19,997 machine learning datasets
19,997 dataset results
Synthinel-1 is a collection of synthetic overhead imagery with full pixel-wise building segmentation labels.
A morpho-syntactically annotated Tunisian Arabish Corpus (TArC).
The TCG dataset is used to evaluate Traffic Control Gesture recognition for autonomous driving. The dataset is based on 3D body skeleton input to perform traffic control gesture classification on every time step. The dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence.
A large collection of interaction-rich video data which are annotated and analyzed.
TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging.
Contains 140 videos with multiple human created summaries, which were acquired in a controlled experiment.
A video dataset for recognising traffic signs hosted with the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society.
Specifically designed for the evaluation of change point detection algorithms, consisting of 37 time series from various domains.
Includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL, the age, the gender, and so on.
Consists of around 30 hours of video, with contents ranging from subtle signs of drowsiness to more obvious ones.
Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class.
A challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamese text. This corpus includes 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders. Answers may be extracted from the contents of single or multiple sentences in the corresponding reading text.
A large-scale dataset for multi-domain aspect-based summarization that attempts to spur research in the direction of open-domain aspect-based summarization.
The WikiSem500 dataset contains around 500 per-language cluster groups for English, Spanish, German, Chinese, and Japanese (a total of 13,314 test cases).
Synthetic training dataset of 50,000 depth images and 320,000 object masks using simulated heaps of 3D CAD models.
XED is a multilingual fine-grained emotion dataset. The dataset consists of human-annotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages.
A large-scale dataset built on questions from TyDi QA lacking same-language answers.
SRL is the task of extracting semantic predicate-argument structures from sentences. X-SRL is a multilingual parallel Semantic Role Labelling (SRL) corpus for English (EN), German (DE), French (FR) and Spanish (ES) that is based on English gold annotations and shares the same labelling scheme across languages.
Verse is a new dataset that augments existing multimodal datasets (COCO and TUHOI) with sense labels.
The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones. The images are collected from roads in India, Japan and the Czech Republic.