19,997 machine learning datasets
19,997 dataset results
This is a multiscale dynamic human mobility flow dataset across the United States, with data starting from January 1st, 2019. By analyzing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state.
The MuSe-CAR database is a large, multimodal (video, audio, and text) dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.
This is a gun detection dataset with 51K annotated gun images for gun detection and other 51K cropped gun chip images for gun classification collected from a few different sources.
RADDet is a radar dataset that contains radar data in the form of Range-Azimuth-Doppler tensors along with the bounding boxes on the tensor for dynamic road users, category labels, and 2D bounding boxes on the Cartesian Bird-Eye-View range map. It is used to train and evaluate methods for object detection using automotive radars.
Bonn RGB-D Dynamic is a dataset for RGB-D SLAM, containing highly dynamic sequences. We provide 24 dynamic sequences, where people perform different tasks, such as manipulating boxes or playing with balloons, plus 2 static sequences. For each scene we provide the ground truth pose of the sensor, recorded with an Optitrack Prime 13 motion capture system. The sequences are in the same format as the TUM RGB-D Dataset, so that the same evaluation tools can be used. Furthermore, we provide a ground truth 3D point cloud of the static environment recorded using a Leica BLK360 terrestrial laser scanner.
CRUW is a dataset for the radar object detection (ROD) task, which aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. The CRUW dataset has a systematic annotation and evaluation system, which involves camera RGB images and radar RF images, collected in various driving scenarios.
Home Action Genome is a large-scale multi-view video database of indoor daily activities. Every activity is captured by synchronized multi-view cameras, including an egocentric view. There are 30 hours of vides with 70 classes of daily activities and 453 classes of atomic actions.
CHAOS challenge aims the segmentation of abdominal organs (liver, kidneys and spleen) from CT and MRI data. ONsite section of the CHAOS was held in The IEEE International Symposium on Biomedical Imaging (ISBI) on April 11, 2019, Venice, ITALY. Online submissions are still welcome!
Deep Learning Hard (DL-HARD) is an annotated dataset designed to more effectively evaluate neural ranking models on complex topics. It builds on TREC Deep Learning (DL) questions extensively annotated with query intent categories, answer types, wikified entities, topic categories, and result type metadata from a leading web search engine.
CTSpine1K is a large-scale and comprehensive dataset for research in spinal image analysis. CTSpine1K is curated from the following four open sources, totalling 1,005 CT volumes (over 500,000 labeled slices and over 11,000 vertebrae) of diverse appearance variations.
OntoGUM is an OntoNotes-like coreference dataset converted from GUM, an English corpus covering 12 genres using deterministic rules.
BiToD is a bilingual multi-domain dataset for end-to-end task-oriented dialogue modeling. BiToD contains over 7k multi-domain dialogues (144k utterances) with a large and realistic bilingual knowledge base. It serves as an effective benchmark for evaluating bilingual ToD systems and cross-lingual transfer learning approaches.
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language
The datasets are machine learning data, in which queries and urls are represented by IDs. The datasets consist of feature vectors extracted from query-url pairs along with relevance judgment labels:
This webgraph is a page-page graph of verified Facebook sites. Nodes represent official Facebook pages while the links are mutual likes between sites. Node features are extracted from the site descriptions that the page owners created to summarize the purpose of the site. This graph was collected through the Facebook Graph API in November 2017 and restricted to pages from 4 categories which are defined by Facebook. These categories are: politicians, governmental organizations, television shows and companies. The task related to this dataset is multi-class node classification for the 4 site categories.
Our dataset which consists of multiple indoor and outdoor experiments for up to 30 m gNB-UE link. In each experiment, we fixed the location of the gNB and move the UE with an increment of roughly one degrees. The table above specifies the direction of user movement with respect to gNB-UE link, distance resolution, and the number of user locations for which we conduct channel measurements. Outdoor 30 m data also contains blockage between 3.9 m to 4.8 m. At each location, we scan the transmission beam and collect data for each beam. By doing so, we can get the full OFDM channels for different locations along the moving trajectory with all the beam angles. Moreover, we use 240 kHz subcarrier spacing, which is consistent with the 5G NR numerology at FR2, so the data we collect will be a true reflection of what a 5G UE will see.
MOD is a large-scale open-domain multimodal dialogue dataset incorporating abundant Internet memes into utterances. The dataset consists of ∼45K Chinese conversations with ∼606K utterances. Each conversation contains about 13 utterances with about 4 Internet memes on average and each utterance equipped with an Internet meme is annotated with the corresponding emotion.
VIL-100 is a video instance lane detection dataset, which contains 100 videos with in total 10,000 frames, acquired from different real traffic scenarios. All the frames in each video are manually annotated to a high-quality instance-level lane annotation, and a set of frame-level and video-level metrics are included for quantitative performance evaluation.
Tiered Reasoning for Intuitive Physics (TRIP) is a novel commonsense reasoning dataset with dense annotations that enable multi-tiered evaluation of machines’ reasoning process. TRIP serves as a benchmark for physical commonsense reasoning that provides traces of reasoning for an end task of plausibility prediction. The dataset consists of human-authored stories describing sequences of concrete physical actions. Given two stories composed of individually plausible sentences and only differing by one sentence (i.e., Sentence 5), the proposed task is to determine which story is more plausible. To understand stories like these and make such a prediction, one must have knowledge of verb causality and precondition, and rules of intuitive physics.