five

VGGFace|人脸识别数据集|计算机视觉数据集

收藏
OpenDataLab2025-04-05 更新2024-05-09 收录
人脸识别
计算机视觉
下载链接:
https://opendatalab.org.cn/OpenDataLab/VGGFace
下载链接
链接失效反馈
资源简介:
数据集包含 2,622 个身份。每个身份都有一个关联的文本文件,其中包含图像的 URL 和相应的面部检测。
提供机构:
OpenDataLab
创建时间:
2022-04-29
AI搜集汇总
数据集介绍
main_image_url
构建方式
VGGFace数据集的构建基于深度学习技术,特别是卷积神经网络(CNN)。该数据集由牛津大学的视觉几何组(Visual Geometry Group)开发,通过从互联网上收集的大量人脸图像进行训练。这些图像经过精细的预处理,包括对齐、裁剪和标准化,以确保输入数据的一致性和质量。随后,利用这些预处理后的图像训练深度神经网络,以提取高层次的人脸特征。
特点
VGGFace数据集以其高质量和广泛的应用范围著称。该数据集包含了超过260万张图像,涵盖了2622个不同个体的面部特征。这些图像具有高分辨率和多样化的光照、姿态和表情变化,使得数据集在人脸识别和验证任务中表现出色。此外,VGGFace还提供了预训练的深度学习模型,便于研究人员和开发者快速应用于实际项目。
使用方法
VGGFace数据集主要用于人脸识别、验证和特征提取等任务。研究人员可以通过加载预训练的VGGFace模型,快速实现高精度的人脸识别系统。对于开发者而言,该数据集提供了丰富的API和工具,支持自定义训练和模型微调。此外,VGGFace还支持与其他深度学习框架的无缝集成,如TensorFlow和PyTorch,使得其在实际应用中具有极高的灵活性和可扩展性。
背景与挑战
背景概述
VGGFace数据集是由牛津大学视觉几何组(Visual Geometry Group, VGG)于2015年创建的,主要研究人员包括Omkar M. Parkhi和Andrea Vedaldi等人。该数据集的核心研究问题集中在人脸识别领域,旨在提供一个大规模、高质量的人脸图像库,以推动深度学习在人脸识别技术中的应用。VGGFace包含了超过260万张图像,涵盖了2622个不同个体的面部特征,极大地促进了人脸识别算法的发展和评估。其影响力不仅体现在学术研究中,还对工业界的人脸识别技术产生了深远影响。
当前挑战
尽管VGGFace数据集在人脸识别领域取得了显著成就,但其构建和应用过程中仍面临诸多挑战。首先,数据集的规模和多样性要求高精度的图像采集和处理技术,以确保每张图像的质量和代表性。其次,人脸识别算法在处理光照、姿态和表情变化时仍存在局限性,这需要更复杂的模型和更丰富的数据来解决。此外,数据集的隐私和安全问题也是一大挑战,如何在保证数据使用效率的同时,确保用户隐私不被侵犯,是当前亟待解决的问题。
发展历史
创建时间与更新
VGGFace数据集由牛津大学视觉几何组(Visual Geometry Group)于2015年创建,旨在推动人脸识别技术的发展。该数据集在创建后经过多次更新,以适应不断变化的技术需求和研究方向。
重要里程碑
VGGFace的首次发布标志着人脸识别领域的一个重要里程碑,它包含了超过260万张图像,涵盖了2622个不同个体的面部数据。这一数据集的规模和多样性极大地推动了深度学习在人脸识别中的应用。随后,VGGFace2的发布进一步扩展了数据集的规模和多样性,包含了超过330万张图像,涵盖了9131个个体,显著提升了人脸识别模型的性能和泛化能力。
当前发展情况
当前,VGGFace数据集已成为人脸识别研究中的一个重要基准,广泛应用于学术研究和工业应用中。其丰富的数据资源和高质量的标注为研究人员提供了宝贵的实验平台,推动了人脸识别技术的不断进步。此外,VGGFace的持续更新和扩展也反映了人脸识别领域对数据多样性和规模的不断追求,为未来的研究提供了坚实的基础。
发展历程
  • VGGFace数据集首次发表,由牛津大学的研究团队创建,包含2622个身份的260万张面部图像。
    2015年
  • VGGFace在多个面部识别和验证任务中得到广泛应用,显著提升了识别准确率。
    2016年
  • VGGFace2数据集发布,包含9131个身份的331万张面部图像,进一步扩展了VGGFace的应用范围。
    2017年
  • VGGFace和VGGFace2数据集在多个国际计算机视觉竞赛中被用作基准数据集,推动了面部识别技术的发展。
    2018年
  • VGGFace2数据集的改进版本发布,增加了更多的多样性和复杂性,以应对现实世界中的识别挑战。
    2020年
常用场景
经典使用场景
在计算机视觉领域,VGGFace数据集以其丰富的面部图像资源和高质量的标注信息,成为人脸识别和表情分析的经典工具。该数据集包含了超过260万张图像,涵盖了2622个不同个体的面部特征,为研究人员提供了一个广泛且多样化的实验平台。通过利用VGGFace,研究者们能够开发和验证各种人脸识别算法,从而推动该领域的前沿技术发展。
解决学术问题
VGGFace数据集在解决人脸识别领域的多个学术问题上发挥了关键作用。首先,它为研究人员提供了一个大规模、多样化的数据集,有助于解决数据不足和样本偏差的问题。其次,通过提供高质量的面部图像和详细的标注信息,VGGFace促进了深度学习模型在人脸识别任务中的应用和优化。此外,该数据集还推动了跨年龄和跨种族人脸识别技术的研究,为解决实际应用中的复杂问题提供了理论基础。
衍生相关工作
VGGFace数据集的发布催生了大量相关研究工作,推动了人脸识别技术的快速发展。例如,基于VGGFace的深度学习模型在多个国际竞赛中取得了优异成绩,进一步验证了其有效性。此外,研究人员还利用VGGFace进行跨领域研究,如情感分析和行为识别,扩展了其应用范围。这些衍生工作不仅丰富了计算机视觉领域的研究内容,也为实际应用提供了更多可能性。
以上内容由AI搜集并总结生成
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

学生课堂行为数据集 (SCB-dataset3)

学生课堂行为数据集(SCB-dataset3)由成都东软学院创建,包含5686张图像和45578个标签,重点关注六种行为:举手、阅读、写作、使用手机、低头和趴桌。数据集覆盖从幼儿园到大学的不同场景,通过YOLOv5、YOLOv7和YOLOv8算法评估,平均精度达到80.3%。该数据集旨在为学生行为检测研究提供坚实基础,解决教育领域中学生行为数据集的缺乏问题。

arXiv 收录

URPC系列数据集, S-URPC2019, UDD

URPC系列数据集包括URPC2017至URPC2020DL,主要用于水下目标的检测和分类。S-URPC2019专注于水下环境的特定检测任务。UDD数据集信息未在README中详细描述。

github 收录

AgiBot World

为了进一步推动通用具身智能领域研究进展,让高质量机器人数据触手可及,作为上海模塑申城语料普惠计划中的一份子,智元机器人携手上海人工智能实验室、国家地方共建人形机器人创新中心以及上海库帕思,重磅发布全球首个基于全域真实场景、全能硬件平台、全程质量把控的百万真机数据集开源项目 AgiBot World。这一里程碑式的开源项目,旨在构建国际领先的开源技术底座,标志着具身智能领域 「ImageNet 时刻」已到来。AgiBot World 是全球首个基于全域真实场景、全能硬件平台、全程质量把控的大规模机器人数据集。相比于 Google 开源的 Open X-Embodiment 数据集,AgiBot World 的长程数据规模高出 10 倍,场景范围覆盖面扩大 100 倍,数据质量从实验室级上升到工业级标准。AgiBot World 数据集收录了八十余种日常生活中的多样化技能,从抓取、放置、推、拉等基础操作,到搅拌、折叠、熨烫等精细长程、双臂协同复杂交互,几乎涵盖了日常生活所需的绝大多数动作需求。

github 收录

中国农村金融统计数据

该数据集包含了中国农村金融的统计信息,涵盖了农村金融机构的数量、贷款余额、存款余额、金融服务覆盖率等关键指标。数据按年度和地区分类,提供了详细的农村金融发展状况。

www.pbc.gov.cn 收录

Canadian Census

**Overview** The data package provides demographics for Canadian population groups according to multiple location categories: Forward Sortation Areas (FSAs), Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs), Federal Electoral Districts (FEDs), Health Regions (HRs) and provinces. **Description** The data are available through the Canadian Census and the National Household Survey (NHS), separated or combined. The main demographic indicators provided for the population groups, stratified not only by location but also for the majority by demographical and socioeconomic characteristics, are population number, females and males, usual residents and private dwellings. The primary use of the data at the Health Region level is for health surveillance and population health research. Federal and provincial departments of health and human resources, social service agencies, and other types of government agencies use the information to monitor, plan, implement and evaluate programs to improve the health of Canadians and the efficiency of health services. Researchers from various fields use the information to conduct research to improve health. Non-profit health organizations and the media use the health region data to raise awareness about health, an issue of concern to all Canadians. The Census population counts for a particular geographic area representing the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be 'non-permanent residents'. National Household Survey (NHS) provides demographic data for various levels of geography, including provinces and territories, census metropolitan areas/census agglomerations, census divisions, census subdivisions, census tracts, federal electoral districts and health regions. In order to provide a comprehensive overview of an area, this product presents data from both the NHS and the Census. NHS data topics include immigration and ethnocultural diversity; aboriginal peoples; education and labor; mobility and migration; language of work; income and housing. 2011 Census data topics include population and dwelling counts; age and sex; families, households and marital status; structural type of dwelling and collectives; and language. The data are collected for private dwellings occupied by usual residents. A private dwelling is a dwelling in which a person or a group of persons permanently reside. Information for the National Household Survey does not include information for collective dwellings. Collective dwellings are dwellings used for commercial, institutional or communal purposes, such as a hotel, a hospital or a work camp. **Benefits** - Useful for canada public health stakeholders, for public health specialist or specialized public and other interested parties. for health surveillance and population health research. for monitoring, planning, implementation and evaluation of health-related programs. media agencies may use the health regions data to raise awareness about health, an issue of concern to all canadians. giving the addition of longitude and latitude in some of the datasets the data can be useful to transpose the values into geographical representations. the fields descriptions along with the dataset description are useful for the user to quickly understand the data and the dataset. **License Information** The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes. **Included Datasets** - [Canadian Population and Dwelling by FSA 2011](https://www.johnsnowlabs.com/marketplace/canadian-population-and-dwelling-by-fsa-2011) - This Canadian Census dataset covers data on population, total private dwellings and private dwellings occupied by usual residents by forward sortation area (FSA). It is enriched with the percentage of the population or dwellings versus the total amount as well as the geographical area, province, and latitude and longitude. The whole Canada's population is marked as 100, referring to 100% for the percentages. - [Detailed Canadian Population Statistics by CMAs and CAs 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-cmas-and-cas-2011) - This dataset covers the population statistics of Canada by Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by FED 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-fed-2011) - This dataset covers the population statistics of Canada from 2011 by Federal Electoral District of 2013 Representation Order. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Health Region 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-health-region-2011) - This dataset covers the population statistics of Canada by health region. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. - [Detailed Canadian Population Statistics by Province 2011](https://www.johnsnowlabs.com/marketplace/detailed-canadian-population-statistics-by-province-2011) - This dataset covers the population statistics of Canada by provinces and territories. It is categorized also by citizen/immigration status, ethnic origin, religion, mobility, education, language, work, housing, income etc. There is detailed characteristics categorization within these stated categories that are in 5 layers. **Data Engineering Overview** **We deliver high-quality data** - Each dataset goes through 3 levels of quality review - 2 Manual reviews are done by domain experts - Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints - Data is normalized into one unified type system - All dates, unites, codes, currencies look the same - All null values are normalized to the same value - All dataset and field names are SQL and Hive compliant - Data and Metadata - Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters - Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated - Data Updates - Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted **Our data is curated and enriched by domain experts** Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts: - Field names, descriptions, and normalized values are chosen by people who actually understand their meaning - Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset - Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations - The data is always kept up to date – even when the source requires manual effort to get updates - Support for data subscribers is provided directly by the domain experts who curated the data sets - Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution. **Need Help?** If you have questions about our products, contact us at [info@johnsnowlabs.com](mailto:info@johnsnowlabs.com).

Databricks 收录