Datature
Developer Tools
Maximize Your Reach: Unleashing the Potential of Promote Your Tool
Datature's platform consists of four main components:
- Nexus: An end-to-end no-code MLOps platform that allows you to collaborate, annotate, train, and deploy multiple computer vision models on a single platform. You can import images and annotations from various sources, use advanced annotation tools and AI-assisted labelling, customize training workflows and hyperparameters, evaluate model performance with interactive metrics and visualisations, and deploy models via API with a single click.
- IntelliBrush: A feature of Nexus that helps you create pixel-perfect annotated images of all formats 10x faster. IntelliBrush uses AI to outline even the most challenging objects with a few clicks. You can specify the level of granularity and refine objects to suit your needs. IntelliBrush supports masks, polygons, and bounding boxes.
- Optimised Algorithms and Libraries: Datature implements state-of-the-art algorithms and libraries for computer vision, such as FasterRCNN, YOLOX, and many more. You can build models with research-tested methods without writing any code.
- Reliable Model Deployment: Datature provides a secure, compliant, and scalable infrastructure for deploying your computer vision models. You can deploy models via API with a single click and access them from anywhere. Datature handles the hosting, scaling, monitoring, and updating of your models.
Datature is used by thousands of teams across various industries, such as construction, healthcare, retail, agriculture, and more. Datature helps you build better computer vision products faster and easier.
Pros
- Datature simplifies the workflow to annotate, train, visualise, and deploy computer vision models.
- Datature offers a no-code MLOps platform that empowers non-technical users to build computer vision applications.
- Datature provides a suite of annotation tools, including IntelliBrush, which is an AI-assisted labelling tool that creates pixel-perfect masks.atature supports state-of-the-art algorithms and libraries for model training, such as FasterRCNN and YOLOX.
- Datature enables reliable model deployment via API with secure, compliant, and scalable infrastructure.
Cons
- Datature requires internet connection and data locality to store and compute images on the fly.
- Datature may not support customised or complex model architectures that require coding or scripting.
- Datature may not have enough image augmentations or hyperparameters to fine-tune the model performance.
- Datature may not provide enough interactive metrics or visualisations to evaluate the model performance.
- Datature may not have enough use-cases or examples to inspire or guide the users in different domains.