Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

Series Name: Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care information and education (percent)Series Code: SH_LGR_ACSRHERelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

According to the Global Gender Gap Report 2020, 88 percent of females worldwide had primary education, compared to 91 percent of males. By comparison, more females than males had attained tertiary education. The Global Gender Index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2020, the leading country was Iceland with a score of 0.87.

The Gender Equality Index is a tool to measure the progress of gender equality in the EU, developed by EIGE. It gives more visibility to areas that need improvement and ultimately supports policy makers to design more effective gender equality measures.

The Gender Equality Index has tracked the painfully slow progress of gender equality in the EU since 2010, mostly due to advances in decision-making. While equality is more pronounced in some Member States than in others, it is far from a reality for everyone in every area. Gender norms around care, gender segregation in education and the labour market, and gender inequalities in pay remain pertinent.

The Index allows Member States to easily monitor and compare gender equality progress across various groups of women and men in the EU over time and to understand where improvements are most needed. The 2022 Index has a thematic focus on care in the Covid-19 pandemic. It explores the division of informal childcare, long-term care and housework between women and men.

The Gender Equality Index is a composite indicator. With a total of six core domains (work, money, knowledge, time, power and health) and two satellite domains (violence against women and intersecting inequalities), it offers a synthetic and easy-to-interpret measure for gender equality, indicating how far (or close) the EU and its Member States are from achieving gender equality on a scale of 1 to 100.

Building on previous editions alongside EIGE’s approach to ensuring intersecting inequalities are captured, the Gender Equality Index 2022 continues to show the diverse realities that different groups of women and men face. It examines how elements such as disability, age, level of education, country of birth and family type can intersect with gender and create many different kinds of pathways in people's lives.

This dataset presents the number of people attending evening schools and illiteracy eradication centers in the State of Qatar. The data is categorized by level of education (Primary, Preparatory, and Secondary) and gender (Male and Female). The dataset helps analyze trends in adult education and literacy efforts across different educational stages and genders.

Series Name: (S.3.C.9) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care information and education: Component 9: Sexuality Education Curriculum Topics (percent)Series Code: SH_LGR_ACSRHEC9Release Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Analysis of ‘Share of men and women in different status and final groups in higher education’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-www-datenportal-bmbf-de-portal-2-5-83 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

Table 2.5.83: Share of men and women in different status and final groups in higher education

--- Original source retains full ownership of the source dataset ---

This dataset contains data on the number of students in schools and universities categorized by level of education (Pre-primary, Primary, etc.), type of education (Government, Private), and gender (Male, Female). The data provides insight into the enrollment trends across different education levels and types of schools in the region. This dataset is essential for analyzing gender and educational distribution within both government and private institutions.

By Bastian Herre, Pablo Arriagada, Esteban Ortiz-Ospina, Hannah Ritchie, Joe Hasell and Max Roser.

About dataset:

Women’s rights are human rights that all women have. But in practice, these rights are often not protected to the same extent as the rights of men.

Among others, women’s rights include: physical integrity rights, such as being free from violence and making choices over their own body; social rights, such as going to school and participating in public life; economic rights, such as owning property, working a job of their choice, and being paid equally for it; and political rights, such as voting for and holding public office.

The protection of these rights allows women to live the lives they want and to thrive in them.

On this page, you can find data on how the protection of women’s rights has changed over time, and how it differs across countries.

There are 6 dataset in here.

1- Female to male ratio of time devoted to unpaid care work. 2- Share of women in top income groups atkinson casarico voitchovsky 2018. 3- Ratio of female to male labor force participation rates ilo wdi. 4- Female to male ratio of time devoted to unpaid care work. 5- Maternal mortality 6- Gender gap in average wages ilo

In each one, there are some topics and variables that we can analysis and visualize them.

Analysis of ‘Students in tertiary education by gender’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/jrc-10113-rio_students_in_tert on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Both male and female students in tertiary education (ISCED97: ED5-6) and their respective shares. The number of students is expressed as a share of the population. Created by filtering the original Eurostat dataset.

--- Original source retains full ownership of the source dataset ---

This dataset provides information on the number of students and teachers in schools across Qatar, categorized by level of education (Pre-primary, Primary, Preparatory), gender (Male, Female), and type of education (Government, Private). The data offers a detailed breakdown of students and teachers in both government and private institutions. It allows for an in-depth analysis of gender disparities and the distribution of students and teachers across different educational levels and types of education. This dataset is useful for understanding trends in gender representation and resource allocation in the education sector.

This dataset provides information on the number of students by level of education, gender, type of education, and nationality in Qatar. The data is categorized by education level (Pre-primary, Primary, etc.), gender (Male, Female), nationality (Qatari, Non-Qatari), and type of education (Government, Private). It presents insights into the distribution of students across various education types and nationalities, helping to analyze trends in school participation, including the participation of Qatari and non-Qatari students in both government and private schools. This dataset is useful for policymakers, educational planners, and analysts tracking national education trends.

This dataset provides information on the number of students by age, level of education, and gender in Qatar. It breaks down the number of students by specific age groups (e.g., Younger than 3), education level (Pre-primary, Primary, Preparatory, Specialized Preparatory, General Secondary, Specialized Secondary), and gender (Male, Female). The data supports analysis on student distribution across different education levels, age categories, and gender trends. It is a useful resource for understanding the participation of young students in early childhood education and tracking educational trends by age and gender.

Analysis of ‘Women in university education teaching body in public universities by category and academic year. MYH (API identifier: 12733)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-453-12733 on 08 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Women in university education teaching body in public universities by category and academic year. Annual. National. Women and Men in Spain

--- Original source retains full ownership of the source dataset ---

Goal 5Achieve gender equality and empower all women and girlsTarget 5.1: End all forms of discrimination against all women and girls everywhereIndicator 5.1.1: Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sexSG_LGL_GENEQLFP: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 1: overarching legal frameworks and public lifeSG_LGL_GENEQVAW: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 2: violence against womenSG_LGL_GENEQEMP: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 3: employment and economic benefitsSG_LGL_GENEQMAR: Legal frameworks that promote, enforce and monitor gender equality (percentage of achievement, 0 - 100) -- Area 4: marriage and familyTarget 5.2: Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitationIndicator 5.2.1: Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by ageVC_VAW_MARR: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)Indicator 5.2.2: Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrenceTarget 5.3: Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilationIndicator 5.3.1: Proportion of women aged 20–24 years who were married or in a union before age 15 and before age 18SP_DYN_MRBF18: Proportion of women aged 20-24 years who were married or in a union before age 18 (%)SP_DYN_MRBF15: Proportion of women aged 20-24 years who were married or in a union before age 15 (%)Indicator 5.3.2: Proportion of girls and women aged 15–49 years who have undergone female genital mutilation/cutting, by ageSH_STA_FGMS: Proportion of girls and women aged 15-49 years who have undergone female genital mutilation/cutting, by age (%)Target 5.4: Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriateIndicator 5.4.1: Proportion of time spent on unpaid domestic and care work, by sex, age and locationSL_DOM_TSPDCW: Proportion of time spent on unpaid care work, by sex, age and location (%)SL_DOM_TSPDDC: Proportion of time spent on unpaid domestic chores, by sex, age and location (%)SL_DOM_TSPD: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%)Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public lifeIndicator 5.5.1: Proportion of seats held by women in (a) national parliaments and (b) local governmentsSG_GEN_PARLN: Number of seats held by women in national parliaments (number)SG_GEN_PARLNT: Current number of seats in national parliaments (number)SG_GEN_PARL: Proportion of seats held by women in national parliaments (% of total number of seats)SG_GEN_LOCGELS: Proportion of elected seats held by women in deliberative bodies of local government (%)Indicator 5.5.2: Proportion of women in managerial positionsIC_GEN_MGTL: Proportion of women in managerial positions (%)IC_GEN_MGTN: Proportion of women in senior and middle management positions (%)Target 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesIndicator 5.6.1: Proportion of women aged 15–49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health careSH_FPL_INFM: Proportion of women who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women aged 15-49 years)SH_FPL_INFMSR: Proportion of women who make their own informed decisions regarding sexual relations (% of women aged 15-49 years)SH_FPL_INFMCU: Proportion of women who make their own informed decisions regarding contraceptive use (% of women aged 15-49 years)SH_FPL_INFMRH: Proportion of women who make their own informed decisions regarding reproductive health care (% of women aged 15-49 years)Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationSH_LGR_ACSRHE: Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education (%)SH_LGR_ACSRHEC1: (S.1.C.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 1: Maternity Care (%)SH_LGR_ACSRHEC10: (S.4.C.10) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 10: HIV Counselling and Test ServicesSH_LGR_ACSRHEC11: (S.4.C.11) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 11: HIV Treatment and Care Services (%)SH_LGR_ACSRHEC12: (S.4.C.12) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 12: HIV Confidentiality (%)SH_LGR_ACSRHEC13: (S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 13: HPV Vaccine (%)SH_LGR_ACSRHEC2: (S.1.C.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 2: Life Saving Commodities (%)SH_LGR_ACSRHEC3: (S.1.C.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 3: AbortionSH_LGR_ACSRHEC4: (S.1.C.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 4: Post-Abortion Care (%)SH_LGR_ACSRHEC5: (S.2.C.5) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 5: Contraceptive Services (%)SH_LGR_ACSRHEC6: (S.2.C.6) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 6: Contraceptive Consent (%)SH_LGR_ACSRHEC7: (S.2.C.7) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 7: Emergency Contraception (%)SH_LGR_ACSRHEC8: (S.3.C.8) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 8: Sexuality Education Curriculum Laws (%)SH_LGR_ACSRHEC9: (S.3.C.9) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Component 9: Sexuality Education Curriculum Topics (%)SH_LGR_ACSRHES1: (S.1) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 1: Maternity Care (%)SH_LGR_ACSRHES2: (S.2) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 2: Contraceptive and Family Planning (%)SH_LGR_ACSRHES3: (S.3) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 3: Sexuality Education (%)SH_LGR_ACSRHES4: (S.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education: Section 4: HIV and HPV (%)Target 5.a: Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources,

The demand for higher education continues to grow as universities compete globally to attract students. But are students opting for private or public institutions? To what extent do they pursue their education abroad? Are women moving into fields traditionally dominated by men, such as science and computing? These are just some of the questions faced by policymakers looking to expand and diversify their national tertiary education systems. Based on its annual data collection, the UIS produces a range of indicators to track trends in tertiary education at the global, regional and national levels. These data include: enrolment and graduation ratios disaggregated by sex and type of programme; enrolment rates in private and public institutions; and graduates by field of study. The UIS has also developed a series of unique indicators to track the flows of foreign or mobile students. These data reveal the shifting demand for higher education, especially in developing countries, by showing where students go to study and where they come from.

Series Name: (S.1.C.4) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care information and education: Component 4: Post-Abortion Care (percent)Series Code: SH_LGR_ACSRHEC4Release Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Student enrolment in elementary and secondary schools across the province, aggregated by gender and school board or school authority. Includes: * board number * board name * elementary male enrolment * elementary female enrolment * secondary male enrolment * secondary female enrolment * total male enrolment * total female enrolment Enrolment data is reported by schools to the Ontario School Information System (OnSIS), October Submissions. The following school types are included: * public * Catholic To protect privacy, numbers are suppressed in categories with less than 10 students. Note: * Starting 2018-2019, enrolment numbers have been rounded to the nearest five. * Where sum/totals are required, actual totals are calculated and then rounded to the nearest 5. As such, rounded numbers may not add up to the reported rounded totals. ## Related * College enrolment * College enrolments - 1996 to 2011 * University enrolment * Enrolment by grade in secondary schools * Second language course enrolment * Course enrolment in secondary schools * Enrolment by grade in elementary schools

This dataset provides the number of students enrolled in private colleges and universities in Qatar, categorized by educational institution, nationality, and gender. The data includes institutions such as Education City Universities, Hamad Bin Khalifa University, and Lusail University. It allows for the analysis of student enrollment trends across different institutions, nationalities (Qatari and Non-Qatari), and genders. This dataset is useful for understanding the distribution of students in Qatar's higher education institutions, as well as the participation of male and female students within these institutions.