Auto-Labeling Feature: Supercharge Your Data Labeling with AI-Powered Auto-Labeling
Introducing Epigos AI's new auto-labeling feature! This game-changing tool leverages state-of-the-art models to streamline data annotation for object detection and image classification tasks.
3 min read
We're excited to announce a major leap forward in our platform with the release of the Auto-Labeling feature! This groundbreaking feature utilizes state-of-the-art (SOTA) models like GroundingDINO, Florence-2 (from Microsoft), and PaliGemma (from Google) to significantly reduce the manual effort required for data labeling, streamlining your workflow and accelerating your AI model development.
What is Auto-Labeling?
Auto-Labeling leverages the power of cutting-edge AI models to automatically identify object and label them in the images within your Epigos AI datasets.
This empowers you to:
- Reduce Labeling Time: Automate repetitive tasks and significantly speed up the data labeling process.
- Improve Labeling Accuracy: Utilize the insights of SOTA models to ensure high-quality labeled data for your AI models.
- Focus on Complexities: Free up valuable time for your team to focus on refining labels for nuanced cases requiring human expertise.
How Does Auto-Labeling Work?
Epigos AI's Auto-Labeling is designed for efficiency and ease of use:
- Click the Auto-Label Button: Initiate the labeling process for a single image or a batch of images.
- Select the AI Model: Choose the SOTA model best suited for your specific labeling needs.
- Provide Prompt Labels (Optional): Offer guidance to the model by providing a few initial labels to focus its analysis.
- Review and Approve: The model will suggest labels. You can then review and approve them with ease.
Demo of auto labelling on Epigos AI.
Unveiling the Powerhouse Models:
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GroundingDINO: This impressive model excels at general image understanding, providing a strong foundation for auto-labeling tasks. You can find the research paper here: Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.
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Florence-2 by Microsoft: This innovative vision-language model combines visual understanding with natural language processing, offering even more accurate and insightful label suggestions. Explore the research paper here: Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks.
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PaliGemma by Google AI: Another powerful vision-language model from Google AI, PaliGemma enhances auto-labeling by leveraging textual cues to improve object recognition and labeling accuracy. Learn more from their blog: PaliGemma.
Accessible to All Users:
The Auto-Labeling feature is available on all Epigos AI subscription plans, including the free tier! This ensures everyone can benefit from this powerful tool and experience faster, more efficient data labeling.
Get Started Today!
Ready to revolutionize your data labeling process with AI-powered auto-labeling? Learn more about Epigos AI's auto label.
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