19,997 machine learning datasets
19,997 dataset results
ALFI (Annotations for Label-Free Images) is a dataset of images and annotations for label-free microscopy imaging. It consists of 29 time-lapse image sequences with various annotations (pixel-wise segmentation masks, object-wise bounding boxes, and tracking information), made publicly available to the scientific community through figshare.
Numbers Station Text to SQL
The purpose of this challenge is to investigate (semi-)automatic spinal curvature estimation algorithms. Participant will have to submit results of Cobb angle for all the test data.
Dataset for the DREAMING - Diminished Reality for Emerging Applications in Medicine through Inpainting Challenge!
Welcome to L-SVD L-SVD is an extensive and rigorously curated video dataset aimed at transforming the field of emotion recognition. This dataset features more than 20,000 short video clips, each carefully annotated to represent a range of human emotions. L-SVD stands at the intersection of Cognitive Science, Psychology, Computer Science, and Medical Science, providing a unique tool for both research and application in these fields.
The CTV-Dataset (CTV stands for Cyclist Top-View) is a trajectories dataset for cyclist behaviour in mixed-traffic environments (aka. shared spaces). This dataset is meant to enlarge the available datasets in the community, focusing on cyclists as main road users to help the research in understanding and predicting cyclist behaviour in shared spaces. The dataset results from an experiment conducted in TU Clausthal to extract data from possible interaction scenarios with other road users, such as pedestrians and cars, in shared spaces. The scenarios were captured using a drone with 4K (3840×2160) resolution at 29.97 fps to ensure high-quality results. The trajectories were extracted using an in-house developed computer vision algorithm.
This is the dataset used in the 2nd data challenge on Learning to Quantify. It is designed for the comparative evaluation of methods for “learning to quantify” in textual datasets, i.e., methods for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. These predictors (called “quantifiers”) are required to issue predictions for several such sets, some of them characterized by class frequencies radically different from the ones of the training set.
The ShapeNoiseHorseBird dataset is a curated collection designed to challenge shape recognition models with varying levels of noise. It integrates samples from the Weizmann Horse and Caltech-UCSD Birds 200 datasets, diversifying the challenges presented to models. The introduced noise contains Salt and pepper noise, Circle Noise, Real image Noise, Occlusion noise, Thresholded probability noise and Detection image Noise, creating a robust benchmark for evaluating the resilience of shape-based recognition algorithms. Researchers can leverage ShapeNoise to assess the adaptability of their models to scenarios where shape perception is crucial.
This dataset collects transparency disclosures about the sexual exploitation of children by social media and their reports about such activity and material to the national clearinghouse, the National Center for Missing and Exploited Children (NCMEC).
The data was captured from an overhead perspective, showcasing the swimming behavior of fish in a simulated flowing water channel. This angle provides a panoramic view from above to observe the water channel and the fish behavior. It enables researchers to better observe and analyze fish swimming patterns, group behavior, and their adaptive abilities to water dynamics. Moreover, the overhead perspective offers more accurate spatial positioning and motion tracking, providing valuable data for studying fish behavior and ecology. By observing and analyzing this data, a deeper understanding of fish ecological adaptability, migration patterns, and interactions with environmental factors in simulated flowing water channels can be gained. This knowledge serves as a scientific basis and decision support for areas such as aquaculture, ecological conservation, and hydraulic research. E-mail: peifei122@gmail.com
Objective This study introduces the BlendedICU dataset, a massive dataset of international intensive care data. This dataset aims to facilitate generalizability studies of machine learning models, as well as statistical studies of clinical practices in the intensive care units.
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The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease
A high-quality, balanced dataset of 330,000 images featuring various types of Chinese license plates. The dataset is generated using Generative Adversarial Networks (GANs), ensuring excellent image quality and a balanced distribution of different license plate types. This dataset is perfect for training and evaluating license plate recognition models.
ABandoned Objects DAtaset (ABODA) is a new public dataset for abandoned object detection. ABODA comprises 11 sequences labeled with various real-application scenarios that are challenging for abandoned-object detection. The situations include crowded scenes, marked changes in lighting condition, night-time detection, as well as indoor and outdoor environments.
The ENEM dataset refers to data collected from the Brazilian High School National Exam (ENEM). Let's explore more about it:
Description The Czy wiesz? (eng. Did you know?) the dataset consists of almost 5k question-answer pairs obtained from Czy wiesz... section of Polish Wikipedia. Each question is written by a Wikipedia collaborator and is answered with a link to a relevant Wikipedia article. In huggingface version of this dataset, they chose the negatives which have the largest token overlap with a question.
The Polish Cyberbullying Dataset is a valuable resource for studying harmful online phenomena, specifically cyberbullying and hate speech in the Polish language. Let's delve into the details:
The NKJP-NER dataset is based on a human-annotated part of the National Corpus of Polish (NKJP). In this dataset, sentences containing named entities of exactly one type have been extracted. The primary task associated with this dataset is to predict the type of the named entity. The dataset provides examples split into three categories:
Crossref is an essential organization in the scholarly publishing domain. It plays a crucial role in facilitating the discovery and linking of scholarly content. Let's delve into the details: