Labelgpt

What is LabelGPT?

LabelGPT is an image annotation tool that automatically labels images using a generative AI model. You provide a text prompt with the names of the classes or objects you’re looking for, and the AI finds and outlines them in your images. Data can be pulled from your local machine or cloud services like AWS, GCP, and Azure.

Its zero-shot label generation engine creates labels automatically without any prior examples, which speeds up the annotation process considerably. Generated labels can be exported directly into your ML models for training and development.

LabelGPT also simplifies label review. You can filter labels by confidence scores and visually verify results. By automating annotation, it reduces manual work and lowers annotation costs.

Key features include:

• Automated annotation powered by foundation models
• Smart automation based on active learning
• Support for multiple data types
• Built-in quality checks
• Integration with ML operations pipelines
• Detailed analytics for project management
• Round-the-clock technical support
• Security measures including encryption, authentication, access controls, and monitoring

Who created LabelGPT?

LabelGPT was created by Puneet, Co-founder & CEO of Labellerr, which he launched in 2018. Before that, he spent seven years leading ML teams and saw that data preparation was a major bottleneck in computer vision AI projects. Labellerr aims to automate every step of the computer vision workflow. The platform targets enterprise ML teams, helping them collaborate, maintain data quality, and shorten project timelines in industries such as automotive, medical imaging, and manufacturing.

What is LabelGPT used for?

• Prompt-based labeling: Provide text prompts to label specific classes or objects.
• Zero-shot learning: Label new classes or objects without prior examples.
• No manual labeling required: Automates the annotation process end to end.
• Multi-platform data access: Work with data stored locally or on cloud services.
• Dataset selection and upload: Choose specific datasets and upload image data to the platform.
• Visual validation: Verify labels and results visually, with confidence-score filtering.
• Direct ML pipeline integration: Export labeled data straight to your ML training systems.
• Time and cost savings: Reduces the time and expense of manual annotation.