Segmentation Rgb Iris_imagedataset

## Overview

Segmentation Rgb Iris_imagedataset is a dataset for semantic segmentation tasks - it contains Pupil 5D8M annotations for 336 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).

There have been very few studies with an evolutionary perspective on eye (iris) color, outside of humans and domesticated animals. Extant members of the family Felidae have a great interspecific and intraspecific diversity of eye colors, in stark contrast to their closest relatives, all of which have only brown eyes. This makes the felids a great model to investigate the evolution of eye color in natural populations. Through machine learning image analysis of publicly available photographs of all felid species, as well as a number of subspecies, five felid eye colors were identified: brown, green, yellow, gray, and blue. Using phylogenetic comparative methods, the presence or absence of these colors was reconstructed on a phylogeny. Additionally, through a new color analysis method, the specific shades of the ancestors’ eyes were quantitatively reconstructed. The ancestral felid population was predicted to have brown-eyed individuals, as well as a novel evolution of gray-eyed individuals, the latter being a key innovation that allowed the rapid diversification of eye color seen in modern felids, including numerous gains and losses of different eye colors. It was also found that the gain of yellow eyes is highly associated with, and may be necessary for, the evolution of round pupils in felids, which may influence the shades present in the eyes in turn. Along with these important insights, the methods presented in this work are widely applicable and will facilitate future research into phylogenetic reconstruction of color beyond irises. Methods Data set In order to sample all felid species, we took advantage of public databases. Images of individuals from 40 extant felid species (all but Felis catus, excluded due to the artificial selection on eye color in domesticated cats by humans), as well as 12 identifiable subspecies and four outgroups (banded linsang, Prionodon linsang; spotted hyena, Crocuta crocuta; common genet, Genetta genetta; and fennec fox, Vulpes zerda), were found using Google Images and iNaturalist using both the scientific name and the common name for each species as search terms. This approach, taking advantage of the enormous resource of publicly available images, allows access to a much larger data set than in the published scientific literature or than would be possible to obtain de novo for this study. Public image-based methods for character state classification have been used previously, such as in a phylogenetic analysis of felid coat patterns (Werdelin and Olsson 1997) and a catalog of iris color variation in the white-browed scrubwren (Cake 2019). However, this approach does require implementing strong criteria for selecting images. Criteria used to choose images included selecting images where the animal was facing towards the camera, at least one eye was unobstructed, the animal was a non-senescent adult, and the eye was not in direct light (causing glare) or completely in shadow (causing unwanted darkening). The taxonomic identity of the animal in each selected image was verified through images present in the literature, as well as the “research grade” section of iNaturalist. When possible, we collected five images per taxon, although some rarer taxa had fewer than five acceptable images available. In addition, some species with a large number of eye colors needed more than five images to capture their variation, determined by quantitative methods discussed below. Each of the 56 taxa and the number of images used are given in Supplementary Table 2. Once the images were selected, they were manually edited using MacOS Preview. This editing process involved choosing the “better” of the two eyes for each image (i.e. the one that is most visible and with the least glare and shadow). Then, the section of the iris for that eye without obstruction, such as glare, shadow, or fur, was cropped out. An example of this is given in Figure S11. The strict selection criteria and image editing eliminated the need to color correct the images, a process that can introduce additional subjectivity; the consistency of the data can be seen in the lack of variation between eyes identified as the same color (Figure S5). This process resulted in a data set of 290 cropped, standardized, irises. These images, along with the original photos, can be found in the Supplementary Material. Eye color identification To impartially identify the eye color(s) present in each felid population, the data set images were loaded by species into Python (version 3.8.8) using the Python Imaging Library (PIL) (Van Rossum and Drake 2009; Clark 2015). For each image, the red, green, and blue (RGB) values for each of its pixels were extracted. Then, they were averaged and the associated hex color code for the average R, G, and B values was printed. The color associated with this code was identified using curated and open source color identification programs (Aerne 2022; Cooper 2022). There is no universally agreed upon list of colors, since exact naming conventions differ on an individual and cultural basis, but these programs offer a workable solution, consisting of tens of thousands of colors names derived from published, corporate, and governmental sources. This data allowed the color of each eye in the data set to be impartially assigned, removing a great deal of the bias inherent in a researcher subjectively deciding the color of each iris. Eye colors were assigned on this basis to one of five fundamental color groups: brown, green (including hazel), yellow (including beige), gray, and blue. The possible color groups were determined before observation of the data based on basic color categories established in the literature: white, black, red, green, yellow, blue, brown, purple, pink, orange, and gray (Berlin and Kay 1991). Of course, not all of the eleven categories ended up being represented by any irises; no irises were observed to be white, black, red, purple, pink, or orange. As an example of this method, if an iris’s color had the RGB values R: 114, G: 160, B: 193, this would correspond to the hex code #72A0C1. This hex code, when put into the color identification programs, results in the identification “Air Superiority Blue”, derived from the British Royal Air Force’s official flag specifications (Cooper 2022; Aerne 2022). Based on the identification, this iris would be added to the “blue” color group, bypassing a researcher having to choose the color themself. If a color’s name did not already contain one of the eleven aforementioned color categories, the name was searched for in the Inter-Society Color Council-National Bureau of Standards (ISCC–NBS) System of Color Designation (Judd and Kelly 1939). For instance, the color with RGB values R: 37, G: 29, B: 14 corresponds to hex code #251D0E, identified as “Burnt Coffee” by the color identification programs. The ISCC–NBS descriptor for this color is “moderate brown”, so the color would be added to the “brown” group. All colors were able to be placed directly from their color name or their ISCC–NBS descriptor and, for colors with both a color category in the name and an ISCC–NBS descriptor, there were no instances in which the two conflicted. While color itself lies on a spectrum, splitting the colors into discrete fundamental groups is the most tractable approach to analyze eye color in a biologically reasonable way. If every eye color was instead taken together on one spectrum and analyzed as a continuous trait, the results would be highly unrealistic. As an example, if there were two sister taxa, one with blue eyes (R: 0, G: 0, B: 139) and one with brown eyes (R: 150, G: 75, B: 0), a continuous reconstruction would assign the ancestor the intermediate eye color in the color space: R: 75, G: 37, B: 69. However, this color is firmly within the “purple” category. It is highly unlikely that a recent ancestor of two taxa with blue and brown eyes had purple eyes, rather than blue eyes, brown eyes, or both, which would be the result if blue and brown were considered as separate categories. Indeed, one would run into the same issue if categories were removed at an earlier stage and each taxon was only considered to have one eye color, determined by averaging all irises. A taxon with blue and brown eyes would again be said to have purple eyes, a color which none of the members of that taxon have. The data being separated into color groups is the most realistic way to investigate this trait, preventing the loss of variation present in the natural populations and simultaneously creating impossible analyses. The lines between color categories are not always clear to an observer (e.g. grayish-blues and bluish-grays can look alike) and, no matter how they are defined, they may still be arbitrary. Nevertheless, this is why we used color identification programs, impartially defining the lines to make the analysis possible. To ensure no data was missed due to low sample size, the first 500 Google Images, as well as all the “research grade” images on iNaturalist, were manually viewed for each species, while referring back to already analyzed data and periodically checked with the color identification programs (Aerne 2022; Cooper 2022). Any missed colors were added to the data set. This method nonetheless has a small, but non-zero, chance to miss rare eye colors that are present in species. However, overall, it provides a robust and repeatable way to identify the general iris colors present in animals. In addition, if, for a given species, one, two, or three eye colors were greatly predominant in the available data online (i.e. the first 500 Google Images, as well as all the “research grade” images on iNaturalist), they were defined as being the most common eye color(s). For three colors to be considered the most common, each color had to be present for >26.6% of the images. For two colors, each had to be present for >40%

NIICU_RGBT_RGB

## Overview

NIICU_RGBT_RGB is a dataset for object detection tasks - it contains Person annotations for 4,705 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 research team sought to create a methodology and software that allows for identification of deceased individuals based on iris patterns, with computer- and human-driven components. Using a dataset of post-mortem and peri-mortem iris images (acquired in near infrared and visible light) representing 259 cases, the research team engineered a software package, PMExpert, that incorporated three post-mortem specific iris matching algorithms. To understand what features humans believe to be useful in post-mortem iris matching, participants analyzed pairs of post-mortem samples, classified them as those originating from the same or different eyes, and annotated features supporting the decision. Iris Images: After the curation of all data collected by the Dutchess County Medical Examiner's Office, NY, iris images from 259 cases were selected for the final dataset release, and for analyses carried out in this project. This data corpus consists of 5,770 NIR and 4,643 RGB images, including images for one peri-mortem case with corresponding post-mortem samples after demise. Human Examination Data: The researchers conducted an experiment to collect annotation data on what humans believe to be distinctive features useful for post-mortem iris matching. Initial participants were recruited through the University of Notre Dame to complete study tasks in-person on-site. Due to the COVID-19 pandemic, the study design was later modified to be an online experiment recruiting participants through Amazon Mechanical Turk. This data acquisition took place in two rounds: The first round was the initial collection of annotation data wherein participants had no prior knowledge of the task or previous decisions. The second round, called the verification step, is where the annotations collected in the first round were presented to future participants for them to either agree with or disagree with along with supporting annotations. Software Package: A software tool called PMExpert was created to provide a simple unified interface for all recognition methods, allowing them to be used in an operational setting. PMExpert consists of two main components: a command line interface (CLI) and a graphical user interface (GUI). Both components are meant to allow examiners to use post-mortem iris recognition methods on images that are collected in their routine operations, offering not only similarity scores and decisions, but also additional information to equip examiners to make their final decision.

Files available at: https://doi.org/10.5281/zenodo.3906216. (TXT)

Méthodologie appliquée à partir de 2022.

*****

Permet de visualiser l’évolution de 2011 à 2023 des consommations d'électricité et de gaz par secteur d'activité, par catégorie de consommation, par code NAF et par IRIS.

Ces données sont publiées dans le respect des règles relatives à la protection des Informations Commercialement Sensibles.

Une notice méthodologique est disponible en PJ.

Les données sont ventilées sur le référentiel INSEE au 1er janvier 2023.

Une question sur le jeu de données ? Un cas d'usage à partager avec d'autres utilisateurs ? Le Forum des experts open data électricité et gaz est là pour ça

ATTENTION : Les données du millésime 2023 (hors GRDF) ont été téléchargées le 01/10/2024 à 10h50, tandis que les données GRDF ont été téléchargées le 16/12/2024 à 21h29.

Ce jeu de données n'est plus maintenu. Se reporter au jeu avec la nouvelle méthodologie à partir de 2022.

Permet de visualiser l’évolution de 2011 à 2021 des consommations d'électricité et de gaz par secteur d'activité, par catégorie de consommation, par code NAF et par IRIS.

Ces données sont publiées dans le respect des règles relatives à la protection des Informations Commercialement Sensibles.

Une question sur le jeu de données ? Un cas d'usage à partager avec d'autres utilisateurs ? Le Forum des experts open data électricité et gaz est là pour ça !

Ce jeu de données n'est plus maintenu. Se reporter au jeu avec la nouvelle méthodologie à partir de 2022.

Permet de visualiser l’évolution de 2011 à 2021 des consommations d'électricité et de gaz par secteur d'activité (résidentiel, tertiaire, industriel, agricole ou non affecté) et par IRIS.

Le jeu de données décrit aussi le nombre de points de livraison par maille géographique.

Les données sont ventilées sur le référentiel INSEE au 1er janvier 2021.

Ces données sont publiées dans le respect des règles relatives à la protection des Informations Commercialement Sensibles.

Une question sur le jeu de données ? Un cas d'usage à partager avec d'autres utilisateurs ? Le Forum des experts open data électricité et gaz est là pour ça !