⭐ OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ APOTEK OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ CARA MENGGUGURKAN KANDUNGAN 087776558899 ⭐ PENJUAL OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ TEMPAT OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ LOKASI OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ JUAL OBAT PENGGUGUR KANDUNGAN 087776558899
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The STL-10 is an image dataset derived from ImageNet and popularly used to evaluate algorithms of unsupervised feature learning or self-taught learning. Besides 100,000 unlabeled images, it contains 13,000 labeled images from 10 object classes (such as birds, cats, trucks), among which 5,000 images are partitioned for training while the remaining 8,000 images for testing. All the images are color images with 96×96 pixels in size.
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AG News (AG’s News Corpus) is a subdataset of AG's corpus of news articles constructed by assembling titles and description fields of articles from the 4 largest classes (“World”, “Sports”, “Business”, “Sci/Tech”) of AG’s Corpus. The AG News contains 30,000 training and 1,900 test samples per class.
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Para cambiar el nombre en tu boleto, comunícate con el servicio de atención al cliente llamando al ☎️📞+52-559-612-5112 (MX) 𝐨𝐫 +1-888-887-4088 (𝐎𝐓𝐀). Se permiten correcciones menores en el nombre, como corregir un nombre mal escrito, pero los boletos no se pueden transferir a otra persona.
938 PAPERS • 52 BENCHMARKS
MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
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The Shanghaitech dataset is a large-scale crowd counting dataset. It consists of 1198 annotated crowd images. The dataset is divided into two parts, Part-A containing 482 images and Part-B containing 716 images. Part-A is split into train and test subsets consisting of 300 and 182 images, respectively. Part-B is split into train and test subsets consisting of 400 and 316 images. Each person in a crowd image is annotated with one point close to the center of the head. In total, the dataset consists of 330,165 annotated people. Images from Part-A were collected from the Internet, while images from Part-B were collected on the busy streets of Shanghai.
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The ShanghaiTech Campus dataset has 13 scenes with complex light conditions and camera angles. It contains 130 abnormal events and over 270, 000 training frames. Moreover, both the frame-level and pixel-level ground truth of abnormal events are annotated in this dataset.
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The UCF-Crime dataset is a large-scale dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies including Abuse, Arrest, Arson, Assault, Road Accident, Burglary, Explosion, Fighting, Robbery, Shooting, Stealing, Shoplifting, and Vandalism. These anomalies are selected because they have a significant impact on public safety.
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This dataset contains expert-labeled telemetry anomaly data from the Mars Science Laboratory (MSL) rover, Curiosity.
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An open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge.
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The UCSD Anomaly Detection Dataset was acquired with a stationary camera mounted at an elevation, overlooking pedestrian walkways. The crowd density in the walkways was variable, ranging from sparse to very crowded. In the normal setting, the video contains only pedestrians. Abnormal events are due to either: the circulation of non pedestrian entities in the walkways anomalous pedestrian motion patterns Commonly occurring anomalies include bikers, skaters, small carts, and people walking across a walkway or in the grass that surrounds it. A few instances of people in wheelchair were also recorded. All abnormalities are naturally occurring, i.e. they were not staged for the purposes of assembling the dataset. The data was split into 2 subsets, each corresponding to a different scene. The video footage recorded from each scene was split into various clips of around 200 frames.
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The VisA dataset contains 12 subsets corresponding to 12 different objects as shown in the above figure. There are 10,821 images with 9,621 normal and 1,200 anomalous samples. Four subsets are different types of printed circuit boards (PCB) with relatively complex structures containing transistors, capacitors, chips, etc. For the case of multiple instances in a view, we collect four subsets: Capsules, Candles, Macaroni1 and Macaroni2. Instances in Capsules and Macaroni2 largely differ in locations and poses. Moreover, we collect four subsets including Cashew, Chewing gum, Fryum and Pipe fryum, where objects are roughly aligned. The anomalous images contain various flaws, including surface defects such as scratches, dents, color spots or crack, and structural defects like misplacement or missing parts.
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The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.
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The original ionosphere dataset from UCI machine learning repository is a binary classification dataset with dimensionality 34. There is one attribute having values all zeros, which is discarded. So the total number of dimensions are 33. The ‘bad’ class is considered as outliers class and the ‘good’ class as inliers.
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The First Temporal Benchmark Designed to Evaluate Real-time Anomaly Detectors Benchmark
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The BTAD ( beanTech Anomaly Detection) dataset is a real-world industrial anomaly dataset. The dataset contains a total of 2830 real-world images of 3 industrial products showcasing body and surface defects.
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Lost and Found is a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic.
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This dataset contains images of unusual dangers which can be encountered by a vehicle on the road – animals, rocks, traffic cones and other obstacles. Its purpose is testing autonomous driving perception algorithms in rare but safety-critical circumstances.
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Fishyscapes is a public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle.
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Avenue Dataset contains 16 training and 21 testing video clips. The videos are captured in CUHK campus avenue with 30652 (15328 training, 15324 testing) frames in total.
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UBnormal is a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, the data set introduces abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, the data set includes disjoint sets of anomaly types in the training and test collections of videos.
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MVTec 3D Anomaly Detection Dataset (MVTec 3D-AD) is a comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It contains over 4000 high-resolution scans acquired by an industrial 3D sensor. Each of the 10 different object categories comprises a set of defect-free training and validation samples and a test set of samples with various kinds of defects. Precise ground-truth annotations are provided for each anomalous test sample.
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MPDD is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 1000 images with pixel-precise defect annotation masks. The dataset is divided into the training subset with anomaly-free samples and the validation subset that contains both normal and anomalous samples. The dataset can be downloaded at the following link.
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A large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal.
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CASIA-FASD is a small face anti-spoofing dataset containing 50 subjects.
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Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII) is a sound dataset of industrial machine sounds.
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MVTec Logical Constraints Anomaly Detection (MVTec LOCO AD) dataset is intended for the evaluation of unsupervised anomaly localization algorithms. The dataset includes both structural and logical anomalies. It contains 3644 images from five different categories inspired by real-world industrial inspection scenarios. Structural anomalies appear as scratches, dents, or contaminations in the manufactured products. Logical anomalies violate underlying constraints, e.g., a permissible object being present in an invalid location or a required object not being present at all. The dataset also includes pixel-precise ground truth data for each anomalous region.
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The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.
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Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of oth
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Street Scene is a dataset for video anomaly detection. Street Scene consists of 46 training and 35 testing high resolution 1280×720 video sequences taken from a USB camera overlooking a scene of a two-lane street with bike lanes and pedestrian sidewalks during daytime. The dataset is challenging because of the variety of activity taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition the videos contain changing shadows, moving background such as a flag and trees blowing in the wind, and occlusions caused by trees and large vehicles. There are a total of 56,847 frames for training and 146,410 frames for testing, extracted from the original videos at 15 frames per second. The dataset contains a total of 205 naturally occurring anomalous events ranging from illegal activities such as jaywalking and illegal U-turns to simply those that do not occur in the training set such as pets be
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Encourages machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change.
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Includes 5,824 fundus images labeled with either positive glaucoma (2,392) or negative glaucoma (3,432).
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Real 3D-AD is the first point cloud anomaly detection dataset for industrial products. Real3D-AD comprises a total of 1,254 samples that are distributed across 12 distinct categories. These categories include Airplane, Car, Candybar, Chicken, Diamond, Duck, Fish, Gemstone, Seahorse, Shell, Starfish, and Toffees. Each training sample is an absence of blind spots, and a realistic, high-accuracy prototype.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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The NINCO (No ImageNet Class Objects) dataset is introduced in the ICML 2023 paper In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. The images in this dataset are free from objects that belong to any of the 1000 classes of ImageNet-1K (ILSVRC2012), which makes NINCO suitable for evaluating out-of-distribution detection on ImageNet-1K .
ToyADMOS dataset is a machine operating sounds dataset of approximately 540 hours of normal machine operating sounds and over 12,000 samples of anomalous sounds collected with four microphones at a 48kHz sampling rate, prepared by Yuma Koizumi and members in NTT Media Intelligence Laboratories. The ToyADMOS dataset is designed for anomaly detection in machine operating sounds (ADMOS) research. It is designed for three tasks of ADMOS: product inspection (toy car), fault diagnosis for fixed machine (toy conveyor), and fault diagnosis for moving machine (toy train).
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RoadAnomaly21 is a dataset for anomaly segmentation, the task of identify the image regions containing objects that have never been seen during training. It consists of an evaluation dataset of 100 images with pixel-level annotations. Each image contains at least one anomalous object, e.g. animals or unknown vehicles. The anomalies can appear anywhere in the image and widely differ in size, covering from 0.5% to 40% of the image
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The UCR Anomaly Archive is a collection of 250 uni-variate time series collected in human medicine, biology, meteorology and industry. The collected time series contain a few natural anomalies though the majority of the anomalies are artificial . The dataset was first used in an anomaly detection contest preceding the ACM SIGKDD conference 2021. Each of the time series contains exactly one, occasionally subtle anomaly after a given time stamp. The data before that timestamp can be considered normal. The time series collected in the UCR Anomaly Archive can be categorized into 12 types originating from the four domains human medicine, meteorology, biology and industry. The distribution across the domains is highly imbalanced with around 64% of the times series being collected in human medicine applications, 22% in biology, 9% in industry and 5% being air temperature measurements. The time series within a single type (e.g. ECG) are not completely unique, but differ in terms of injected an
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Yelp-Fraud is a multi-relational graph dataset built upon the Yelp spam review dataset, which can be used in evaluating graph-based node classification, fraud detection, and anomaly detection models.
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HyperKvasir dataset contains 110,079 images and 374 videos where it captures anatomical landmarks and pathological and normal findings. A total of around 1 million images and video frames altogether.
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For benchmarking, please refer to its variant UPFD-POL and UPFD-GOS.
Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category.
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A dataset consisting of stereo thermal, stereo color, and cross-modality image pairs with high accuracy ground truth (< 2mm) generated from a LiDAR. The authors scanned 100 cluttered indoor and 80 outdoor scenes featuring challenging environments and conditions. CATS contains approximately 1400 images of pedestrians, vehicles, electronics, and other thermally interesting objects in different environmental conditions, including nighttime, daytime, and foggy scenes.
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Darpa is a dataset consisting of communications between source IPs and destination IPs. This dataset contains different attacks between IPs.
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Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types).