The text discusses various image labeling tools used in machine learning projects, particularly those that serve as alternatives to the VGG Image Annotator (VIA). The article reviews 11 top image annotation tools, each with its unique features, pros, and cons, to help data scientists and computer vision teams choose the best tool for their specific needs. The criteria for evaluating these tools include usability and interface, annotation features, integration and compatibility, collaboration and version control, and pricing. Each tool is described in detail, highlighting its strengths and weaknesses, and providing information on whether it's suitable for small projects, enterprise environments, or has specific security requirements. By considering factors such as project scale, team capabilities, budget, and the need for automation features and AI assistance, users can make informed decisions about which image annotation tool to use.