foundational models - Labellerr

foundational models

A collection of 5 posts

Zero-Shot Performance Of CLIP Over Food Classification Dataset: Here Are The Findings

CLIP's Performance On Food Dataset: Here Are The Findings

The CLIP (Contrastive Language–Image Pre-training) model represents a groundbreaking convergence of natural language understanding and computer vision, allowing it to excel in various tasks involving images and text. Its foundation is rooted in zero-shot transfer learning, which empowers the model to make accurate predictions on entirely new classes or

LabelGPT: Revolutionizing Computer Vision with AI-Powered Data Annotation

Elevating ML: AI-Driven Data Annotation with LabelGPT

Table of Contents 1. Introduction 2. Pre-requisite Concepts for Automated Data Labelling 3. About Foundation Models 4. Overview of Zero-Shot Learning and Its Role in Automated Data Labeling 5. Generative AI-Powered Prompt-Based Data Labeling 6. LabelGPT 7. How LabelGPT Works 8. Benefits of LabelGPT 9. Conclusion 10. Frequently Asked Questions

Insights And Best Practices For Building and Deploying Computer Vision Models

Insights And Best Practices For Building and Deploying Computer Vision Models

Building and deploying computer vision (CV) models require a deep understanding of both the theoretical aspects of computer vision and the practical challenges of developing robust and efficient systems. Computer Vision Engineers play a critical role in this process, leveraging their expertise to design, train, and deploy CV models that

computer vision

Foundation Models for Image Search: Enhancing Efficiency and Accuracy

Foundation models like CLIP and GLIP are transforming the way businesses use AI for visual search. These advanced systems leverage multimodal capabilities to enhance image search model accuracy by linking text and visuals, making searches faster, more precise, and highly adaptable.

Beginner's Guide to Using Foundation Models in ML Projects

Beginner's Guide: Using Foundation Models in ML Projects

Foundation models, like GPT-3 and BERT, simplify ML projects by providing pre-trained models that can be fine-tuned for specific tasks, reducing development time. This guide covers setup, fine-tuning, and deployment to help you get started effectively.