19,997 machine learning datasets
19,997 dataset results
Official repository for the AnnoMI dataset: the first public collection of expert-annotated MI transcripts.
FMARS is a large-scale dataset of Very High Resolution (VHR) remote sensing images with annotations generated using Vision Foundation Models. The dataset focuses on disaster management applications and provides pre-event imagery and annotations for major crisis events worldwide from 2021 to 2023.
Release for uploading scripts and data to Zenodo
MMPD Dataset is proposed in ECCV'2024 "When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset".
Large Language Models (LLMs) are increasingly used for boosting organizational efficiency and automating tasks. While not originally designed for complex cognitive processes, recent efforts have further extended to employ LLMs in activities such as reasoning, planning, and decision-making. In business processes, such abilities could be invaluable for leveraging on the massive corpora LLMs have been trained on for gaining a deep understanding of such processes. In adherence to this goal, we attach here the BPC dataset, a newly developed set of process-aware Q&A that can be used to assess the ability of LLMs to reason about causal and process perspectives of business operations. We refer to this view as Causally-augmented Business Processes (BP^C). The benchmark comprises a set of domain-specific BPC related situations, a set of questions about these situations, and a set of ground truth answers to these questions. Reasoning on BP^C is of crucial importance for process interventions and
Introduction to the Needle In A Haystack Test The Needle In A Haystack test, inspired by NeedleInAHaystack, is an evaluation method that randomly inserts key information into long texts to create prompts for large language models (LLMs). This test aims to determine whether LLMs can effectively extract key information from extensive texts, thereby assessing their capabilities in processing and understanding long documents.
This is the in-ear rigid earpiece-embedded microphone variant of the VibraVox dataset.
The LoRA Weight Size Evaluation (LoRA-WiSE) is a comprehensive benchmark specifically designed to evaluate LoRA dataset size recovery methods for generative models LoRA-WiSE spans various dataset sizes, backbones, ranks, and personalization sets, as presented in the "Dataset Size Recovery from LoRA Weights"
This is the in-ear comply foam-embedded microphone variant of the VibraVox dataset.
This is the throat microphone (laryngophone) variant of the VibraVox dataset.
This is the forehead accelerometer variant of the VibraVox dataset.
This is the temple vibration pickup variant of the VibraVox dataset.
This is the reference headset microphone variant of the VibraVox dataset.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
The presented dataset contains 10,000 Jupyter notebooks, each of which contains at least one error. In addition to the notebook content, the dataset also provides information about the repository where the notebook is stored.
THRawS is a new dataset of raw Sentinel-2 (S-2) satellite data containing warm temperature hotspots such as wildfires and volcanic eruptions from around the world. The dataset aims to promote the development of energy-efficient pre-processing algorithms and AI models for onboard-satellite applications. A custom methodology was designed to identify events in raw data using corresponding Level-1C (L1C) products and a lightweight coarse coregistration and georeferencing strategy was employed to deal with unprocessed data. The dataset comprises over 100 samples, including wildfire, volcanic eruption, and event-free volcanic areas, to enable warm-events detection and general classification applications. Finally, the performances of the proposed coarse spatial coregistration technique and the SuperGlue Deep Neural Network method were compared to highlight different constraints in terms of timing and quality of spatial registration to minimize spatial displacement error for a specific scene.
Microscopy is a cornerstone of biomedical research, enabling detailed study of biological structures at multiple scales. Advances in cryo-electron microscopy, high-throughput fluorescence microscopy, and whole-slide imaging allow the rapid generation of terabytes of image data, which are essential for fields such as cell biology, biomedical research, and pathology. These data span multiple scales, allowing researchers to examine atomic/molecular, subcellular/cellular, and cell/tissue-level structures with high precision. A crucial first step in microscopy analysis is interpreting and reasoning about the significance of image findings. This requires domain expertise and comprehensive knowledge of biology, normal/abnormal states, and the capabilities and limitations of microscopy techniques. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers’ efficiency, identifying new image biomarkers, and accelerating hypothesis ge
This repository has a review-level multidomain multilingual dataset for Aspect-based Sentiment Analysis(ABSA) for the paper ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection.
In our work, we have designed and implemented a novel workflow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits. Our dataset containing 26,617 unique CVEs coming from 6,945 unique GitHub projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. These CVEs are associated with 31,883 unique commits that fixed those vulnerabilities. Compared to prior work, our dataset brings about a 397% increase in CVEs, a 295% increase in covered open-source projects, and a 480% increase in commit fixes. Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We used N
The TXL-PBC Dataset is a comprehensive collection of re-annotated and integrated cell images from multiple cell datasets. The main objective of this study is to perform sample reduction, re-labeling, and integration from BCCD and BCD datasets. Then, the original dataset is integrated with two new cell datasets, PBC dataset Peripheral Blood Cells and Raabin-WBC dataset Raabin White Blood Cells, to create a high-quality, sample balanced new dataset. We call it TXL-PBC dataset. We use the Labelimg tool Semi-automated labeling is performed using YOLOv8n.to annotate all the datasets. It is specifically designed for evaluating various object detection models, especially those that use the YOLO format.