19,997 machine learning datasets
19,997 dataset results
The MedNLI dataset consists of the sentence pairs developed by Physicians from the Past Medical History section of MIMIC-III clinical notes annotated for Definitely True, Maybe True and Definitely False. The dataset contains 11,232 training, 1,395 development and 1,422 test instances. This provides a natural language inference task (NLI) grounded in the medical history of patients.
Is a collection of action videos from many different countries. The motivation is to create a public dataset that would benefit training and pretraining of action recognition models for everybody, rather than making it useful for limited countries.
AMZ Computers is a co-purchase graph extracted from Amazon, where nodes represent products, edges represent the co-purchased relations of products, and features are bag-of-words vectors extracted from product reviews.
LRW-1000 has been renamed as CAS-VSR-W1k.* It is a naturally-distributed large-scale benchmark for word-level lipreading in the wild, including 1000 classes with about 718,018 video samples from more than 2000 individual speakers. There are more than 1,000,000 Chinese character instances in total. Each class corresponds to the syllables of a Mandarin word which is composed by one or several Chinese characters. This dataset aims to cover a natural variability over different speech modes and imaging conditions to incorporate challenges encountered in practical applications.
Natural Language Inference (NLI), also called Textual Entailment, is an important task in NLP with the goal of determining the inference relationship between a premise p and a hypothesis h. It is a three-class problem, where each pair (p, h) is assigned to one of these classes: "ENTAILMENT" if the hypothesis can be inferred from the premise, "CONTRADICTION" if the hypothesis contradicts the premise, and "NEUTRAL" if none of the above holds. There are large datasets such as SNLI, MNLI, and SciTail for NLI in English, but there are few datasets for poor-data languages like Persian. Persian (Farsi) language is a pluricentric language spoken by around 110 million people in countries like Iran, Afghanistan, and Tajikistan. FarsTail is the first relatively large-scale Persian dataset for NLI task. A total of 10,367 samples are generated from a collection of 3,539 multiple-choice questions. The train, validation, and test portions include 7,266, 1,537, and 1,564 instances, respectively.
PPM is a portrait matting benchmark with the following characteristics:
Mindboggle is a large publicly available dataset of manually labeled brain MRI. It consists of 101 subjects collected from different sites, with cortical meshes varying from 102K to 185K vertices. Each brain surface contains 25 or 31 manually labeled parcels.
Atari-HEAD is a dataset of human actions and eye movements recorded while playing Atari videos games. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded. The gaze data was recorded using an EyeLink 1000 eye tracker at 1000Hz. The human subjects are amateur players who are familiar with the games. The human subjects were only allowed to play for 15 minutes and were required to rest for at least 15 minutes before the next trial. Data was collected from 4 subjects, 16 games, 175 15-minute trials, and a total of 2.97 million frames/demonstrations.
Imagewoof is a subset of 10 dog breed classes from Imagenet. The breeds are: Australian terrier, Border terrier, Samoyed, Beagle, Shih-Tzu, English foxhound, Rhodesian ridgeback, Dingo, Golden retriever, Old English sheepdog.
TUM-GAID (TUM Gait from Audio, Image and Depth) collects 305 subjects performing two walking trajectories in an indoor environment. The first trajectory is traversed from left to right and the second one from right to left. Two recording sessions were performed, one in January, where subjects wore heavy jackets and mostly winter boots, and another one in April, where subjects wore lighter clothes. The action is captured by a Microsoft Kinect sensor which provides a video stream with a resolution of 640×480 pixels and a frame rate around 30 FPS.
Middlebury 2005 is a stereo dataset of indoor scenes.
FAIR-Play is a video-audio dataset consisting of 1,871 video clips and their corresponding binaural audio clips recording in a music room. The video clip and binaural clip of the same index are roughly aligned.
Amazon Mechanical Turk (AMT) is used to collect annotations on HowTo100M videos. 30k 60-second clips are randomly sampled from 9,421 videos and present each clip to the turkers, who are asked to select a video segment containing a single, self-contained scene. After this segment selection step, another group of workers are asked to write descriptions for each displayed segment. Narrations are not provided to the workers to ensure that their written queries are based on visual content only. These final video segments are 10-20 seconds long on average, and the length of queries ranges from 8 to 20 words. From this process, 51,390 queries are collected for 24k 60-second clips from 9,371 videos in HowTo100M, on average 2-3 queries per clip. The video clips and its associated queries are split into 80% train, 10% val and 10% test.
The Japanese-English business conversation corpus, namely Business Scene Dialogue corpus, was constructed in 3 steps:
CQASUMM is a dataset for CQA (Community Question Answering) summarization, constructed from the 4.4 million Yahoo! Answers L6 dataset. The dataset contains ~300k annotated samples.
SketchyScene is a large-scale dataset of scene sketches to advance research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities of realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly scalable and extensible, easily allowing augmenting and/or changing scene composition.
The UCF Sports dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery and GettyImages.
Is an acronym disambiguation (AD) dataset for scientific domain with 62,441 samples which is significantly larger than the previous scientific AD dataset.
The AIRS (Aerial Imagery for Roof Segmentation) dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation.
The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. The dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions.