bongo2112/mulokoziepk-dreambooth-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Jules Dataset

## Overview

Jules Dataset is a dataset for instance segmentation tasks - it contains Pallet annotations for 2,140 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Ow

## Overview

Dataset Ow is a dataset for object detection tasks - it contains Player annotations for 10,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

Defect Detection In Screws

## Overview

Defect Detection In Screws is a dataset for classification tasks - it contains Screw annotations for 480 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

andersonbcdefg/openai-moderation-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Home Objects

## Overview

Home Objects is a dataset for object detection tasks - it contains Objects annotations for 4,467 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The authors introduced DeepFish Dataset as a benchmark suite accompanied by a vast dataset tailored for training and evaluating various computer vision tasks. This dataset comprises roughly 40,000 images captured underwater across 20 distinct habitats in the tropical waters of Australia. Initially, the dataset solely featured classification labels. However, recognizing the need for a more comprehensive fish analysis benchmark, the authors augmented it by collecting segmentation labels. These labels empower models to autonomously monitor fish populations, pinpoint their locations, and estimate their sizes, thereby enhancing the dataset's utility for diverse analytical purposes.

The U.S. Geological Survey has developed a National Elevation Database (NED). The NED is a seamless mosaic of best-available elevation data. The 7.5-minute elevation data for the conterminous United States are the primary initial source data. In addition to the availability of complete 7.5-minute data, efficient processing methods were developed to filter production artifacts in the existing data, convert to the NAD83 datum, edge-match, and fill slivers of missing data at quadrangle seams. One of the effects of the NED processing steps is a much-improved base of elevation data for calculating slope and hydrologic derivatives.

Dataset

This dataset was created by M. Raza Siddique

Contents

3d Error Monitoring3

## Overview

3d Error Monitoring3 is a dataset for object detection tasks - it contains Under Over Extrusion Spaghetti annotations for 523 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Purpose

This challenge is intended to be fun, relaxed, and collaborative. Today you should just sit back, relax, share ideas, and simply enjoy the art of computer programming.

What is TSP

The Traveling Salesperson Problem (TSP) is set up like this:

You are a salesperson, and you have a list of cities you want to visit. You wish to travel to each city exactly once then return home. What order should you visit the cities to minimize the total distance you travel?

This is obviously a Graph Theory problem. We can model this problem as a graph where cities are nodes, and between every pair of cities there's a edge weighted by the distance between those cities.

Dataset

We'll assume the salesperson lives in a boring, flat, 2D Cartesian plane, and that each city can be described simply as existing at a single (x, y) location. Each datafile describes some number of cities, one city per line in the file, where each city has a index (first city is index-0) and a location.

I've provided four toy datasets for you to play with.

Tiny Dataset This dataset contains only 10 cities. Hint: This dataset is small enough that you can write a brute-force algorithm to find the optimal solution. Small Dataset This dataset contains 30 cities, clustered into 3 regions. Hint: Find local optimal solutions within each region, then combine those local solutions intelligently. (Of course first you have to find the regions, either by visualizing the data and/or via a clustering algorithm like k-means.) Medium Dataset This dataset contains 100 cities. Hint: Start with a greedy algorithm, then have your program iteratively (and randomly) improve upon the solution found by the greedy algorithm. Large Dataset Hint: Best of luck. :)

Original Data

Acknowlegement

Foto von Stephen Monroe auf Unsplash

In this document, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets. Contact at Tilburg University Francesco Lelli Data files: 6 raw Microsoft Excel files (.xls) Supplemental material: Data_Publication_Package.pdf Detailed description of the data has been released in the following preprint: [Preprint in progress] Structure data package The folder contains the 6 .xls documents, the data publication package. Link to the preprint describing the dataset is in the description of the dataset itself. The six .xls documents are also present in their preferred file format csv (see Notes for further explanation). Production date: 01-2024---- 05-2024 Method: Data on information security breaches through manual search of the Internet, financial data through Refinitiv (LSEG). (Approval obtained from Refinitiv to publish these data) Universe: S&P500 companies Country / Nation: USA

Kaggle Datasets For Traffic

## Overview

Kaggle Datasets For Traffic is a dataset for object detection tasks - it contains Traffic Sign annotations for 8,122 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Brawl Stars Detection

## Overview

Brawl Stars Detection is a dataset for object detection tasks - it contains Brawlers annotations for 210 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Reorganized version of Wild-Heart/Disney-VideoGeneration-Dataset. This is needed for Mochi-1 fine-tuning.

This dataset contains Essays written by students and the corresponding scores they have achieved for that particular question.

NOTE: These scores are not official and based on the evaluation of independent teachers and the data has been collected from another Kaggle Dataset and simplified for the purpose of tutorial published alongside on the blog.

Dataset

This dataset was created by Timerkhanov Yuriy

Released under CC0: Public Domain

Contents

Layout planning is centrally important in the field of architecture and urban design. Among the various basic units carrying urban functions, residential community plays a vital part for supporting human life. Therefore, the layout planning of residential community has always been of concern, and has attracted particular attention since the advent of deep learning that facilitates the automated layout generation and spatial pattern recognition. However, the research circles generally suffer from the insufficiency of residential community layout benchmark or high-quality datasets, which hampers the future exploration of data-driven methods for residential community layout planning. The lack of datasets is largely due to the difficulties of large-scale real-world residential data acquisition and long-term expert screening. In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date. ReCo Dataset is presented in multiple data formats with 37,646 residential community layout plans, covering 598,728 residential buildings with height information. ReCo can be conveniently adapted for residential community layout related urban design tasks, e.g., generative layout design, morphological pattern recognition and spatial evaluation. To validate the utility of ReCo in automated residential community layout planning, two Generative Adversarial Network (GAN) based generative models are further applied to the dataset. We expect ReCo Dataset to inspire more creative and practical work in intelligent design and beyond.

About this dataset

Context

The dataset tabulates the population of Hopkinsville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Hopkinsville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.8% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Hopkinsville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Hopkinsville total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hopkinsville Population by Race & Ethnicity. You can refer the same here