19,997 machine learning datasets
19,997 dataset results
The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models.
Chest X-ray images for pneumonia detection.
smac+ defense infantry scenario with parallel episodic buffer
SMAC+ defensive infantry scenario with sequential episodic buffer
SMAC+ defensive armored scenario with sequential episodic buffer
SMAC+ defensive outnumbered scenario with sequential episodic buffer
This is a dataset for a video inverse-tone-mapping task. The dataset contains various contents for the task of restoring HDR video: fireworks, flowers, football, night city, scenes with reflections. Videos have different brightness ranges and contain different types of lighting. The camera for shooting the dataset captures 14 stops of the dynamic range.
The Universal Morphology (UniMorph) project is a collaborative effort to improve how NLP handles complex morphology in the world’s languages. The goal of UniMorph is to annotate morphological data in a universal schema that allows an inflected word from any language to be defined by its lexical meaning, typically carried by the lemma, and by a rendering of its inflectional form in terms of a bundle of morphological features from our schema. The specification of the schema is described here and in Sylak-Glassman (2016).
EgoProceL is a large-scale dataset for procedure learning. It consists of 62 hours of egocentric videos recorded by 130 subjects performing 16 tasks for procedure learning. EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC, EGTEA Gaze+, and individual tasks like toy-bike assembly, tent assembly, PC assembly, and PC disassembly. EgoProceL overcomes the limitations of third-person videos. As, using third-person videos makes the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action.
The Open Access Series of Imaging Studies (OASIS) is a project aimed at making neuroimaging data sets of the brain freely available to the scientific community. By compiling and freely distributing neuroimaging data sets, we hope to facilitate future discoveries in basic and clinical neuroscience.
Colorization validation set for unconditional/conditional colorization tasks. Subset of the ImageNet validation images and excludes andy grayscale single-channel images.
A large-scale dataset for the point cloud completion task on the ShapeNet dataset.
Moral Foundations Reddit Corpus (MFRC) is a collection of 16,123 Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework.
EgoHOS is a labeled dataset consisting of 11243 egocentric images with per-pixel segmentation labels of hands and objects being interacted with during a diverse array of daily activities. The data are collected form multiple sources: 7,458 frames from Ego4D, 2,212 frames from EPIC-KITCHEN, 806 frames from THU-READ, and 350 frames of our own collected egocentric videos with people playing Escape Room. This dataset is designed for tasks including hand state classification, video activity recognition, 3D mesh reconstruction of hand-object interactions, and video inpainting of hand-object foregrounds in egocentric videos.
GLOBEM is a multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. The datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years.
The EntitySeg dataset contains 33,227 images with high-quality mask annotations. Compared with existing dataets, there are three distinct properties in EntitySeg. First, 71.25% and 86.23% of the images are of high resolution with at least 2000px×2000px and 1000px×1000px which is more consistent with current digital imaging trends. Second, the dataset is open-world and is not limited to predefined classes. Third, the mask annotation along the boundaries are more accurate than existing datasets.
Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision,
Stuttering is a complex speech disorder that negatively affects an individual’s ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a t
SciRepEval is a comprehensive benchmark for training and evaluating scientific document representations. It includes 25 challenging and realistic tasks, 11 of which are new, across four formats: classification, regression, ranking and search.
EXPY-TKY contains the traffic speed information and the corresponding traffic incident information in 10-minute interval for 1843 expressway road links in Tokyo over three months (2021/10∼2021/12). Compared with other benchmarks for traffic prediction, EXPY-TKY covers a larger scale and more complex incident situations. Potential tasks of EXPY-TKY include traffic prediction, incident detection, and road type classification.