Labelgpt

What is Labelgpt?

LabelGPT is an automated image annotation tool powered by a generative AI model. It generates labels on raw images by taking class or object names as a text prompt and then using its generative AI model to detect and segment the label on the related image. LabelGPT supports importing data from various sources, including local platforms and cloud sources like AWS, GCP, and Azure. The zero-shot label generation engine in LabelGPT automates the creation of automatic labels on images, increasing efficiency and speeding up the label generation process. LabelGPT contributes to the Machine Learning pipeline by allowing the export of generated labels directly into ML models, aiding in training and development.

LabelGPT assists in reviewing labels by allowing users to filter based on confidence scores and visually verify the output. It supports the detection and segmentation of labels using generative AI models and text prompts for labeling, enabling swift image annotation with high-quality results. LabelGPT speeds up the labeling process by automating labeling, reducing manual intervention, and cutting down on annotation costs. The tool allows for annotations to be utilized in vision model training and supports cloud integration with platforms like AWS, GCP, and Azure.

Furthermore, LabelGPT's features include automated annotation, active learning-based labeling automation, multiple data type support, smart quality assurance, efficient integration with ML operations, advanced analytics for project management, and 24/7 technical support. The tool prioritizes security and privacy with encryption, authentication, access controls, and monitoring to ensure confidential and secure data handling.

Who created Labelgpt?

LabelGPT was created by Puneet, the Co-founder & CEO of Labellerr. Labellerr was launched in 2018 after Puneet spent 7 years leading ML teams and identified data preparation as a key bottleneck in computer vision AI workflows. The company aims to automate every stage of the computer vision workflow and provides a platform for enterprise ML teams to collaborate, ensure data quality, and reduce project timelines in industries like automotive, medical imaging, and manufacturing.

What is Labelgpt used for?

• Exports to ML training engine
• Multi-platform (cloud, local) data access
• Foundation model powered labeling
• No manual labeling required
• Fast label generation
• Saves time in labeling
• Visual label validation
• Allows dataset selection
• Prompt for class/object labeling
• Enables data upload
• Visual result verification
• Direct ML pipeline integration
• Reduces annotation costs
• Speeds up labeling process
• Zero-shot learning
• Text prompts for labeling
• Image segmentation capabilities
• Prompt-based class/object detection
• High-quality label generation

Who is Labelgpt for?

• Automated image annotators
• Machine Learning teams in automotive, medical imaging, and manufacturing
• Machine learning engineer
• Data Scientist
• Computer Vision Engineer
• AI Researcher
• Data Annotation Specialist
• Machine learning engineers
• Data scientists
• AI researchers
• Computer Vision Engineers
• Automotive professionals
• Medical imaging professionals
• Manufacturing Professionals
• Data Analysts

How to use Labelgpt?

To use LabelGPT, follow these steps:

1. Import Data: Data can be imported from various sources like local platforms or cloud services such as AWS, GCP, and Azure, as well as through APIs.

2. Label Generation: Enter class or object names as text prompts for LabelGPT to generate labels on raw images automatically.

3. Review Labels: Validate label quality by filtering based on high confidence scores and visually verifying the results.

4. Integration: LabelGPT integrates directly into Machine Learning pipelines by exporting the generated labels for model training.

5. Zero-Shot Label Generation: Utilize the zero-shot label generation engine to automate label creation, drastically reducing manual intervention.

6. Support: Benefit from LabelGPT's cloud integration, fast labeling, visual validation, and zero-shot learning capabilities.

LabelGPT stands out for its ability to automate image annotation, utilize foundation models, generate labeled data swiftly, validate quality efficiently, and provide straightforward integration into Machine Learning workflows. By leveraging its features like direct ML pipeline integration, zero-shot label generation, and support for various data types, LabelGPT streamlines and accelerates the labeling process while reducing costs and enhancing productivity.