19,997 machine learning datasets
19,997 dataset results
The NetHack Learning Environment (NLE) is a Reinforcement Learning environment based on NetHack 3.6.6. It is designed to provide a standard reinforcement learning interface to the game, and comes with tasks that function as a first step to evaluate agents on this new environment. NetHack is one of the oldest and arguably most impactful videogames in history, as well as being one of the hardest roguelikes currently being played by humans. It is procedurally generated, rich in entities and dynamics, and overall an extremely challenging environment for current state-of-the-art RL agents, while being much cheaper to run compared to other challenging testbeds. Through NLE, the authors wish to establish NetHack as one of the next challenges for research in decision making and machine learning.
Various documents dataset. Each of the 65 documents includes scanned ground truth images, both hard and easy distorted photos, and document-centered cropped images.
The Rhetorical Structure Theory (RST) Discourse Treebank consists of 385 Wall Street Journal articles from the Penn Treebank annotated with discourse structure in the RST framework along with human-generated extracts and abstracts associated with the source documents.
RECCON is a dataset for the task of recognizing emotion cause in conversations.
The ITOP dataset consists of 40K training and 10K testing depth images for each of the front-view and top-view tracks. This dataset contains depth images with 20 actors who perform 15 sequences each and is recorded by two Asus Xtion Pro cameras. The ground-truth of this dataset is the 3D coordinates of 15 body joints.
LitBank is an annotated dataset of 100 works of English-language fiction to support tasks in natural language processing and the computational humanities, described in more detail in the following publications:
EURLEX57K is a new publicly available legal LMTC dataset, dubbed EURLEX57K, containing 57k English EU legislative documents from the EUR-LEX portal, tagged with ∼4.3k labels (concepts) from the European Vocabulary (EUROVOC).
Collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ.
The DeepWeeds dataset consists of 17,509 images capturing eight different weed species native to Australia in situ with neighbouring flora.
HeadQA is a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans.
KAIST Multispectral Pedestrian Dataset
A large-scale dataset for Complex KBQA.
MMAct is a large-scale dataset for multi/cross modal action understanding. This dataset has been recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios.
Social-IQ is an unconstrained benchmark specifically designed to train and evaluate socially intelligent technologies. By providing a rich source of open-ended questions and answers, Social-IQ opens the door to explainable social intelligence. The dataset contains rigorously annotated and validated videos, questions and answers, as well as annotations for the complexity level of each question and answer. Social-IQ contains 1,250 natural in-the-wild social situations, 7,500 questions and 52,500 correct and incorrect answers.
This is a 21 class land use image dataset meant for research purposes.
IXI Dataset is a collection of 600 MR brain images from normal, healthy subjects. The MR image acquisition protocol for each subject includes:
Toyota Smarthome Trimmed has been designed for the activity classification task of 31 activities. The videos were clipped per activity, resulting in a total of 16,115 short RGB+D video samples. activities were performed in a natural manner. As a result, the dataset poses a unique combination of challenges: high intra-class variation, high-class imbalance, and activities with similar motion and high duration variance. Activities were annotated with both coarse and fine-grained labels. These characteristics differentiate Toyota Smarthome Trimmed from other datasets for activity classification.
ShapeWorld is a new evaluation methodology and framework for multimodal deep learning models, with a focus on formal-semantic style generalization capabilities. In this framework, artificial data is automatically generated according to predefined specifications. This controlled data generation makes it possible to introduce previously unseen instance configurations during evaluation, which consequently require the system to recombine learned concepts in novel ways.
CoSQA (Code Search and Question Answering) It includes 20,604 labels for pairs of natural language queries and codes, each annotated by at least 3 human annotators.