CLUSTER 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. CLUSTER aims at identifying community clusters in a semi-supervised setting. Source: Benchmarking Graph Neural Networks

Alibaba Cluster Trace captures detailed statistics for the co-located workloads of long-running and batch jobs over a course of 24 hours. The trace consists of three parts: (1) statistics of the studied homogeneous cluster of 1,313 machines, including each machine’s hardware configuration, and the runtime {CPU, Memory, Disk} resource usage for a duration of 12 hours (the 2nd half of the 24-hour period); (2) long-running job workloads, including a trace of all container deployment requests and actions, and a resource usage trace for 12 hours; (3) co-located batch job workloads, including a trace of all batch job requests and actions, and a trace of per-instance resource usage over 24 hours. It also has a second version of traces cluster-trace-v2018 that includes about 4,000 machines in a period of 8 days. Besides having larger scaler than trace-v2017, this piece trace also contains the DAG information of the production batch workloads.

VPCD contains multi-modal annotations (face, body and voice) for all primary and secondary characters from a range of diverse TV-shows and movies. It is used for evaluating multi-modal person-clustering. It contains body-tracks for each annotated character, face-tracks when visible, and voice-tracks when speaking, with their associated features. It consists of more than 30,000 face and body tracks of 300+ characters, from over 23 hours of video.

Contains $10^7$ points, sampled from 20 clusters, with incremental concept drift - On each batch (of size 1000) the mean of each of the clusters moves a random (small) length in some random direction, the means move independently of each other. This dataset should be used sequentially, in batches of $1000$.

This dataset consists of blurred, noisy and defocused images. ### Introduction Dataset consists of blurred images captured using mobile phones in real-world scenario. Images were captured under wide variety of lighting conditions, weather, indoor and outdoor. This dataset can be used for Image De-noising, Deblurring and noise removal algorithms. This can be also work as robust test set for denoising algorithms. ### Dataset Features - Captured by 3000+ unique users - Rich in diversity - Mobile phone view point - HD Resolution - 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

Code and datasets S1 and S2 used in: - Mostafa M. Abbas, Michaël Aupetit, Michael Sedlmair, Halima Bensmail. ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns. Computer Graphics Forum 38(3): 225-236 (2019). https://doi.org/10.1111/cgf.13684 - Hamza, M. M., Ullah, E., Baggag, A., Bensmail, H., Sedlmair, M., & Aupetit, M. ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings, to appear in SAGE Information Visualization Journal 23(2) 105-122 (2024). https://doi.org/10.1177/14738716231220536 ARXIV: https://arxiv.org/abs/2106.00599 - M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag and H. Bensmail, Toward Perception-Based Evaluation of Clustering Techniques for Visual Analytics 2019 IEEE Visualization Conference (VIS), Vancouver, BC, Canada, 2019, pp. 141-145. doi: 10.1109/VISUAL.2019.8933620 https://ieeexplore.ieee.org/document/8933620 S1: a set of 1000 scatterplot data generated by 2-component Gaussian Mixture Model with various parameters, together with the 8 GMM parameter values and 34 human judgments about perceived separability in scatterplot image: the task was to answer if they see "one" or "more-than-one" clusters. S2: a set of 435 pairs of scatterplot data generated by projecting high-dimensional data with various dimensionality reduction techniques, together with 31 human judgments about which of the two images in a pair shows a more complex cluster pattern: right is more complex, left is more complex, or both are equally complex.

Synthetic Datasets for ICSC Flagship 2.6.1. "Fast Extended Computer Vision" paper #1 "Datacube segmentation via Deep Spectral Clustering" It is a preliminary paper for the ICSC Spoke 2 WP6 flagship 2.6.1 "Fast Extended Computer Vision". Code repository at: https://github.com/ICSC-Spoke2-repo/FastExtendedVision-DeepCluster

Dataset to be used with the https://github.com/MathBioCU/WSINDy_CellCluster code

Click to add a brief description of the dataset (Markdown and LaTeX enabled). Provide: * a high-level explanation of the dataset characteristics * explain motivations and summary of its content * potential use cases of the dataset

LiPC (LiDAR Point Cloud Clustering Benchmark Suite) is a benchmark suite for point cloud clustering algorithms based on open-source software and open datasets. It aims to provide the community with a collection of methods and datasets that are easy to use, comparable, and that experimental results are traceable and reproducible. Source: https://github.com/cavayangtao/lidar_clustering_bench Image Source: https://github.com/cavayangtao/lidar_clustering_bench

This dataset contains the data used for all statistical analysis in our publication "Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering", summarised in a single .csv file. For more details on the study methodology, please refer to our manuscript: Ooi, K.; Lam, B.; Hong, J.; Watcharasupat, K. N.; Ong, Z.-T.; Gan, W.-S. Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering. Sustainability, 2022. For our replication code utilising this data, please refer to our Github repository: https://github.com/ntudsp/singapore-soundscape-site-selection-survey A short explanation of the columns in the .csv file is as follows: Full of life & exciting [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Full of life & exciting". Full of life & exciting [# times visited]: The number of times that the participant had visited the chosen location they considered "Full of life & exciting" before, as reported by the participant. Full of life & exciting [Duration]: The average duration per visit to the chosen location the participant considered "Full of life & exciting", as reported by the participant. Chaotic & restless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Chaotic & restless". Chaotic & restless [# times visited]: The number of times that the participant had visited the chosen location they considered "Chaotic & restless" before, as reported by the participant. Chaotic & restless [Duration]: The average duration per visit to the chosen location the participant considered "Chaotic & restless", as reported by the participant. Calm & tranquil [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Calm & tranquil". Calm & tranquil [# times visited]: The number of times that the participant had visited the chosen location they considered "Calm & tranquil" before, as reported by the participant. Calm & tranquil [Duration]: The average duration per visit to the chosen location the participant considered "Calm & tranquil", as reported by the participant. Boring & lifeless [Latitude]: The latitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [Longitude]: The longitude, in degrees, of the location chosen by the participant as "Boring & lifeless". Boring & lifeless [# times visited]: The number of times that the participant had visited the chosen location they considered "Boring & lifeless" before, as reported by the participant. Boring & lifeless [Duration]: The average duration per visit to the chosen location the participant considered "Boring & lifeless", as reported by the participant.

Tool Database for image-set clustering This database was generated to evaluate a robotic application dealing with image-set clustering. The goal is to sort and store tools on an table in an unsupervised way, from pixel inputs. Pictures contains objects that can be found in a shop-floor. Each picture contains only one object. There are five different conditions, for each condition, lighting conditions and background are changed. For each condition, four picture of each object are taken under different orientations.

This dataset is an extremely challenging set of over 8000+ original Fire and Smoke images captured and crowdsourced from over 1200+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 8000+ - Captured by : Over 1200+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Vehicle detection, Autorickshaw detection, Self driving, Indian vehicles, Number Plate detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset consists of odometer or speedometer images of bike and car vehicles. ### Introduction This dataset can be used to detect or recognize odometer readings of the vehicles. Moreover, it can be used to classify the make of the car and bikes. The usecases can in the domain of insurance, repair and OCR. ### Dataset Features - Captured by 4000+ unique users - Rich in diversity - Mobile phone view point - Various lighting conditions - Digital and Analog Categories - Vehicle Model Types ### 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

This dataset consists of images of bottles and cups. ### Introduction Dataset consists of images of bottle and cups captured using mobile phones in a real-world scenario. Images were captured under a wide variety of indoor lighting conditions. This dataset can be used for the detection of a wide variety of bottles and cups made up of a variety of materials from a lot of different of viewpoints, locations, orientations, etc. ### Dataset Features - Captured by 3000+ unique users - Captured using mobile phones - Variety of different material of bottles and cups - HD Resolution - Highly diverse - Various lighting conditions - Indoor scenes ### 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 at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 3000+ original Crowd images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 400+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 300+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Human detection, CCTV monitoring of crowd, Crowd tracking, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 5000+ original Electronic Items images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 1000+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Applications : Electronics Detection, Daily item detection, Home Automation, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 5000+ original Hindi text images captured and crowdsourced from over 700+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at DataclusterLabs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 700+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 400+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Hindi text detection, Hindi NLP, Text recognition, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 5000+ original India food images captured and crowdsourced from over 800+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 5000+ - Captured by : Over 800+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points, dimlight etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Indian food classification, Dish classification, Food plate detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

Introduction The dataset consists of Indian traffic signs images for classification and detection. The images have been taken in varied weather conditions in daylight, evening and nights. The dataset has a wide variety of variations of illumination, distances, view points etc. This dataset represents a very challenging set of unstructured images of Indian traffic signboards. ### Dataset Features - Captured by 2000+ unique users - Covers wide variety of Indian traffic signs - Captured with 20+ cities accross India - Captured using mobile phones - Highly diverse - Various lighting conditions like day, night, - Outdoor scene with variety of view points ### 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 at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is collected by DataCluster Labs, India. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai This dataset is an extremely challenging set of over 3000+ original Mobile Phone images captured and crowdsourced from over 1000+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 1000+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 600+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Applications : Mobile Phone detection, cracked screen detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 2000+ original Oximeter images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 2000+ - Captured by : Over 300+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 50+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Oximeter Reading detection, Medical Device, Healthcare system detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

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

This dataset is an extremely challenging set of over 3000+ originally Stair images captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 500+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Stair detection , Stair Edge detection , Computer Vision , etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 7000+ original Suitcase/Luggage images captured and crowdsourced from over 800+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at ****DC Labs. ### Dataset Features - Dataset size : 6000+ - Captured by : Over 1000+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 800+ cities accross India - Diversity : Various lighting conditions like day, night, varied distances, view points etc. - Device used : Captured using mobile phones in 2021-2022 - Usage : Luggage detection, suitcase detection, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 3000+ original Transparent object images such as glasses and mirrors are captured and crowdsourced from over 500+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 3000+ - Captured by : Over 500+ crowdsource contributors - Resolution : 99% images HD and above (1920x1080 and above) - Location : Captured with 600+ cities accross India - Diversity : Diversity in object type, lighting, camera type etc. - Device used : Captured using mobile phones in 2020-2022 - Usage : Glass detection | Mirror detection | Transparent Cups detection | Home automation etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform

This dataset is an extremely challenging set of over 2000+ original Visiting card/ID card images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 2000+ - Captured by : Over 150+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 300+ cities accross India - Diversity : Covers a wide variety of things such as reflective paper, different fonts, different color and type of cards, etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Visiting card detection, card edge detection, paper edge detection, ID card OCR, Hindi OCR, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more. Note: All the images are manually captured and verified by a large contributor base on DataCluster platform