19,997 machine learning datasets
19,997 dataset results
NEWSKVQA is a new dataset of 12K news videos spanning across 156 hours with 1M multiple-choice question-answer pairs covering 8263 unique entities.
For the Drone-vs-Bird Detection Challenge 2021, 77 different video sequences have been made available as training data. These video sequences originate from the previous installment of the challenge and were collected using MPEG4-coded static cameras by the SafeShore project, by the Fraunhofer IOSB research institute and by the ALADDIN2 project. On average, the video sequences consist of 1,384 frames, while each frame contains 1.12 annotated drones. The video sequences are recorded with both static cameras and moving cameras and the resolution varies between 720×576 and 3840×2160 pixels. In total, 8 different types of drones exist in the dataset , i.e. 3 with fixed wings and 5 rotary ones. For each video, a separate annotation file is provided, which contains the frame number and the bounding box (expressed as [topx topy width height]) for the frames in which drones enter the scenes.
The dataset X of this work is an extension of the heartSeg dataset. Each sample x ∈ X is an RGB image capturing the heart region of Medaka (Oryzias latipes) hatchlings from a constant ventral view. Since the body of Medaka is see-through, noninvasive studies regarding the internal organs and the whole circulatory system are practicable. A Medaka’s heart contains three parts: the atrium, the ventricle, and the bulbus. The atrium receives deoxygenated blood from the circulatory system and delivers it to the ventricle, which forwards it into the bulbus. The bulbus is the heart’s exit chamber and provides the gill arches with a constant blood flow. The blood flow through these three chambers was captured in 63 short recordings (around 11 seconds with 24 frames per second each) in total, from which the single image samples x ∈ X are extracted. The dataset is split into training and test data following the heartSeg dataset with ntrain = 565 samples in the training set Xtrain and ntest = 165
Presented data contains the record of five spreading campaigns that occurred in a virtual world platform. Users distributed avatars between each other during the campaigns. The processes varied in time and range and were either incentivized or not incentivized. Campaign data is accompanied by events. The data can be used to build a multilayer network to place the campaigns in a wider context. To the best of the authors knowledge, the study is the first publicly available dataset containing a complete real multilayer social network together, along with five complete spreading processes in it.
This csv consists of (x-position, y-position, area) tuples of three views (left, middle, right) of downscaled binary masks with aspect ratio kept (64 x 128) from the 2019 YouTube-VIS challenge, which can be found at https://competitions.codalab.org/competitions/20127#participate-get-data. Extracting pairs from this csv results in 234,652 transitions in the given statistics. These statistics can be used to augment ground truth factor distributions with natural transitions, which we demonstrate with spriteworld. For details, we refer to our paper, which can be found at https://openreview.net/forum?id=EbIDjBynYJ8.
The feature files are named with the youtube IDs. https://drive.google.com/drive/folders/10-6hkQxMKMGwLXANxfPRE7xw5PKiMjLn?usp=sharing
ata Set Name: Rice Dataset (Commeo and Osmancik) Abstract: A total of 3810 rice grain's images were taken for the two species (Cammeo and Osmancik), processed and feature inferences were made. 7 morphological features were obtained for each grain of rice.
Yavuz Selim TASPINAR, Murat KOKLU and Mustafa ALTIN
This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).
This is a research artifact for the ICSE'22 paper "GitHub Sponsors: Exploring a New Way to Contribute to Open Source". The following three research questions were constructed to guide the study.
Deep Soccer Captioning is a dataset consists of 22k caption-clip pairs and three visual features (images, optical flow, inpainting) for 500 hours of SoccerNet videos.
ArgSciChat is an argumentative dialogue dataset. It consists of 498 messages collected from 41 dialogues on 20 scientific papers. It can be used to evaluate conversational agents and further encourage research on argumentative scientific agents.
HelloWorld is a dataset of kinesthetic demonstrations collected using a Franka Emika Panda robot. During the data collection, the robot was made to write the lower case letters $h, e, l, o, w, r, d$ one at a time on a horizontal surface and the $x$ and $y$ coordinates of the end-effector were recorded. Multiple demonstrations were collected for each letter. These demonstrations can be used for kinesthetic teaching. See further details here.
The CIP dataset is composed of 2 subsets, containing low-cost (MPU9250) and high-end (MTwAwinda) Magnetic, Angular Rate, and Gravity (MARG) sensor data respectively. It provides data for the analysis of the complete inertial pose pipeline, from raw measurements, to sensor-to-segment calibration, multi-sensor fusion, skeleton kinematics, to the complete human pose. Multiple trials were collected with 21 and 10 subjects respectively, performing 6 types of movements (ranging from calibration, to daily-activities, range-of-motion and random). It presents a high degree of variability and complex dynamics while containing common sources of error found on real conditions. This amounts to 3.5M samples, synchronized with a ground-truth inertial motion capture system (Xsens) at 60hz. This dataset may contribute to assess, benchmark and develop novel algorithms for each of the pipelines' processing steps, with applications in classic or data-driven inertial pose estimation algorithms, human movem
Here I provided the datasets I used for this analysis. It includes the tweets I streamed using the Tweepy package on Python during the peach of the wildfire season in late summer/early fall of 2020.
This dataset is used for user identity linkage across two online social networks in Chinese. It contains two popular Chinese social platforms: Sina Weibo\footnote{https://weibo.com} and Douban\footnote{https://www.douban.com}.
This is a repository of PCAP files obtained by executing ransomware binaries and capturing the network traffic created when encrypting a set of files shared from an SMB server. There are 94 samples from 32 different ransomware families downloaded from malware-traffic-analysis and hybrid-analysis. There is a link to an info page for each sample, offering some information about the sample and about the scenario where it ran ('More info' column in the table). You can download 10% of the packets from each traffic trace for free. If you find it useful and you want to download the whole samples, we ask for your e-mail and institution name, in order to keep a record of hoy many people are interested in these files. This helps us to keep this repository up and include more samples (as it proves that it is interesting for the community). We do not send you any kind of spam. We will only send you a link to download the full pcap files. In order to refer to this repository please include the link
https://github.com/zzr-idam/Under-Display-Camera-UAV
Optical images of printed circuit boards as well as detailed annotations of any text, logos, and surface-mount devices (SMDs). There are several hundred samples spanning a wide variety of manufacturing locations, sizes, node technology, applications, and more.
At the Isfahan Fertility and Infertility Center, semen samples were collected from fifteen patients. The sperm samples were fixed and stained using the Diff-Quick method. Using an Olympus CX21 microscope with a ×100 objective lens and a ×10 eyepiece and a Sony color camera (Model No SSC-DC58AP), 725 images were taken. The resolution of each image was 576×720 pixels. From these images, the sperm heads were cropped and classified into five classes by three specialists. The classes are Normal, Pyriform, Tapered, Amorphous, and Others. After the classification, only the samples which there was a collective consensus about their class were kept in the dataset. Four classes of Normal, Pyriform, Tapered, and Amorphous are included in this dataset. The resulting dataset of sperm heads denoted as Human Sperm Head Morphology dataset (HuSHeM) consists of four folders, each corresponding to a specific set of sperm shapes. The folder names reflect the shape of the contained images. There are 54