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

TelmoRobredo/Habi-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

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).

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).

In this paper, we introduce a victim dataset for the RoboCup Rescue competitions. The RoboCup Rescue robots have to collect points within several disciplines, e.g. a search task within an area to survey simulated baby doll (victim).

When a robot comes across a victim, a heat detector does not completely proof if this is a living being and not just a heat emitting somewhat else. Further investigations are necessary so that a face detection could prove the existence of a victim. Lots of face detection approaches can be found in literature, which manly are used for human face recognition. These cannot be straightforward used for victim faces which are, in case of the RoboCup Rescue competitions, typically dolls. Thus we present the results of standard approaches and developed an own approach via bag-of-visual-words (BoVW).

Plates Full Frame

## Overview

Plates Full Frame is a dataset for object detection tasks - it contains Plate annotations for 2,134 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).

Large Shape and Texture dataset (LAS&T) is a giant dataset of shapes and textures for tasks of visual shapes and textures identification and retrieval from single image.

LAS&T is the largest and most diverse dataset for shape, texture and material recognition and retrieval in 2D and 3D with 650,000 images, based on real world shapes and textures

Overview The LAS&T Dataset aims to test the most basic aspect of vision in the most general way. Mainly the ability to identify any shape, texture, and material in any setting and environment, without being limited to specific types or classes of objects, materials, and environments. For shapes, this means identifying and retrieving any shape in 2D or 3D with every element of the shape changed between images, including the shape material and texture, orientation, size, and environment. For textures and materials, the goal is to recognize the same texture or material when appearing on different objects, environments, and light conditions. The dataset relies on shapes, textures, and materials extracted from real-world images, leading to an almost unlimited quantity and diversity of real-world natural patterns. Each section of the dataset (shapes, and textures), contains 3D parts that rely on physics-based scenes with realistic light materials and object simulation and abstract 2D parts. In addition, the real-world benchmark for 3D shapes.

The dataset divided to several parts 3D shape recognition and retrieval.

2D shape recognition and retrieval.

3D Materials recognition and retrieval.

2D Texture recognition and retrieval.

Each can be used independently for training and testing.

Additional assets are a set of 350,000 natural 2D shapes extracted from real-world images

3D shape recognition real-world images benchmark

Dataset

This dataset was created by mdnurhossen

Released under CC0: Public Domain

Contents

Bed

## Overview

Bed is a dataset for object detection tasks - it contains Bed Person Guardbar annotations for 455 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset is grouped by service provider specialty, and provides information about the number of recipients, number of claims, and dollar amount for given diagnosis claims. Restricted to claims with service date between 01/2012 to 12/2017. Restricted to claims with a primary diagnosis only. Restricted to top 100 most frequent diagnosis codes that are marked as primary diagnosis of a claim. Provider is the rendering provider marked in the claim. Provider specialty is the primary specialty of the rendering provider. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA.

TinY_YOLO_Final

## Overview

TinY_YOLO_Final is a dataset for object detection tasks - it contains 0 1 2 annotations for 1,144 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).

About this dataset

Context

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

Key observations

There is a majority of male population, with 56.89% of total population being male. 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 Loomis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Loomis 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 Loomis Population by Race & Ethnicity. You can refer the same here

Sécurité Routière

## Overview

Sécurité Routière is a dataset for object detection tasks - it contains Phones Seatbelt annotations for 9,720 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).

Wiki-Reliability: Machine Learning datasets for measuring content reliability on WikipediaConsists of metadata features and content text datasets, with the formats:- {template_name}_features.csv - {template_name}_difftxt.csv.gz - {template_name}_fulltxt.csv.gz For more details on the project, dataset schema, and links to data usage and benchmarking:https://meta.wikimedia.org/wiki/Research:Wiki-Reliability:_A_Large_Scale_Dataset_for_Content_Reliability_on_Wikipedia

Mixed_Sheep

## Overview

Mixed_Sheep is a dataset for object detection tasks - it contains Sheep annotations for 2,396 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).

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 51.15% 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 Hartville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Hartville 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 Hartville Population by Race & Ethnicity. You can refer the same here

and secure

This dataset is based on the Sample Leads Dataset and is intended to allow some simple filtering by lead source. I had modified this dataset to support an upcoming Towards Data Science article walking through the process. Link to be shared once published.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 51.36% 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 Woodway is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Woodway 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 Woodway Population by Race & Ethnicity. You can refer the same here