Struggling to choose between Computer Vision Annotation Tool (CVAT) and Prodigy ML? Both products offer unique advantages, making it a tough decision.
Computer Vision Annotation Tool (CVAT) is a Ai Tools & Services solution with tags like image-annotation, video-annotation, computer-vision, open-source.
It boasts features such as Image, video and 3D point cloud annotation, Multiple user management with different roles, Predefined tags and automatic annotation, Interpolation of bounding boxes across frames, Review and acceptance workflows, REST API, Integration with deep learning frameworks and pros including Open source and free, Active development and support community, Powerful annotation capabilities, Collaborative workflows, Integrates with popular ML/DL frameworks.
On the other hand, Prodigy ML is a Ai Tools & Services product tagged with machine-learning, data-labeling, computer-vision, nlp.
Its standout features include Active learning to prioritize labeling, Pre-built templates for common tasks, Real-time model evaluation, Team collaboration, API access, Integrations with popular ML frameworks, and it shines with pros like Speeds up model training, Reduces need for large labeled datasets, Intuitive interface, Works for image, text, audio and other data types.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
CVAT is an open source computer vision annotation tool for labeling images and video. It allows for collaborative annotation of datasets with features like predefined tags, interpolation of bounding boxes across frames, and review/acceptance workflows.
Prodigy ML is an annotation tool that helps train machine learning models faster. It allows users to rapidly label datasets and build accurate models with less data.