SaratChandraAnil/mldataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Vasamsetti Jayaganesh

Released under MIT

Contents

Dataset Card for Dataset Name

  Dataset Summary

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  Data Instances

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  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/Kartheesh/MLdataset.

Dataset

This dataset was created by JESSICA BETHA YOLANDA

Contents

Sabari231024/mldataset dataset hosted on Hugging Face and contributed by the HF Datasets community

This datastet contains kernels extracted from OpenMP microbenchmarks collected in DataRaceBench. The purpose of this dataset is to serve as training data for machine learning with programming language processing (PLP).

This dataset is required to train the models in the CRISM ML toolbox [1].

In the project, we demonstrate the utility of machine learning in two essential CRISM analysis tasks: nonlinear noise removal and mineral classification. We train a hierarchical Bayesian model for estimating distributions of spectral patterns on pixel-scale training data collected from dozens of well-characterized CRISM images.

The following files are included:

CRISM_bland_unratioed.mat: unratioed training spectra for bland pixels.

CRISM_labeled_pixels_ratioed.mat: ratioed training spectra for mineral classes.

CRISM_labeled_pixel_patterns.pdf: visualization of the training segmentation maps and average spectra.

The training spectra are in Matlab v7.3 (and newer) format. To load them in Python, use the mat73 library, because scipy doesn't support the format.

The bland unratioed spectra have the following variables:

Name Size Description

pixspec 337 617 × 350 Unratioed spectra

im_names 340 List of CRISM image names, mapping them to numerical IDs

pixims 337 617 Numerical ID of the image the spectrum is from

pixcrds 337 617 × 2 (x,y) coordinates of the points in the original image

The labeled ratioed pixels have the following variables:

Name Size Description

pixspec 592 413 × 350 Ratioed spectra

pixlabs 592 413 Mineral labels

im_names 77 List of CRISM image names, mapping them to numerical IDs

pixims 592 413 Numerical ID of the image the spectrum is from

pixpat 592 413 ID of the connected patch in the image the pixel belongs to

pixcrds 592 413 × 2 (x,y) coordinates of pixels in their respective image

Citation (cite this paper when using the data):

Plebani, E., Ehlmann, B. L., Leask, E. K., Fox, V. K., & Dundar, M. M. (2022). A machine learning toolkit for CRISM image analysis. Icarus, 376, 114849.

Dataset

This dataset was created by g6201924112_haodongliu

Contents

Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

AI HW07 ML

## Overview

AI HW07 ML is a dataset for object detection tasks - it contains Fruits annotations for 456 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).

This dataset is built from the Solar Dynamics Observatory (SDO) Machine Learning Dataset provided by Nasa.Solar Dynamics Observatory (SDO) Machine Learning Dataset was accessed on 04.04.2025 from https://registry.opendata.aws/sdoml-fdl.Paper of SDO ML Dataset: https://iopscience.iop.org/article/10.3847/1538-4365/ab1005The dataset is created by taking the AIA channels 'sdomlv2_small.zarr' recorded in 2010 and downscale them to 128x128 pixels.The data is stored in a netCDF file.Since NASA does not provide a restrictive license, the dataset here will also stay under CC0 to keep it in NASA spirit!(https://science.data.nasa.gov/about/license)

Dataset

This dataset was created by NAVEENRAJ MURUGESAN

Released under Apache 2.0

Contents

ML

## Overview

ML is a dataset for object detection tasks - it contains Objects annotations for 361 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).

Included is a machine learning-ready dataset of experimental PDB protein structures.

Here are a few use cases for this project:

1. Quality Control in Manufacturing: Utilize GOOD ML to inspect various products on the assembly line, ensuring that they meet quality standards. By identifying bounding classes of individual components, the model can quickly detect incorrect assembly, misaligned parts, or missing elements in the final product.

2. Recycling and Waste Management: Use the GOOD ML model to streamline the process of sorting waste and recyclable items at waste treatment facilities. Identifying objects' bounding classes can help machines separate objects more efficiently and reduce the manual labor needed for sorting.

3. Inventory Management: Implement the GOOD ML model in warehouses or retail stores to monitor products on shelves and track stock levels. Bounding class identification can enable automatic restocking notifications, as well as catch misplaced items in real-time.

4. Object Recognition for Autonomous Vehicles: Equip self-driving vehicle systems with the GOOD ML model to enhance road safety by identifying and categorizing different objects in its environment. detections of bounding classes can help the vehicle system make more informed decisions, avoiding obstacles, and navigating complex scenarios.

5. Assisting Visually Impaired Users: Create assistive technology with the help of the GOOD ML model to help visually impaired users identify everyday objects. By quickly detecting various bounding classes, the software can describe objects to users and provide useful context about their surroundings.

Mk Ml

## Overview

Mk Ml is a dataset for object detection tasks - it contains Letters annotations for 1,190 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).

Minor Ml

## Overview

Minor Ml is a dataset for object detection tasks - it contains Boats Ships Surfboards annotations for 8,447 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).

tinycrops/dorsaroh-ml-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Amey Sunil Deshmukh

Contents

Aniruddh79012/ml-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community