19,997 machine learning datasets
19,997 dataset results
Used to show systematic performance improvement in applications such as high frame-rate video synthesis, feature/corner detection and tracking, as well as high dynamic range image reconstruction.
RISE is a large-scale video dataset for Recognizing Industrial Smoke Emissions. A citizen science approach was adopted to collaborate with local community members to annotate whether a video clip has smoke emissions. The dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons.
A special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks).
Consists of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field).
RSDD-Time is a dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Additionally, the dataset includes exact temporal spans that relate to the date of diagnosis.
RUSLAN is a Russian spoken language corpus for text-to-speech task. RUSLAN contains 22,200 audio samples with text annotations – more than 31 hours of high-quality speech of one person – being one of the largest annotated Russian corpus in terms of speech duration for a single speaker.
Includes Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language.
A salient object subitizing image dataset of about 14K everyday images which are annotated using an online crowdsourcing marketplace.
SART is a collection of three datasets for Similarity, Analogies and Relatedness for the Tatar language. The three subsets are: * Similarity dataset - 202 pairs of words along with averaged human scores of similarity degree between the words (in 0-to-10 scale). For example, "йорт, бина, 7.69". * Relatedness dataset - 252 pairs of words along with averaged human scores of relatedness degree between the words. For example, "урам, балалар, 5.38". * Analogies dataset - set of analytical questions of the form A:B::C:D, meaning A to B as C to D, and D is to be predicted. For example, "Әнкара Төркия Париж Франция". Contains 34 categories, and in total 30 144 questions.
A visual complexity dataset that compromises of more than 1,400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism. The images in each category portray diverse characteristics including various low-level and high-level features, objects, backgrounds, textures and patterns, text, and graphics.
A dataset of ranked scan-CAD similarity annotations, enabling new, fine-grained evaluation of CAD model retrieval to cluttered, noisy, partial scans.
Background: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. Methods: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic ev
The Sentimental LIAR dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In this dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, the speaker names are converted to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API. Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then the sample is tagged as Positive for the sentiment attribute, otherwise Negative is assigned. A further extension is introduced by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states na
A newly developed natural scene text dataset of Chinese shop signs in street views.
Collects a huge number of job descriptions from Dice.com - one of the most popular career website about Tech jobs in USA. From these job descriptions, skills are extracted for each one by using skills dictionary. Now, the dataset is presented by a list of collections of skills based on job descriptions. After crawling, there are a total of 5GB with more than 1,400,000 job descriptions. From these data, skills are extracted and performed as a list of skills in the same context, the context here includes skills in the same job description.
SpatialSense Benchmark is a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques.
StereoMSI comprises of 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the PIRM2018 challenge.
SubEdits is a human-annnoated post-editing dataset of neural machine translation outputs, compiled from in-house NMT outputs and human post-edits of subtitles form Rakuten Viki. It is collected from English-German annotations and contains 160k triplets.
Comprised of real human and wax figure images and videos that endorse the problem of face spoofing detection. The dataset consists of more than 1800 face images and 110 videos of 55 people/waxworks, arranged in training, validation and test sets with a large range in expression, illumination and pose variations.
Uses a platform with 77 candies and sweets to rank. Over 2000 users submitted over 44000 grades resulting in a matrix with 28% coverage.