19,997 machine learning datasets
19,997 dataset results
MyoPS is a dataset for myocardial pathology segmentation combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segment
FloorPlanCAD is a large-scale real-world CAD drawing dataset containing over 15,000 floor plans, ranging from residential to commercial buildings.
The ROBUST-MIS dataset was made available to support the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge 2019, part of the Endoscopic Vision Challenge associated with MICCAI.
We introduce a new audio dataset called SoundDescs that can be used for tasks such as text to audio retrieval, audio captioning etc. This dataset contains 32,979 pairs of audio files and text descriptions. There are 23 categories found in SoundDescs including but not limited to nature, clocks, fire etc.
ProteinKG25 is a large-scale KG dataset with aligned descriptions and protein sequences respectively to GO terms and proteins entities. ProteinKG25 contains 4,990,097 triplets (4,879,951 Protein-GO triplets and 110,146 GO-GO triplets), 612,483 entities (565,254 proteins and 47,229 GO terms) and 31 relations.
ADIMA is a novel, linguistically diverse, ethically sourced, expert annotated and well-balanced multilingual profanity detection audio dataset comprising of 11,775 audio samples in 10 Indic languages spanning 65 hours and spoken by 6,446 unique users.
This dataset is collected from various global and local news sources. Toponyms are manually annotated in the articles with the corresponding entries from GeoNames. In total, the dataset consists of 118 articles.
Kinetics-100 is a dataset split created from the Kinetics dataset to evaluate the performance of few-shot action recognition models. 100 classes are randomly selected from a total of 400 categories, each composed of 100 examples. The 100 classes are further split into 64, 12, and 24 non-overlapping classes to use as the meta-training set, meta-validation set, and meta-testing set, respectively. Link to the selected samples can be found here: https://github.com/ffmpbgrnn/CMN/tree/master/kinetics-100
X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health andHuman Services of Montgomery County, MD, USA. This set contains 138 posterior-anterior x-rays, of which 80 x-rays are normal and 58 x-rays areabnormal with manifestations of tuberculosis. All images are de-identified and available in DICOM format. The set covers a wide range of abnormalities,including effusions and miliary patterns. The data set includes radiology readings available as a text files and summary of its content
Research on semantic segmentation of traffic scenes using color and polarization information (including training and testing sets).
This datasets is a subset of the Amazon reviews dataset which contain Fashion related products
GigaST is a large-scale pseudo speech translation (ST) corpus. The corpus was created by translating the text in GigaSpeech, an English ASR corpus, into German and Chinese. The training set is translated by a strong machine translation system and the test set was translated by human. ST models trained with an addition of the corpus obtain new state-of-the-art results on the MuST-C English-German benchmark test set.
COUCH is a large human-chair interaction dataset with clean annotations. The dataset consists of 3 hours and over 500 sequences of motion capture (MoCap) on human-chair interactions.
The Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.
The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.
Echocardiography, or cardiac ultrasound, is the most widely used and readily available imaging modality to assess cardiac function and structure. Combining portable instrumentation, rapid image acquisition, high temporal resolution, and without the risks of ionizing radiation, echocardiography is one of the most frequently utilized imaging studies in the United States and serves as the backbone of cardiovascular imaging. For diseases ranging from heart failure to valvular heart diseases, echocardiography is both necessary and sufficient to diagnose many cardiovascular diseases. In addition to our deep learning model, we introduce a new large video dataset of echocardiograms for computer vision research. The EchoNet-Dynamic database includes 10,030 labeled echocardiogram videos and human expert annotations (measurements, tracings, and calculations) to provide a baseline to study cardiac motion and chamber sizes.
The goal of this challenge is to solve simultaneously ten image classification problems representative of very different visual domains. The data for each domain is obtained from the following image classification benchmarks:
MathMLben is a benchmark to the evaluate tools for mathematical format conversion (LaTeX ↔ MathML ↔ CAS). It comprises semantically annotated and linked formulae extracted from the NTCIR 11/12 arXiv and Wikipedia task / dataset, NIST Digital Library of Mathematical Functions (DLMF) and annotations using the AnnoMathTeX formula and identifier name recommender system (https://annomathtex.wmflabs.org).
multi-view imagery of people interacting with a variety of rich 3D environments
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