latterworks/geo-img-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The main goal of AI in agriculture is to improve crop yield, control crop pests/diseases, and reduce cost. The agricultural sector in developing countries faces severe in the form of disease and pest infestation, the knowledge gap between farmers and technology, and a lack of storage facilities, among others. To help address some of these challenges, this work presents crop pests/disease datasets sourced from local farms in Ghana. The dataset is presented in two folds; the raw images which consists of 24,881 images ( 6,549-Cashew, 7,508-Cassava, 5,389-Maize, and 5,435-Tomato) and augmented images which is further split into train and test set consists of 102,976 images (25,811-Cashew, 26,330-Cassava, 23,657-Maize, and 27,178-Tomato), categorized into 22 classes. All images are de-identified, validated by expert plant virologists, and freely available for use by the research 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)

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

🔹 Description: This dataset contains 8,247 labeled images related to Valorant, categorized by agents, weapons, abilities, maps, and game modes. It includes file paths and corresponding tags, making it ideal for image classification, AI-powered search engines, and deep learning projects.

📌 Dataset Features - 📂 Images stored in valorant_images/ - 🏷 Tags extracted from filenames (e.g., valorant_Sova_5.jpg → Sova) - 📜 CSV File (valorant_dataset.csv) with 1. image_path: Full path to each image 2. tag: Label extracted from filename1.

📌 Use Cases ✔ Train a deep learning model for Valorant image classification ✔ Build an AI-powered Valorant search engine ✔ Create an image-based recommendation system ✔ Develop a Valorant-themed generative AI model

Industry Detection

## Overview

Industry Detection is a dataset for object detection tasks - it contains Industry annotations for 255 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).

Orignal Dataset

## Overview

Orignal Dataset is a dataset for object detection tasks - it contains Objects annotations for 656 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).

TableView

## Overview

TableView is a dataset for object detection tasks - it contains Dishes annotations for 2,990 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.

This dataset is a compilation of data obtained from the Idaho Department of Water Quality, the Idaho Department of Water Resources, and the Water Quality Portal. The 'SiteID' table catalogues organization-specific identification numbers assigned to each monitoring location.

The dataset contains 293 HGG and 76 LGG pre-operative scans in four MRI modalities, which are T1, T2, T1c and FLAIR.

RAID (Robust AI Detection) is a benchmark dataset designed to evaluate AI-generated text detectors. It contains adversarially manipulated text to assess the robustness of detection models. This dataset is derived from the full RAID dataset but includes only adversarially attacked text.

Dataset Composition

The RAID dataset includes text modified using various adversarial attack strategies. These attacks introduce distortions that can mislead AI detectors.

Adversarial Attacks Included

• Article Deletion: Removing articles (e.g., 'a', 'an', 'the') from the text.
• Homoglyph: Replacing characters with visually similar ones from different scripts.
• Number Swap: Substituting numbers with their textual representations or vice versa.
• Paraphrase: Rewriting sentences with different wording while maintaining the original meaning.
• Synonym Swap: Replacing words with their synonyms.
• Misspelling: Introducing common misspellings into the text.
• Whitespace Addition: Adding extra whitespace characters within words or sentences.
• Upper-Lower Swap: Changing the case of letters in the text.
• Zero-Width Space: Inserting zero-width space characters that are invisible but affect text processing.
• Insert Paragraphs: Adding unrelated paragraphs into the text.
• Alternative Spelling: Using alternative spellings for words (e.g., 'colour' vs. 'color').

These adversarial attacks are designed to test the resilience of AI-generated text detectors against various manipulation techniques.

Dataset Access

The original RAID dataset is available on multiple platforms:

• GitHub: RAID Repository
• Hugging Face Datasets: RAID on Hugging Face

For detailed information on the dataset and its usage, please refer to the RAID GitHub repository.

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

the layout planning of residential community has always been of concern

Dumb

## Overview

Dumb is a dataset for instance segmentation tasks - it contains Pole annotations for 300 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).

Database containing observations of fungi and Mycetozoa mainly from Denmark. New observations are continuously added through the registration portal http://svampe.databasen.org, which was developed as part of the "Danmarks Svampeatlas" project. The project is a collaboration between the Natural History Museum of Denmark and Department of Biology, University of Copenhagen, the Danish Mycological Society and MycoKey. The project received generous financial support from Aage V. Jensen Naturfond. The aim of Svampeatlas is to compile all Basidiomycota from Denmark and to increase the knowledge of fungal distribution and ecology in Denmark, by making this information publicly available. With more than 400 active users contributing to the project, there has been more than 325.000 finds with a total of about 2.500 species of Basidiomycota. In addition a similar number of older finds has been imported from various published sources, persona and project databases.

About this dataset

Context

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

Key observations

There is a majority of female population, with 57.39% 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 Owaneco is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Owaneco 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 Owaneco Population by Race & Ethnicity. You can refer the same here

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

This dataset was constructed from the test set split of the VoxCeleb 2 dataset (VoxCeleb). The VoxCeleb 2 test set contains 118 speakers each in several different videos. To develop this dataset, only one video per speaker was selected. A face image was also extracted from the video, as well as, a low resolution face image (8x8). Age, gender and ethnicity of the person in the face image were determined using the “DeepFace” library, a face recognition and facial attribute analysis library.

This dataset can be used to evaluate speech2face, speech conditioned face generation and speech conditioned face super-resolution systems.

Rock Analysis

## Overview

Rock Analysis is a dataset for object detection tasks - it contains Rock annotations for 300 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).