19,997 machine learning datasets
19,997 dataset results
We provide video observations of humans performing two simple tasks in natural environments. The tasks are pushing and drawer opening.
FEAFA+ is a dataset for Facial expression analysis and 3D Facial animation. It includes 150 video sequences from FEAFA and DISFA, with a total of 230,184 frames being manually annotated on floating-point intensity value of 24 redefined AUs using the Expression Quantitative Tool.
Subset of AdobeVFR. The dataset contains images depicting English text and consists of 1000 synthetic images for training and 100 for testing, for each of 2383 font classes. The training and test sets are called VFR_syn_train and VFR_syn_val, respectively.
Subset of AdobeVFR. The dataset contains "real-world text images".
This dataset is derived from the Stack Overflow Data hosted by kaggle.com and available to query through Kernels using the BigQuery API: https://www.kaggle.com/stackoverflow/stackoverflow
An open, broad-coverage corpus for informal Persian named entity recognition was collected from Twitter.
Emotional Dialogue Acts data contains dialogue act labels for existing emotion multi-modal conversational datasets. We chose two popular multimodal emotion datasets: Multimodal EmotionLines Dataset (MELD) and Interactive Emotional dyadic MOtion CAPture database (IEMOCAP). EDAs reveal associations between dialogue acts and emotional states in a natural-conversational language such as Accept/Agree dialogue acts often occur with the Joy emotion, Apology with Sadness, and Thanking with Joy.
This repository contains gzipped files containing more than 2 million tokens (words) from answers submitted by more than 6,000 students over the course of their first 30 days of using Duolingo. It also contains baseline starter code written in Python. There are three data sets, corresponding to three different language courses. More details on the data set and task are available at: http://sharedtask.duolingo.com. (2018-01-10)
This is a gzipped CSV file containing the 13 million Duolingo student learning traces used in experiments by Settles & Meeder (2016). For more details and replication source code, visit: https://github.com/duolingo/halflife-regression (2016-06-07)
Robotic Interestingness dataset was created to promote the development visual interesting scene prediction for such purpose, for robots to better sense the world.
CoVaxLies v1 includes 17 known Misinformation Targets (MisTs) found on Twitter about the covid-19 vaccines. Language experts annotated tweets as Relevant or Not Relevant, and then further annotated Relevant tweets with Stance towards each MisT. This collection is a first step in providing large-scale resources for misinformation detection and misinformation stance identification.
Most publications that aim to optimize neural networks for CBIR, train and test their models on domain specific datasets. It is therefore unclear, if those networks can be used as a general-purpose image feature extractor. After analyzing popular image retrieval test sets we decided to manually curate GPR1200, an easy to use and accessible but challenging benchmark dataset with 1200 categories and 10 class examples. Classes and images were manually selected from six publicly available datasets of different image areas, ensuring high class diversity and clean class boundaries.
By releasing this dataset, we aim at providing a new testbed for computer vision techniques using Deep Learning. The main peculiarity is the shift from the domain of "natural images" proper of common benchmark dataset to biological imaging. We anticipate that the advantages of doing so could be two-fold: i) fostering research in biomedical-related fields - for which popular pre-trained models perform typically poorly - and ii) promoting methodological research in deep learning by addressing peculiar requirements of these images. Possible applications include but are not limited to semantic segmentation, object detection and object counting. The data consist of 283 high-resolution pictures (1600x1200 pixels) of mice brain slices acquired through a fluorescence microscope. The final goal is to individuate and count neurons highlighted in the pictures by means of a marker, so to assess the result of a biological experiment. The corresponding ground-truth labels were generated through a hy
Frame-to-frame video alignment/synchronization
This dataset contains a set of face images taken between April 1992 and April 1994 at AT&T Laboratories Cambridge.
The dataset contains 3304 cases from the Supreme Court of the United States from 1955 to 2021. Each case has the case's identifiers as well as the facts of the case and the decision outcome. Other related datasets rarely included the facts of the case which could prove to be helpful in natural language processing applications. One potential use case of this dataset is determining the outcome of a case using its facts.
The Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation models in aerial domains. Additionally, a set of relevant meta-parameters can be used to quantify dataset variability as well as the impact of environmental conditions on model performance.
This dataset contains a set of sentences by extracting all the sentences mentioning the term from the court decisions retrieved from the Caselaw access project data.
QST contains 1,167 video clips that are cut out from 216 time-lapse 4K videos collected from YouTube, which can be used for a variety of tasks, such as (high-resolution) video generation, (high-resolution) video prediction, (high-resolution) image generation, texture generation, image inpainting, image/video super-resolution, image/video colorization, image/video animating, etc. Each short clip contains multiple frames (from a minimum of 58 frames to a maximum of 1,200 frames, a total of 285,446 frames), and the resolution of each frame is more than 1,024 x 1,024. Specifically, QST consists of a training set (containing 1000 clips, totally 244,930 frames), a validation set (containing 100 clips, totally 23,200 frames), and a testing set (containing 67 clips, totally 17,316 frames). Click here (Key: qst1) to download the QST dataset.
In recent years, the number of range scanners and surface reconstruction algorithms has been growing rapidly. Many researchers, however, do not have access to scanning facilities or dense polygonal models. The purpose of this repository is to make some range data and detailed reconstructions available to the public.