19,997 machine learning datasets
19,997 dataset results
The MAGE dataset provides a large set of generated texts using 27 LLMs from seven different groups: OpenAI GPT, LLaMA, GLM130B, FLAN-T5, OPT, BigScience, and EleutherAI. In total, the dataset contains 432,682 texts, along with two additional sets. The first is an additional test set with texts from unseen domains generated by an unseen model, namely GPT-4. The second set is designed to evaluate the robustness of detectors against paraphrasing attacks. To achieve this, the GPT-3.5-turbo model was employed to paraphrase the sentences from the first set, with all paraphrased texts treated as machine-generated.
Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional r
MULTITQ is a large-scale dataset featuring ample relevant facts and multiple temporal granularities.
Existing benchmarks for temporal QA focus on a single information source (either a KB or a text corpus), and include only few questions with implicit constraints. we devise a new method for automatically creating temporal questions with implicit constraints, with systematic controllability of different aspects, including the relative importance of different source types (text, infoboxes, KB), fractions of prominent vs. long-tail entities, question complexity, and more.
AMPS contains over 100,000 problems pulled from Khan Academy and approximately 5 million problems generated from manually designed Mathematica scripts.
The RoboTAP dataset follows the same annotation format as TAP-Vid, but is released as an addition to TAP-Vid. In terms of domain, RoboTAP dataset is mostly similar to TAP-Vid-RGB-Stacking, with a key difference that all robotics videos are real and manually annotated. Video sources and object categories are also more diversified. The benchmark dataset includes 265 videos, serving for evaluation purpose only.
This is the home of a collaborative data collection effort by U. Chicago and TTI-Chicago researchers. This is to our knowledge the first collection of American Sign Language fingerspelling data "in the wild," that is in naturally occurring (online) video.
This is the home of a collaborative data collection effort by U. Chicago and TTI-Chicago researchers. This is to our knowledge the first collection of American Sign Language fingerspelling data "in the wild," that is in naturally occurring (online) video. The collection consists of two data set releases, ChicagoFSWild and ChicagoFSWild+.
FM-IQA is a question-answering dataset containing over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations.
SK-LARGE is a benchmark dataset for object skeleton detection, built on the MS COCO dataset. It contains 1491 images, 746 for training and 745 for testing.
The CommitmentBank is a corpus of 1,200 naturally occurring discourses whose final sentence contains a clause-embedding predicate under an entailment canceling operator (question, modal, negation, antecedent of conditional).
The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. The PH² dataset has been developed for research and benchmarking purposes, in order to facilitate comparative studies on both segmentation and classification algorithms of dermoscopic images. PH² is a dermoscopic image database acquired at the Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.
The Chinese judicial reading comprehension (CJRC) dataset contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts.
DiscoFuse was created by applying a rule-based splitting method on two corpora - sports articles crawled from the Web, and Wikipedia. See the paper for a detailed description of the dataset generation process and evaluation.
A binarized version of MNIST.
DataViSal.rar (including the ground truth data) is our new collected dataset for the following paper.
UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.
Office-Caltech-10 a standard benchmark for domain adaptation, which consists of Office 10 and Caltech 10 datasets. It contains the 10 overlapping categories between the Office dataset and Caltech256 dataset. SURF BoW historgram features, vector quantized to 800 dimensions are also available for this dataset.
This relational database consists of 24 unique names in two families (they have equivalent structures).
Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes.