LabelGPT
Automated AI image annotation tool that generates labels from text prompts, supporting zero-shot labeling for ML teams.
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What is LabelGPT?
LabelGPT is an automated data annotation tool by Labellerr that turns raw images into labeled data in minutes using zero-shot label generation. Its foundation model-based engine enables ML teams to generate large volumes of labeled data without prior training examples, leveraging generative AI for super-fast automated labeling. The tool uses prompt-based labeling, where users provide text prompts with class names or object descriptions, and LabelGPT automatically generates annotations.
Key features include zero-shot labeling using simple text prompts, generation of labels for thousands of images/videos/PDFs/text/audio files in minutes, high-accuracy labels with confidence scores for review/validation, seamless data import from various sources (AWS, GCP, Azure, API), and direct integration into ML pipelines via SDK. LabelGPT supports multiple annotation types including bounding boxes, polygons, segmentation, landmarks/keypoints, lines, and ellipses. It leverages multiple foundation models like Dino and SAM for increased speed and accuracy, achieving up to 99% label accuracy.
LabelGPT is designed for machine learning researchers, AI software developers, big data engineers, data scientists, and ML teams working on vision model training, retail analytics, healthcare imaging, and LLM data preparation. It's suitable for businesses of all sizes from startups to large enterprises needing to scale labeling efforts. The tool reduces data preparation time by 90% and annotation costs by 80%, accelerating the AI development lifecycle significantly.
LabelGPT pricing
Pricing model: Free
Labellerr offers a freemium pricing model. The Researcher Plan is free for students and researchers, including 2,500 data credits (files), 1 seat, 1 workspace, all data type support (jpg, png, wav, mp4, txt), up to 100 projects, API access with SDK, and customer onboarding support (9am-6pm IST email). The StartUp Plan costs $600.00/month for 5 users. The Enterprise Plan costs $3,000.00/month for unlimited users. Pro plans range from $99/month for Team to custom Enterprise plans. Annual subscriptions include a 20% discount. Add-on seats are available at prices listed on the site.
LabelGPT pros
- Zero-shot labeling requires no prior training data or examples
- Label millions of images in minutes, world's fastest auto-labeling
- Simple text prompt-based interface eliminates complex labeling UIs
- 99% highAccuracy labels with confidence scores for validation
- 90% reduction in data preparation time
- 80% reduction in annotation costs
- Supports multiple data types: images, videos, PDFs, text, audio
- Multiple annotation types: bounding box, polygon, segmentation, keypoint, line, ellipse
- Leverages multiple foundation models (Dino, SAM, SAM 2, SAM 3) for accuracy
- Seamless cloud integration: AWS, GCP, Azure data connection
- API and SDK access for ML pipeline integration
- Export to multiple formats: CSV, JSON, COCO, Pascal VOC, custom
- No complex interfaces reduces labeling errors
- Smart feedback loop for quality understanding and adjustments
- Open dataset library accelerates annotation process
- Pre-labeling solutions automate common annotation tasks
- Real-time progress tracking for team collaboration
- MLOps integration with GCP Vertex AI and AWS SageMaker
- Intuitive interface for easy task creation and assignment
- Available for students and researchers with free plan
LabelGPT cons
- Limited customization options for specialized labeling needs
- Potential accuracy issues with AI-generated labels
- Dependence on prompt input quality affects results
- May not suit highly complex or unusual datasets
- Less accurate than thorough manual labeling for critical tasks
- Requires prompt expertise for optimal zero-shot performance
- Paid plans start at $600/month, expensive for small teams
- Enterprise plans up to $3,000/month for unlimited users
- Free tier limited to 2,500 data credits and 1 seat
- May need human review/validation for confidence scores
Frequently asked questions about LabelGPT
What is LabelGPT?
LabelGPT is a zero-shot labeling system and automated data annotation platform that generates labels for images using OpenAI's GPT-3 technology fine-tuned for labeling tasks. It leverages multiple foundation models like Dino and SAM to enable ML teams to generate large volumes of labeled data without prior training examples, simply by providing text prompts with class names.
How does zero-shot labeling work in LabelGPT?
Zero-shot labeling in LabelGPT allows users to generate accurate labels without any prior labeled examples. Users provide text prompts describing the classes or objects they need, and the system's hybrid deep learning models automatically generate annotations with high confidence scores. This eliminates the need to collect labeled data or fine-tune models for each use case.
What data types does LabelGPT support?
LabelGPT supports diverse data types including images, videos, PDFs, text files, and audio files. Users can import data from various sources and the tool generates labels for thousands of these files in mere minutes, making it versatile for different ML project needs.
What annotation types are available in LabelGPT?
LabelGPT supports multiple annotation types: Bounding Box for object localization, Polygon for outlining complex/irregular shapes, Line, Segmentation (Segment Anything), Landmark/Keypoint for specific points, and Ellipse. This accommodates different data annotation needs regardless of data complexity.
How accurate are LabelGPT's generated labels?
LabelGPT achieves high-accuracy labels with up to 99% accuracy. The generated labels include confidence scores that enable efficient review and validation by human annotators, allowing teams to focus on verification rather than manual labeling from scratch.
How do I connect my data to LabelGPT?
Users can connect their image and video datasets from cloud services like AWS, GCP, Azure, or through API to the Labellerr platform. The high-performance SDK allows programmatically fetching and syncing high-quality data to streamline custom AI workflows and automate real-time data fetching.
What export formats does LabelGPT support?
LabelGPT exports labeled data in multiple formats including CSV, JSON, COCO, Pascal VOC, and custom formats. This makes it easy to push labeled data directly to machine learning training engines without additional format conversion.
Is LabelGPT suitable for startups and large enterprises?
Yes, LabelGPT is perfect for businesses of all sizes. Startups can streamline their data labeling process with its intuitive interface, while large enterprises can scale their labeling efforts. The platform supports creating labeling tasks, assigning them to team members, and tracking progress in real-time.
How does LabelGPT integrate with ML pipelines?
LabelGPT integrates seamlessly into ML pipelines through API access and a high-performance Python SDK. It offers streamlined MLOps integration with existing AI development environments including GCP Vertex AI, AWS SageMaker, and custom environments, enabling programmmatic data sync to automate workflows.
What is the cost savings with LabelGPT compared to traditional labeling?
LabelGPT drastically reduces annotation costs by up to 80% compared to traditional annotation methods. It also reduces data preparation time by 90%, accelerating the AI development lifecycle. This cost-effective development makes it economical for teams building and deploying AI models faster.