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PATTERN is a node classification tasks generated with Stochastic Block Models, which is widely used to model communities in social networks by modulating the intra- and extra-communities connections, thereby controlling the difficulty of the task. PATTERN tests the fundamental graph task of recognizing specific predetermined subgraphs. Source: Benchmarking Graph Neural Networks
PatternNet is a large-scale high-resolution remote sensing dataset collected for remote sensing image retrieval. There are 38 classes and each class has 800 images of size 256×256 pixels. The images in PatternNet are collected from Google Earth imagery or via the Google Map API for some US cities. The following table shows the classes and the corresponding spatial resolutions. The figure shows some example images from each class.
GarmentCodeData contains 115,000 data points that cover a variety of designs in many common garment categories: tops, shirts, dresses, jumpsuits, skirts, pants, etc., fitted to a variety of body shapes sampled from a custom statistical body model based on CAESAR, as well as a standard reference body shape, applying three different textile materials.
A set of patterns used in psychophysical research to evaluate the ability of saliency algorithms to find targets distinct from distractors in orientation, color and size. Each image is a 7x7 grid and contains a single target. All images are 1024x1024px and have corresponding ground truth masks for the target and distractors.
PointPattern is a graph classification dataset constructed by simple point patterns from statistical mechanics. The authors simulated three point patterns in 2D: hard disks in equilibrium (HD), Poisson point process, and random sequential adsorption (RSA) of disks. The HD and Poisson distributions can be seen as simple models that describe the microstructures of liquids and gases while the RSA is a nonequilibrium stochastic process that introduces new particles one by one subject to nonoverlapping conditions. These systems are well known to be structurally different, while being easy to simulate, thus provides a solid and controllable classification task. For each point pattern, the particles are treated as nodes, and edges are subsequently drawn according to whether two particles are within a threshold distance. Source: Path Integral Based Convolution and Pooling for Graph Neural Networks Image Source: Path Integral Based Convolution and Pooling for Graph Neural Networks
CPM-Synt-1 is a dataset consisting of 5555 images with synthesis - makeup images with pattern segmentation mask
CPM-Synt-2 is a dataset consisting of 1625 images with synthesis - triplets: makeup, non-makeup, ground-truth.
This data set provides fine-granular statistics on trading traffic generated by six global exchanges over the course of two days in February 2019 for a set of representative feeds and recorded by the systems of vwd Vereinigte Wirtschaftsdienste GmbH (now known as Infront Financial Technology GmbH). Please note that these numbers represent only limited market segments of the actual exchange and the measured feeds might provide different products and instrument types. The exchanges are identified as AU = Sydney, FFM = Frankfurt am Main (GER), HK = Hong Kong (CN), Q = NASDAQ (USA), TK = Tokyo (JPN), UK = London (UK). Please see the Zenodo page https://doi.org/10.5281/zenodo.6381970 for details on syntax etc.
The Dataset contains more than 23500 3D garment models with their corresponding sewing patterns, each representing a unique garment design sampled from one of the 19 different categories. The dataset is suitable for training Deep Learning models to solve a variety of clothing-related tasks.
Fire Drill Anti-Pattern Dataset is a collection of nine real-world software projects for detection of the fire drill anti-pattern with ground truth, issue-tracking data, source code density, models and code. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. It includes data, ground truth, code, and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. Therefore, this package includes the following: Fire Drill in issue-tracking data: * Ground truth for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus. * Coefficients for indicators for the first method, per project. * Detailed issue-tracing data for each project: what occurred and when. * Time logs for each project. Fire Drill in source-code data: * Three technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits. * Source code density data (metrics) for each commit in each of the nine projects as a separate dataset. * Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).
After defining a taxonomy of the main stone deterioration patterns and anomalies, we selected 354 highly representative images of stone-built heritage, offering them a careful selection of labels to choose from.
Dataset (part 1/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked
Dataset (part 2/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked
Dataset (part 3/3) for Motion Patterns Identification part of MotionID: Human Authentication Approach. Data type: bin (should be converted by attached notebook). Six users each with a Samsung Galaxy S10e smartphone collected IMU data every day for 2 weeks. At the end of two weeks, the users switched smartphones with each other and restarted the process. Each user spent 2 weeks per smartphone during the whole data collection process, which took 12 weeks in total. Throughout the experiment, the Galaxy S10e was the main and only device of each user. The smartphones were used habitually and ordinarily, with the only difference from real-life scenarios being that the data collection app was always on. Each measurement has a corresponding timestamp. Screen.txt file consists of timestamps with current status of device: 1) SCREEN_OFF - a phone turned off 2) SCREEN_ON - a phone is on 3) USER_PRESENT - a phone has just been unlocked
PatternCom is a composed image retrieval benchmark based on PatternNet. PatternNet is a large-scale high-resolution remote sensing image retrieval dataset. There are 38 classes and each class has 800 images of size 256×256 pixels. In PatternCom, we select some classes to be depicted in query images, and add a query text that defines an attribute relevant to that class. For instance, query images of “swimming pools” are combined with text queries defining “shape” as “rectangular”, “oval”, and “kidney-shaped”. In total, PatternCom includes six attributes consisted of up to four different classes each. Each attribute can be associated with two to five values per class. The number of positives ranges from 2 to 1345 and there are more than 21k queries in total.
The relational pattern similarity dataset is a new dataset upon the work of Zeichner et al. (2012), which consists of relational patterns with semantic inference labels annotated. The dataset includes 5,555 pairs extracted by Reverb (Fader et al., 2011), 2,447 pairs with inference relation and 3,108 pairs (the rest) without one. Source: Composing Distributed Representations of Relational Patterns
The SPOTS-10 dataset is an extensive collection of grayscale images showcasing diverse patterns found in ten animal species. Specifically, SPOTS-10 contains 50,000 32 x 32 grayscale images, divided into ten categories, with 5,000 images per category. The training set comprises 40,000 images, while the test set contains 10,000 images. The SPOTS-10 dataset is freely available on the project GitHub page: https://github.com/amotica/spots-10 by cloning the repository.
ec-darkpattern is a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.’s study in 2019 [11kScale], which consists of 1,818 dark pattern texts from shopping sites. Negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.'s dataset. Source: Dark patterns in e-commerce: a dataset and its baseline evaluations Image Source: https://arxiv.org/pdf/2211.06543v1.pdf
This dataset consists of two categories. Real world images and spoof images(Captured from Screen) which shows moire patterns. ### Introduction This dataset can be used to classify real world images and images captured from laptop, mobile phones or TV. The spoof images shows the moire pattern which can help detect fake or spoof personality. This dataset can be used to enhance vision based security algorithms. ### Dataset Features - Captured by 3000+ unique users - Rich in diversity - Mobile phone view point - Various lighting conditions - Indoor and Outdoor scene ### Dataset Features - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us on sales@datacluster.ai Visit www.datacluster.in to know more. Note: All the images are manually verified and are contributed by the large contributor base on DataCluster platform