19,997 machine learning datasets
19,997 dataset results
The datasets includes curves drawn on 3D surfaces (triangle meshes) in Virtual Reality. A total of 2,880 curves were created using two different techniques by 20 users on 6 meshes. For each curve, a 3D curve executed by the user is provided, the projected curve created on the mesh, and the ground truth target curve on the mesh. For collecting the data, two different task types were employed, which are described in the paper.
Dataset of 1,517,419 quantum reaction rate constant products kQM(T)QR(T) computed from the transmission coefficient for model single and double barrier minimum energy paths. Here kQM(T) is the quantum reaction rate constant at temperature T and QR(T) is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict logkQM(T)QR(T).
Authors of the Dataset:
TVRecap a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVRecap contains stories that are authored by professional screenwriters and that feature complex interactions among multiple characters. Generating stories in TVRecap requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, by swapping the input and output, TVRecap can serve as a challenging testbed for abstractive summarization.
HYouTube is a video for Video harmonization, which aims to adjust the foreground of a composite video to make it compatible with the background. The dataset was created by adjusting the foreground of real videos to create synthetic composite videos. It is based on Youtube-VOS
EUEN17037 Daylight and View Standard Test Dataset.
CI-ToD is a dataset for Consistency Identification in Task-oriented Dialog system.
METEOR is a complex traffic dataset which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. METEOR is a unique dataset in terms of capturing the heterogeneity of microscopic and macroscopic traffic characteristics.
This dataset contains anonymized data from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 RT-PCR and additional laboratory tests during a visit to the hospital.
This dataset was created as part of the following study, which was published in the Journal of Hydrology: A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area https://doi.org/10.1016/j.jhydrol.2021.126903. The pre-print is available on arXiv: https://arxiv.org/pdf/2105.05539.pdf
The dataset is taken from the First shared task on Information Extractor for Conversational Systems in Indian Languages (IECSIL) . It consists of 15,48,570 Hindi words in Devanagari script and corresponding NER labels. Each sentence end is marked by \newline" tag. Fig. 1 shows a snapshot of one sentence in the dataset. Our Dataset has nine classes, namely, Datenum, Event, Location, Name, Number, Occupation, Organization, Other, Things.
ChMusic is a traditional Chinese music dataset for training model and performance evaluation of musical instrument recognition. This dataset cover 11 musical instruments, consisting of Erhu, Pipa, Sanxian, Dizi, Suona, Zhuiqin, Zhongruan, Liuqin, Guzheng, Yangqin and Sheng.
TFRD is a dataset to evaluate machine learning modelling methods for theTemperature field reconstruction of heat source systems (TFR-HSS).
CAT is a specialized dataset for co-saliency detection - one of the core tasks in the field of computer vision. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks. CAT consists of 33,500 images
Introduction The FewGLUE_64_labeled dataset is a new version of FewGLUE dataset. It contains a 64-sample training set, a development set (the original SuperGLUE development set), a test set, and an unlabeled set. It is constructed to facilitate the research of few-shot learning for natural language understanding tasks.
VQA-MHUG is a 49-participant dataset of multimodal human gaze on both images and questions during visual question answering (VQA) collected using a high-speed eye tracker.
The Lincolnbeet dataset is an object detection dataset designed to encourage research in the identification of items in environments with high levels of occlusion, and in the development of better approaches to evaluate object detection models in practical scenarios. This dataset was introduced in the paper: "Towards practical object detection for weed spraying in precision agriculture".
JDDC 2.0 is a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform JD.com, containing about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand images, along with product knowledge bases and image category annotations. The dataset is divided into the training set, the validation set, and the test set according to the ratio of 80%, 10%, and 10%.
StoryDB is a broad multi-language dataset of narratives. StoryDB is a corpus of texts that includes stories in 42 different languages. Every language includes 500+ stories. Some of the languages include more than 20 000 stories. Every story is indexed across languages and labeled with tags such as a genre or a topic. The corpus shows rich topical and language variation and can serve as a resource for the study of the role of narrative in natural language processing across various languages including low resource ones.
SCIMAT is a large question-answer dataset for mathematics and science problems; such dataset can have impact on online education, intelligent tutoring and automated grading.