Ai in Security and surveillance How License Plate Recognition Works? Automatic License Plate Recognition (ALPR) explains how computer vision detects, cleans, and reads vehicle license plates using classical image processing and OCR, revealing the challenges and real-world techniques behind modern traffic and security systems.
computer vision Power Grid Inspection using Computer Vision Manual power grid inspections are risky and slow. Discover how Computer Vision and drones are transforming utility maintenance. This guide explores how AI automates defect detection, ensures worker safety, and enables predictive maintenance to prevent outages before they happen.
CV Use Case Count Different Types of Blood Cell using CV and Labellerr Computer vision automates RBC and WBC detection, replacing manual microscopy with fast, accurate, and consistent analysis. Using Labellerr’s annotation tools and COCO export, experts can create high-quality datasets and train reliable YOLO models for automated blood cell counting.
CV Use Case Building a Egg Cell Detection System using Labellerr and YOLO Computer vision automates egg cell detection, segmentation, and tracking in microscopic videos. With Labellerr’s AI-assisted annotation and automatic propagation, researchers can build accurate YOLO-based segmentation models quickly and scale their analysis efficiently.
CV Use Case Building a Cell Segmentation System using Labellerr and YOLO Computer vision brings speed and accuracy to cell segmentation, processing dense microscopy images rapidly. With Labellerr’s SAM-powered annotation and collaboration tools, researchers can build scalable, reliable AI models for biomedical imaging.
SAM SAM Fine-Tuning Using LoRA Learn how to fine‑tune SAM with LoRA to achieve precise, domain‑specific segmentation without massive GPU costs. Freeze the SAM backbone, train only tiny low‑rank adapters, and deploy high‑accuracy models on modest hardware—fast, modular, and efficient.
Vision-language models How to Fine-Tune Llama 3.2 Vision On a Custom Dataset? Unlock advanced multimodal AI by fine‑tuning Llama 3.2 Vision on your own dataset. Follow this guide through Unsloth, NeMo 2.0 and Hugging Face workflows to customize image‑text reasoning for OCR, VQA, captioning, and more.
LLMs LLMs & Reasoning Models: How They Work and Are Trained! LLMs reason by analyzing data, applying logic, and solving problems step by step. They are trained with structured datasets, prompting techniques, and reinforcement learning.
Voicebot Fine Tuning 5 Best Voicebot Fine Tuning Tools in 2026 In 2025, top voicebot fine-tuning tools include Labellerr, Label Studio, Labelbox, Kili, and Databricks Lakehouse. These platforms offer customizable workflows, multi-format support, collaborative annotation, and seamless ML integration.
Complete Guide On Fine-Tuning LLMs using RLHF Explore how LLM reinforcement learning with human feedback (RLHF) fine-tunes large language models, improving natural language responses and optimizing AI behavior through rewards, human input, and iterative learning for enhanced model performance.
segment anything Fine-Tuning Segment Anything Model (SAM) Learn how to fine-tune Segment Anything Model (SAM) for precise image segmentation tasks. Discover techniques like one-shot learning to fine-tune SAM efficiently and improve performance on specific datasets, ensuring high accuracy with limited data.
Tutorial To Leverage Open AI's CLIP Model For Fashion Industry Discover how fine-tuning CLIP model can revolutionize fashion image recognition. Learn to optimize OpenAI's CLIP with domain-specific data for the fashion industry.
Multimodal AI Evaluation and Fine-Tuning for Image Captioning Models - A Case Study Table of Contents 1. Evaluation Metrics 2. Fine-Tuning Strategies for Image Captioning Models 3. Challenges in Image Captioning 4. Case Study 5. Conclusion 6. FAQ Image captioning models are sophisticated AI systems designed to generate descriptive text for images automatically. These models play a crucial role in various applications, such
