Imagine being able to separate the foreground from the background in your videos with clear, accurate mattes every time. With AI models like MatAnyone, video matting delivers precise alpha mattes using consistent memory banks, ensuring smooth and reliable results across frames. This advancement opens new possibilities for video editors, filmmakers, and AI enthusiasts interested in […] The post MatAnyone Explained: Consistent Memory for Better Video Matting first appeared on LearnOpenCV ..read more

GraphRAG is a pivotal research from Microsoft improving the shortcomings of naive RAG by employing structured Knowledge graph which includes entities, relations, claims etc, for traceability by traversing multi-hop nodes. The post GraphRAG: Now Faster and Cost-Effective for Medical Document Analysis  first appeared on LearnOpenCV ..read more

In this article, we explore OmniParser a UI screen parsing pipeline combining fine-tuned YOLO model for icon detection and Florence2 for icon recognition and icon description generation. The post OmniParser: Vision Based GUI Agent first appeared on LearnOpenCV ..read more

Object detection has undergone tremendous advancements, with models like YOLOv12, YOLOv11, and YOLOv7-based DarkNet leading the way in real-time detection. While these models perform exceptionally well on general object detection datasets, fine-tuning YOLOv12 on HRSC2016-MS (High-Resolution Ship Collections) presents unique challenges. This article provides a detailed end-to-end pipeline for fine-tuning YOLOv12, YOLOv11, and YOLOv7-based DarkNet […] The post Fine-Tuning YOLOv12: Comparison with YOLOv11 & YOLOv7-Based DarkNet first appeared on LearnOpenCV ..read more

A comprehensive step-by-step guide on fine-tuning RetinaNet using PyTorch to achieve 79% accuracy on wildlife detection tasks. In this tutorial, we dive deep into RetinaNet’s architecture, explain the benefits of Focal Loss, handle class imbalance, and demonstrate practical tips for efficient fine-tuning—even with limited GPU resources. Plus, we benchmark our RetinaNet model against YOLO11 to showcase key improvements in precision! Perfect for anyone interested in applying cutting-edge deep learning to real-world wildlife conservation problems. The post FineTuning RetinaNet for Wildlife Detect ..read more

DUSt3R (Dense and Unconstrained Stereo 3D Reconstruction) introduces a novel paradigm in multi-view 3D reconstruction, eliminating the need for predefined camera poses and intrinsics. In this article let's understand DUSt3R architecture and do a one-on-one comparison to COLMAP MVS, DUSt3R clearly outperforms other baselines in terms of output quality and inference time. The post DUSt3R: Geometric 3D Vision Made Easy : Explanation and Results first appeared on LearnOpenCV ..read more

Real-time object detection has become essential for many practical applications, and the YOLO (You Only Look Once) series by Ultralytics has always been a state-of-the-art model series, providing a robust balance between speed and accuracy. The inefficiencies of attention mechanisms have hindered their adoption in high-speed systems like YOLO. YOLOv12 aims to change this by integrating attention […] The post YOLOv12: Attention Meets Speed first appeared on LearnOpenCV ..read more

Video generation models using the diffusion based approach for training are a significant advancement in the domain of Generative AI. Models like SORA and Veo 2 take the idea of creating images and apply them to generating video content, changing how we create and use digital media. They help create videos that show realistic human […] The post Video Generation: A Diffusion based approach first appeared on LearnOpenCV ..read more

AI, being no longer confined to passive algorithms, is transforming itself into autonomous agents that can perceive, reason, and act with increasing intelligence. These agents are designed to navigate uncertainty, adapt to changing conditions, and exhibit common sense, marking a significant leap toward true machine intelligence. The rapid evolution of AI has ushered in an […] The post Agentic AI: A Comprehensive Introduction first appeared on LearnOpenCV ..read more

Leaf diseases reduce crop yields and impact food security. Finetuning SAM2 helps detect and segment diseased areas using deep learning. With a small dataset, we achieved 74% IoU, making early disease detection possible. Try the code, fine-tune it, and improve results. #AI #DeepLearning #Agriculture The post Finetuning SAM2 for Leaf Disease Segmentation first appeared on LearnOpenCV ..read more