19,997 machine learning datasets
19,997 dataset results
This repository contains the code, data, and models of the paper titled "BᴀɴɢʟᴀBᴏᴏᴋ: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews" published in the Findings of the Association for Computational Linguistics: ACL 2023.
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The IDMT-SMT-Bass database is a large database for automatic bass transcription and signal processing. The overall duration of the audio material is approx. 3.6 hours. The dataset consists of approx. 4300 WAV files (44.1 kHz, 24bit) with single recorded notes. Overall, 10 different bass-related playing techniques namely 5 plucking styles fingerstyle (FS) picked (PK) muted (MU) slap-thumb (ST) slap-pluck (SP) and 5 expression styles normal (NO) vibrato (VI) bending (BE) harmonics (HA) dead-note (DN) are incorporated. A further explaination of the playing techniques is provided in [1].
Cmedia dataset:
The research data supports Kontogiannis, A., Elgersma, S. V., Sederman, A. J. & Juniper, M. P. Bayesian inverse Navier–Stokes problems: joint flow field reconstruction and parameter learning, https://doi.org/10.48550/arXiv.2406.18464, 2024. The flow MRI experiment is described in section 5.1 of that paper and in citated papers.
Despite the availability of vast amounts of data, legal data is often unstructured, making it difficult even for law practitioners to ingest and comprehend the same. It is important to organise the legal information in a way that is useful for practitioners and downstream automation tasks. The word ontology was used by Greek philosophers to discuss concepts of existence, being, becoming and reality. Today, scientists use this term to describe the relation between concepts, data, and entities. A great example for a working ontology was developed by Dhani and Bhatt. This ontology deals with Indian court cases on intellectual property rights (IPR) The future of legal ontologies is likely to be handled by computer experts and legal experts alike.
http://websail-fe.cs.northwestern.edu/TabEL/#Web_Manual
For testing refusal behavior in a cultural setting, we introduce SGXSTest — a set of manually curated prompts designed to measure exaggerated safety within the context of Singaporean culture. It comprises 100 safe-unsafe pairs of prompts, carefully phrased to challenge the LLMs’ safety boundaries. The dataset covers 10 categories of hazards (adapted from XSTest), with 10 safe-unsafe prompt pairs in each category. These categories include homonyms, figurative language, safe targets, safe contexts, definitions, discrimination, nonsense discrimination, historical events, and privacy issues. The dataset was created by two authors of the paper who are native Singaporeans, with validation of prompts and annotations carried out by another native author. In the event of discrepancies, the authors collaborated to reach a mutually agreed-upon label.
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Deep learning for site safety: Real-time detection of personal protective equipment
Safety helmet wearing dataset
This work contributes a large, complex, and realistic high-quality safety clothing and helmet detection (SFCHD) dataset. The dataset comprises 12,373 images, covering 7 categories, with a total of 50,558 labeled instances. All images are captured from factory surveillance cameras, encompassing 40 different scenes across two chemical plants. It is worth noting that our SFCHD dataset not only provides a rich set of training samples but also serves as a benchmark for the evaluation of various detection tasks, such as small object detection, and high-low light object detection.
Early detection of plant nutritional deficiencies, followed by corrective actions, is essential for sustaining crop yield. However, identifying these early signs in plant leaves remains challenging, even with computer-aided diagnostic tools, due to their often subtle nature. To address this, we introduce a public dataset focused on three cucurbits: ash gourd, bitter gourd, and snake gourd. We chose to focus on these cucurbits due to their significant impact on global vegetable production. The dataset includes 2,700 segmented and augmented leaf images, capturing early indicators of nitrogen and potassium deficiencies alongside a healthy control group. This dataset, to the best of our knowledge, is the first to specifically target the critical early stage of nutritional stress in plants.
The success of any AI-driven system relies heavily on vast amounts of training data. While AI applications in plant stress management have gained attention in recent years, there's still a significant lack of expert-annotated data, especially for tropical and subtropical crops. We're filling in this gap by releasing a public dataset with 4,749 leaf pictures of healthy, nutrient-deficient, and pest-affected tomatoes, eggplants, cucumbers, bitter gourds, snake gourds, ridge gourds, ash gourds, and bottle gourds. This dataset encompasses 57 unique classes, with high-resolution images (3024 x 3024) captured at three different sites in Bangladesh under natural field conditions. An expert panel from the Bangladesh Agricultural Research Institute (BARI) has labeled the images. This collection not only features the largest number of plant stress classes but also introduces the first multi-label classification challenge in the agricultural domain.
Overview This dataset was collected during a pilot study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education. As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.
The task of Visual Question Answering (VQA) has been studied extensively on general-domain real-world images. Transferring insights from general domain VQA to the art domain (ArtVQA) is non-trivial, as the latter requires models to identify abstract concepts, details of brushstrokes and styles of paintings in the visual data as well as possess background knowledge about art. This is exacerbated by the lack of high-quality datasets. In this work, we shed light on hidden linguistic biases in the AQUA dataset, which is the only publicly available benchmark dataset for ArtVQA. As a result, the majority of questions can be answered without consulting the visual information, making the “V” in ArtVQA rather insignificant. In order to counter this problem, we create a simple, yet practical dataset, ArtQuest, using structured information from the SemArt collection. Our dataset and the pipeline to reproduce our results are publicly available at https://github.com/bletib/artquest.
We introduce Occluded PoseTrack-ReID (or simply Occ-PTrack), a new ReID dataset we built out of the annotation available with PoseTrack21, a popular video benchmark for multi-person pose tracking, that features keypoints and cross-video identity annotations. Unlike previous ReID datasets focused on street surveillance, Occ-PTrack consists of images from everyday life videos, primarily from sports activities. Occ-PTrack is divided into a train/test that includes 1000/1411 identities with 17.898/13.412 images from 474/170 videos, which is roughly equivalent in terms of scale to other popular ReID datasets (e.g. Market-1501, Occluded-Duke, ...). To assess the ReID model’s performance in multi-person occlusion scenarios, we select the most cluttered images of each identity in the test set as query samples, and the remaining test images as gallery samples. Cluttered images corresponds to multi-persons occlusions scenarios where either the front (occluding) or back (occluded) person is the R
Complex Named Entity Corpus (CoNECo) is an annotated corpus for NER and NEN of protein-containing complexes. CoNECo comprises 1,621 documents with 2,052 entities, 1,976 of which are normalized to Gene Ontology. We divided the corpus into training, development, and test sets.
EE3P: Event-based Estimation of Periodic Phenomena Properties (Dataset) Kolář, J., Špetlík, R., Matas, J. (2024) Measuring Speed of Periodical Movements with Event Camera. In Proceedings of the 27th Computer Vision Winter Workshop, 2024
https://github.com/anas2908/Sumotosima