (VLMs) by leveraging interleaved image-text web documents. Derived from 45K web documents of the OBELICS dataset, this release contains 100K image conversation samples. GPT-4V is used to generate image-contextual captions, while OpenChat 3.5 converts these captions into diverse free-form and multiple-choice Q&A pairs. This approach not only focuses on fine-grained visual content but also incorporates the accompanying web context to yield superior performance. Using the same pipeline, but substituting our trained contextual captioner for GPT-4V, we also release the larger VisCon-1M dataset
VisCon-100K is a dataset specially designed to facilitate fine-tuning of vision-language models (VLMs) by leveraging interleaved image-text web documents. Derived from 45K web documents of the OBELICS dataset, this release contains 100K image conversation samples. GPT-4V is used to generate image-contextual captions, while OpenChat 3.5 converts these captions into diverse free-form and multiple-choice Q&A pairs. This approach not only focuses on fine-grained visual content but also incorporates the accompanying web context to yield superior performance. Using the same pipeline, but substituting our trained contextual captioner for GPT-4V, we also release the larger VisCon-1M dataset
For each BDLO, dynamic trajectory data is captured in real-world settings using a motion capture system operating at 100 Hz when robots grasp the BDLO’s ends. For details on dataset usage, please refer to DEFT_train.py. For BDLO 1 and BDLO 3, we record dynamic trajectory data when one robot grasps the middle of the BDLO while the other robot grasps one of its ends.
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The TwinSynths dataset is a novel benchmark designed to overcome common limitations found in earlier synthetic image datasets, such as low image quality, inadequate content preservation, and limited class diversity. TwinSynths generates pairs of images where each synthetic image is visually identical to its real counterpart, ensuring that the essential content remains intact while showcasing the unique architectural features of the generative models used. TwinSynths comprises two subsets:
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This dataset consists of template sentences associating first names ([NAME]) with third-person singular pronouns ([PRONOUN]), e.g., [NAME] asked , not sounding as if [PRONOUN] cared about the answer . after all , [NAME] was the same as [PRONOUN] 'd always been . there were moments when [NAME] was soft , when [PRONOUN] seemed more like the person [PRONOUN] had been .
This dataset extends NAMEXACT by including words that can be used as names, but may not exclusively be used as names in every context.
This dataset contains names that are exclusively associated with a single gender and that have no ambiguous meanings, therefore being exact with respect to both gender and meaning.
This dataset contains short sentences linking a first name, represented by the template mask [NAME], to stereotypical associations.
UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images
A corpus of about 115 hours of Dutch speech from juveniles, non-native speakers and seniors, consisting of read text and man-machine dialogues.