Brief Explanation

This dataset is created to develop and evaluate a cataloging system which assigns appropriate metadata to an image record for database management in digital libraries. That is assumed for evaluating a task, in which given an image and assigned tags, an appropriate Wikipedia page is selected for each of the given tags.

A main characteristic of the dataset is including ambiguous tags. Thus, visual contents of images are not unique to their tags. For example, it includes a tag 'mouse' which has double meaning of not a mammal but a computer controller device. The annotations are corresponding Wikipedia articles for tags as correct entities by human judgement.

The dataset offers both data and programs that reproduce experiments of the above-mentioned task. Its data consist of sources of images and annotations. The image sources are URLs of 420 images uploaded to Flickr. The annotations are a total 2,464 relevant Wikipedia pages manually judged for tags of the images. The dataset also provides programs in Jupiter notebook (scripts.ipynb) to conduct a series of experiments running some baseline methods for the designated task and evaluating the results.

Structure of the Dataset

1. data directory 1.1. image_URL.txt This file lists URLs of image files.

   1.2. rels.txt This file lists collect Wikipedia pages for each topic in topics.txt

   1.3. topics.txt This file lists a target pair, which is called a topic in this dataset, of an image and a tag to be disambiguated.

   1.4. enwiki_20171001.xml This file is extracted texts from the title and body parts of English Wikipedia articles as of 1st October 2017. This is a modified data of Wikipedia dump data (https://archive.org/download/enwiki-20171001).

2. img directory This directory is a placeholder directory to fetch image files for downloading.

3. results directory This directory is a placeholder directory to store results files for evaluation. It maintains three results of baseline methods in sub-directories. They contain json files each of which is a result of one topic, and are ready to be evaluated using an evaluation scripts in scripts.ipynb for reference of both usage and performance.

4. scripts.ipynb The scripts for running baseline methods and evaluation are ready in this Jupyter notebook file.

The 'Dresden Image Database' comprises original JPEG images from 73 camera devices across 25 camera models. This dataset is primarily used for Source Camera Device and Model Identification, offering over 14,000 images captured under controlled conditions.

Copyright: "Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee."

Original Source (Not Working as on 28 June 2024): http://forensics.inf.tu-dresden.de/dresden_image_database/

Please Cite the corresponding paper "Gloe, T., & Böhme, R. (2010, March). The'Dresden Image Database'for benchmarking digital image forensics. In Proceedings of the 2010 ACM symposium on applied computing (pp. 1584-1590)."

Testing the use of Morpho to store image data. Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Freeves.18.1 for complete metadata about this dataset.

Digital Image

## Overview

Digital Image is a dataset for object detection tasks - it contains Objects annotations for 264 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 "Real & Fake Images Dataset-For Image Forensics" is meticulously curated to support the development and evaluation of algorithms for detecting fake images. This dataset is organized into three primary directories: train, test, and validation. Each of these directories contains two subdirectories, one with real images and the other with fake images.

The dataset is an invaluable resource for researchers, data scientists, and developers working in the field of image forensics, deep learning, and computer vision. It provides a solid foundation for training models that can distinguish between authentic and manipulated images. This task has become increasingly crucial with the rise of deepfakes and other forms of image manipulation.

By utilizing this dataset, you can:

• Develop robust algorithms for detecting fake images.
• Enhance existing models by training them on a balanced dataset of real and fake images.
• Conduct comprehensive evaluations of model performance in real-world scenarios.
• Explore the nuances of image manipulation techniques to understand better and combat digital forgeries.

This dataset is particularly useful for those aiming to advance the state of the art in image forensics, ensuring that digital content remains trustworthy in an era of increasingly sophisticated visual deception.

The Clementine UVVIS DIM Mosaic is a full-resolution (100 meters per pixel) global mosaic produced by the U.S. Geological Survey from Clementine EDR Data. Imaging data acquired by the Ultraviolet/Visible Camera were used to create the multi-band mosaic.

A dataset for digital image forensics, containing 224 images.

Hosted by Iron Mountain. The DRC is an IBM Image on Demand System. It stores and manages digital images and fixed content. It currently holds British Coal Corporation (BCC) scanned Medical Records (formerly paper based), EDM Legacy (Inbound, Medical and Outbound folders), Nurse Records and VWF Medical records from a former Atos contract. It also holds a number of other scanned images from legacy databases relating to BCC staff records, earning and spirometry records transferred for other databases. In addition NCS Claim Archive data is transferred on a regular basis.

This dataset contains images made digitally employing a desktop scanner, a camera, and screen capture software.

This dataset includes 63 still images extracted from digital video imagery of sediment grab samples, along with laboratory grain size analysis of the sediment grab samples, taken from the mouth of the Columbia River, OR and WA, USA. Digital video was collected in September 2014 in the mouth of the Columbia River, USA, as part of the U.S. Geological Survey Coastal and Marine Geology Program contribution to the Office of Naval Research funded River and Inlets Dynamics experiment (RIVET II). Still images were extracted from the underwater video footage whenever the camera was resting on the sediment bed and individual sediment grains were visible and in focus. The images were used to calculate the calibration curve through auto-correlation regressed against the results of laboratory-determined median grain size (D50) of the grab samples (Barnard, 2007), provided in an accompanying .csv file.

This data set contains the Magellan the FMAP, a full-resolution (75 meters/pixel) global mosaic, produced by the U.S. Geological Survey from Magellan F-BIDR data. The complete dataset consists of 340 quadrangles in Sinusoidal equal-area projection. Quadrangles extend approximately 12 degrees in latitude, except for those between 84 and 90 degrees North and South. Quadrangles near the equator extend 12 degrees in longitude longitudinal extent is increased to maintain a roughly constant number of samples.

Brisbane City Council’s digital collection of images, maps, plans and documents documenting Brisbane's History from the 1850s through to now. Search by title, subject or keyword. To search and view use the link in the Data and resources section below.

A curated collection of verified forensic images contributed by the digital forensics community.

This Image Gallery is provided as a complimentary source of high-quality digital photographs available from the Agricultural Research Service information staff. Photos, (over 2,000 .jpegs) in the Image Gallery are copyright-free, public domain images unless otherwise indicated. Resources in this dataset:Resource Title: USDA ARS Image Gallery (Web page) . File Name: Web Page, url: https://www.ars.usda.gov/oc/images/image-gallery/ Over 2000 copyright-free images from ARS staff.

This dataset consists of images of records containing meteorological surface observations in synoptic format for various stations in the country of Paraguay. It has a period of record ranging from 1940 to 1998. The observations include (but are not limited to) 6-hourly values of temperature, humidity, cloud coverage, cloud heights, rainfall, and wind speed/direction.

Pictures Paintings And Digital Images

## Overview

Pictures Paintings And Digital Images is a dataset for instance segmentation tasks - it contains Pictures annotations for 1,408 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).

High-fidelity computer-based modeling, simulation and visualization systems for the study of temporal bone anatomy and training for middle ear surgery are based on a sequence of digital anatomical images, which must cover a large tissue volume and yet display details in high resolution and with high fidelity. However, the use of existing image libraries by independent developers of virtual models of the ear is limited by copyright protection and low image resolution. A fresh frozen human temporal bone was CT-scanned and serially sectioned at 25 µm and digital images of the block surface were recorded at 50- to 100-µm increments with a Light PhaseTM single-shot camera back attachment. A total of 605 images were recorded in 24-bit RGB resolution. After color correction and elimination of image size variation by differential cropping to 15.4 cm × 9.7 cm, all images were resampled to 3,078 × 1,942 pixels at a final resolution of 50 µm/pixel and stored as 605 one-Mb JPEG files together with a three-dimensional viewer. The resulting complete set of image data provides: (1) a source material suitable for generating computer models of the human ear; (2) a resource of high-quality digital images of anatomical cross sections from the human ear, and (3) a PC-based viewer of the temporal bone in three perpendicular planes of section.

The dataset presents the collection of a diverse electrocardiogram (ECG) database for testing and evaluating ECG digitization solutions. The Powerful Medical ECG image database was curated using 100 ECG waveforms selected from the PTB-XL Digital Waveform Database and various images generated from the base waveforms with varying lead visibility and real-world paper deformations, including the use of different mobile phones, bends, crumbles, scans, and photos of computer screens with ECGs. The ECG waveforms were augmented using various techniques, including changes in contrast, brightness, perspective transformation, rotation, image blur, JPEG compression, and resolution change. This extensive approach yielded 6,000 unique entries, which provides a wide range of data variance and extreme cases to evaluate the limitations of ECG digitization solutions and improve their performance, and serves as a benchmark to evaluate ECG digitization solutions.

PM-ECG-ID database contains electrocardiogram (ECG) images and their corresponding ECG information. The data records are organized in a hierarchical folder structure, which includes metadata, waveform data, and visual data folders. The contents of each folder are described below:

• metadata.csv:
  This file serves as a key-to-key bridge between the image data and the corresponding ECG information. It contains the following columns:
  • Image name: image name with extension,
  • ECG ID: this ID corresponds to the specific ECG identifier from the original PTB-XL dataset. Under this ID you can find a cutout array in the leads.npz and rhythms.npz,
  • Image relative path: relative path to the image in question,
  • Image page: page number of the particular image (starting from 0),
  • ECG number of pages: number of pages in the whole ECG,
  • ECG number of columns per page: number of columns per page in the ECG,
  • ECG number of rows per page: number of rows in the ECG,
  • ECG number of rhythm leads: number of rhythms in the ECG,
  • ECG format: short version of the ECG format.
• data folder:
  • leads.npz: NPZ file containing all underlying cutout lead signals; each signal is there under its ECG ID.
  • rhythms.npz: NPZ file containing all underlying rhythm signals; each signal is there under its ECG ID. If no rhythm lead is in the ECG, you will find an empty array in the NPZ.
• visual_data folder:
  This folder contains subfolders for various image data, including augmented photos and visualization and different types of photos of ECG printouts. The subfolders are organized based on the specific augmentation or type of photograph. These folders contain images with various augmentation settings, such as different levels of blur, brightness, contrast, padding, perspective transformation, resolution scaling, and rotation. The database is organized in a way that allows for easy navigation and understanding of the different augmentations applied to the image data. Each of these subfolders contains images relevant to the specific augmentation or type of photograph. The metadata.csv file provides a direct link to each image and its associated ECG information.

The Computational Photography Project for Pill Identification (C3PI) was sunset in 2018. No new images will be added to the collection. Identifiers for pills will not be updated. Images and metadata are for research and development purposes only.

The Computational Photography Project for Pill Identification (C3PI) created the RxIMAGE database of freely available high-quality digital images of prescription pills and associated data for use in conducting computer vision research in text- and image-based search and retrieval. Photographs of pills for the RxIMAGE database were taken under laboratory lighting conditions, from a camera directly above the front and the back faces of the pill, at high resolution, and using specialized digital macro-photography techniques. Image segmentation algorithms were then applied to create the JPEG images in the database.

Historical information about the project is available in the NLM archive at https://wayback.archive-it.org/7867/20190423182937/https:/lhncbc.nlm.nih.gov/project/c3pi-computational-photography-project-pill-identification.

A highly usable and diverse artistic image dataset from Artstation comprised of high resolution facial portriats. A wide range of technique and styles make up the artworks which makes for a great generative art dataset. Since most of the images had either no clear usage license and/or are derivative works of copyrighted material. This dataset is for non commerical research use only.