The paper introduces the VGG Image Annotator (VIA), a lightweight, standalone, and offline manual annotation tool for images, audio, and video. developed by the Visual Geometry Group (VGG) at the University of Oxford. VIA is written using HTML, Javascript, and CSS, and runs in modern web browsers without installation. It allows human annotators to define spatial regions in images and temporal segments in audio and video, which can be exported to JSON or CSV formats for further processing. VIA supports collaborative annotation of large datasets and is available under a BSD open-source license. The software has been widely adopted in various academic and industrial applications, including image annotation for face detection, speaker diarization in videos, and identifying objects in microscopy images. VIA's minimalist design and rigorous testing have made it user-friendly and configurable. The software has been used over 1,000,000 times as of July 2019. Future developments include enhanced collaborative annotation capabilities and the integration of computer vision plugins to assist in the annotation process. The VIA project continues to evolve based on user feedback and contributions from the open-source community.The paper introduces the VGG Image Annotator (VIA), a lightweight, standalone, and offline manual annotation tool for images, audio, and video. developed by the Visual Geometry Group (VGG) at the University of Oxford. VIA is written using HTML, Javascript, and CSS, and runs in modern web browsers without installation. It allows human annotators to define spatial regions in images and temporal segments in audio and video, which can be exported to JSON or CSV formats for further processing. VIA supports collaborative annotation of large datasets and is available under a BSD open-source license. The software has been widely adopted in various academic and industrial applications, including image annotation for face detection, speaker diarization in videos, and identifying objects in microscopy images. VIA's minimalist design and rigorous testing have made it user-friendly and configurable. The software has been used over 1,000,000 times as of July 2019. Future developments include enhanced collaborative annotation capabilities and the integration of computer vision plugins to assist in the annotation process. The VIA project continues to evolve based on user feedback and contributions from the open-source community.