19,997 machine learning datasets
19,997 dataset results
MindReader is a novel dataset providing explicit user ratings over a knowledge graph within the movie domain. The latest stable version of the dataset contains 218,794 ratings from 2,316 users over 12,206 entities entities, and an associated knowledge graph consisting of 18,133 movie-related entities. The dataset is collected from an online movie recommendation game, MindReader, where users are pseudo-randomly asked to provide preferences for both movie- and non-movie entities (e.g., genres, actors, and directors). For each entity, users can either like it, dislike it, or state that they do not know it.
Imagine one day, your breathing became consistently labored and shallow. Months later you were finally diagnosed with pulmonary fibrosis, a disorder with no known cause and no known cure, created by scarring of the lungs. If that happened to you, you would want to know your prognosis. That’s where a troubling disease becomes frightening for the patient: outcomes can range from long-term stability to rapid deterioration, but doctors aren’t easily able to tell where an individual may fall on that spectrum. Your help, and data science, may be able to aid in this prediction, which would dramatically help both patients and clinicians.
A public open dataset of synthetic chest X-ray images of COVID-19.
We obtain A2Dre by selecting only instances that were labeled as non-trivial, which are 433 REs from 190 videos. We do not use the trivial cases as the analysis of such examples is not relevant, as referents can be described by using the category alone. Each annotator was presented with a RE, a video in which the target object was marked by a bounding box, and a set of questions paraphrasing our categories. A2Dre was annotated by 3 authors of the paper. Our final set of category annotations used for analysis was derived by means of majority voting: for each nontrivial RE, we kept all category labels which were assigned to the RE by at least two annotators.
A2Dre is a subset from the A2D test set including $433$~\textit{non-trivial} REs. Due to its highly unbalanced distribution across the $7$~semantic categories we select the $4$~major categories \textsl{appearance, location, motion and static}. The four categories have in common that in most cases, for a given referent, a RE can be provided that expresses a certain category, and one that does not. We use these categories to augment A2Dre with additional REs, which vary according to the presence or absence of each of them. Specifically, based on our categorization of the original REs, for each RE~$re$ and category~$C$, we produce an additional RE~$re'$ by modifying $re$ slightly such that it does (or does not) express~$C$. For example, for the last RE in Figure~\ref{fig:a2d-images}, i.e. \emph{girl in yellow dress standing near the woman}, which could be categorized as \textit{appearance}, \textit{location}, no \textit{motion} and \textit{static}, we produce new REs for each category:
RaindropsOnWindshield is a dataset for training and assessing vision algorithms' performance for different tasks of image artifacts detection on either camera lens or windshield. The dataset contains 8190 images, of which 3390 contain raindrops. Images are annotated with the binary mask representing areas with raindrops.
In this paper, we develop a new privacy enhancing tool: ML-CB—a means of using distinguishable pictorial information combined with underlying website source code to produce accurate and robust machine learning classifiers able to discern fingerprinting (i.e., surreptitious tracking) from non-fingerprinting canvas-based actions.
hERG is a large-scale biophysics federated molecular dataset related to cardiac toxicity. It consists of 10,572 compounds, with an average of 29.39 nodes and 94.09 edges in each graph.
The information contained in JUSThink Dialogue and Actions Corpus dataset includes dialogue transcripts, event logs, and test responses of children aged 9 through 12, as they participate in a robot-mediated human-human collaborative learning activity named JUSThink, where children in teams of two solve a problem on graphs together.
The NISQA Corpus includes more than 14,000 speech samples with simulated (e.g. codecs, packet-loss, background noise) and live (e.g. mobile phone, Zoom, Skype, WhatsApp) conditions. Each file is labelled with subjective ratings of the overall quality and the quality dimensions Noisiness, Coloration, Discontinuity, and Loudness. In total, it contains more than 97,000 human ratings for each of the dimensions and the overall MOS.
This dataset contains general and named entities annotations on both clean written text and on noisy speech data. It includes 1000 sentences from Wikipedia and 1000 sentences of speech data that appear in two forms: (1) transcribed manually, and (2) the output of an ASR engine. Each of the datasets includes a total of around 6500 mentions linked to there DBPedia pages.
The Inpainting dataset consists of synchronized Labeled image and LiDAR scanned point clouds. It's captured by HESAI Pandora All-in-One Sensing Kit. It is collected under various lighting conditions and traffic densities in Beijing, China.
Co/FeMn bilayers measured.
This is a video and image segmentation dataset for human head and shoulders, relevant for creating elegant media for videoconferencing and virtual reality applications. The source data includes ten online conference-style green screen videos. The authors extracted 3600 frames from the videos and generated the ground truth masks for each character in the video, and then applied virtual background to the frames to generate the training/testing sets.
HumanMT is a collection of human ratings and corrections of machine translations. It consists of two parts: The first part contains five-point and pairwise sentence-level ratings, the second part contains error markings and corrections. Details are described in the following.
This data adds textual meta-infomation data to two existing corpora for cross language information retrieval: BoostCLIR, and the Large Scale CLIR Dataset (wiki-clir).
WiTA (Writing in The Air) is a dataset for the challenging writing in the air (WiTA) task -- an elaborate task bridging vision and NLP. The dataset consists of five sub-datasets in two languages (Korean and English) and amounts to 209,926 video instances from 122 participants. Finger movement for WiTA is captured with RGB cameras to ensure wide accessibility and cost-efficiency.
CPM-Synt-1 is a dataset consisting of 5555 images with synthesis - makeup images with pattern segmentation mask
CPM-Synt-2 is a dataset consisting of 1625 images with synthesis - triplets: makeup, non-makeup, ground-truth.
Stickers is a dataset consisting of 577 high-quality sticker images with alpha channel.