If you need a platform that lets you set up custom data workflows and perform advanced image manipulation for high-productivity data labeling, V7 is worth a close look. V7 is a machine learning development platform that automates tasks and optimizes data labeling, which can reduce labeling costs up to 80% and automate tasks up to 90%. It includes Auto-Annotate, Custom Data Workflows, Advanced Image Manipulation, Video Annotation, and support for a range of industry regulations, making it well-suited for industries like Healthcare, Insurance, and Finance.
Another good option is Encord, a full-stack data development platform for building predictive and generative computer vision applications. Encord includes Annotate for a range of annotation types and custom workflows, Active for monitoring and debugging model performance, and Index for data management and exploration. It integrates well with storage and MLOps tools and supports security standards, making it a powerful option for improving model performance and shortening annotation time.
For a more complete solution, SuperAnnotate offers an end-to-end platform for training, evaluating, and deploying AI models with high-quality datasets. It can handle a range of data types, including images, videos, text and audio, and offers a customizable UI, advanced AI tools and a global marketplace for annotation teams. With features like data integration, customizable layouts and advanced data insights, SuperAnnotate is designed to accelerate AI development while maintaining data security and quality.
Last, Label Studio offers a flexible data labeling tool that handles multiple data types and offers customizable layouts, ML-assisted labeling and integration with cloud storage systems. It's open-source and free, with an enterprise version offering more features. Label Studio is used by data scientists and companies of all sizes, with a large community and abundance of support resources.