yolo

The author introduces 'llmog,' a tool that uses LLMs for auto-annotation of datasets, running locally with llama.cpp or vllm, or via external API, for users who prefer a UI over coding.

This paper proposes a YOLO- and CLIP-based vision-language framework to classify mosquito flight frames for Dengue virus detection, achieving 98.54% accuracy and 99.91% sensitivity at frame level, with complete video-level performance after temporal aggregation.

Tencent researchers propose YOLO-PEFT, a structure-aware framework that treats adapter placement as constraint planning, outperforming full fine-tuning on YOLO detectors while using significantly fewer trainable parameters.

YOLO-PEFT is a structure-aware framework that formulates adapter placement as constraint planning for parameter-efficient fine-tuning of YOLO detectors, achieving better mAP than full fine-tuning with reduced memory.

The author implemented YOLO26n model inference from scratch using ARM64 assembly language without any external frameworks, demonstrating low-level AI inference techniques.

CommuniWave is a machine learning model introduced to quantify the Degree of Informal Behavior (DIB) in urban communities using behavior capture, YOLOv10, and random forest, enabling dynamic monitoring for urban managers.

Introduces MatMMExtract pipeline to decompose compound scientific figures into panels and annotate them using LLMs, creating the MatSciFig dataset of over 390,000 image-text pairs for vision-language learning in materials science.

A self-improving GUI agent that learns from its mistakes, using a perception stack with YOLO and OCR, calling LLM only when needed, and running locally. It's open source under the MIT license.

Proposes AE-YOLO, an attention-guided autoencoder-enhanced YOLO framework for robust insulator defect detection in UAV transmission-line imagery, achieving 95.10% [email protected] and outperforming YOLO baselines by 5 points.

Ultralytics YOLO26 introduces a unified real-time vision model family with NMS-free inference, improved training strategies, and multi-task capabilities for detection, segmentation, and pose estimation, achieving state-of-the-art accuracy-latency trade-offs.

BenjaminDEKR highlights Cybercab Spotter, who uses YOLO on public traffic cameras to detect Tesla Cybercabs, calling it a clever application of AI vision.

The RF-DETR model proposed at ICLR2026 combines Transformer's high accuracy with real-time performance, achieving high scores in 100 real-world scenarios and offering sizes from Nano to 2XL, potentially replacing YOLO in real-time detection.

A social media post expresses excitement about the return or renewed relevance of the YOLOv3 object detection model.

Article concerning YOLO, the widely used real-time object detection model family.

A user is seeking advice on improving their object detection model trained with YOLO11n for deployment on a Raspberry Pi 5, struggling with the gap between theoretical mAP50 metrics and practical detection performance.