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AIWalker
AIWalker
Mar 23, 2026 · Artificial Intelligence

Dynamic Dense Computing and Minimal End‑to‑End Design: YOLO-Master & YOLO26

By introducing a dynamic mixture‑of‑experts routing scheme and an end‑to‑end architecture that eliminates NMS and DFL, YOLO‑Master and YOLO26 dramatically cut compute waste and latency on edge devices, achieving up to 43% faster CPU inference while keeping model accuracy, with all code openly released.

Computer VisionMixture of ExpertsModel Optimization
0 likes · 7 min read
Dynamic Dense Computing and Minimal End‑to‑End Design: YOLO-Master & YOLO26
AIWalker
AIWalker
Mar 7, 2026 · Artificial Intelligence

YOLO-Master v2026.02 Unveils Four Innovations for SOTA Object Detection

Tencent’s YOLO-Master v2026.02 adds a Mixture‑of‑Experts architecture, zero‑overhead LoRA fine‑tuning, Sparse SAHI inference for large images, and Cluster‑Weighted NMS, delivering 3‑5× faster inference, up to 70% reduced training resources, and markedly higher detection accuracy across diverse benchmarks.

Computer VisionLoRAMixture of Experts
0 likes · 15 min read
YOLO-Master v2026.02 Unveils Four Innovations for SOTA Object Detection
AI Algorithm Path
AI Algorithm Path
Mar 4, 2026 · Artificial Intelligence

Beginner’s Guide: Building a Pedestrian Detection Skill with NanoBot

This step‑by‑step tutorial shows how to install NanoBot, configure it with a DeepSeek API key, create a YOLO‑based pedestrian detection skill via natural‑language commands, test the generated code, and extend the output to JSON, demonstrating AI agents in Python.

AI AgentDeepSeekNanobot
0 likes · 6 min read
Beginner’s Guide: Building a Pedestrian Detection Skill with NanoBot
Code Mala Tang
Code Mala Tang
Mar 1, 2026 · Artificial Intelligence

Why YOLO Dominates Real-Time Object Detection: A Complete Guide

This article provides a comprehensive overview of the YOLO (You Only Look Once) algorithm, explaining its core principles, architecture, version history, training workflow, real‑world applications, strengths, and current limitations for modern computer‑vision tasks.

Computer VisionDeep LearningReal-Time
0 likes · 9 min read
Why YOLO Dominates Real-Time Object Detection: A Complete Guide
AIWalker
AIWalker
Aug 19, 2025 · Artificial Intelligence

Easy Ways to Boost YOLO: Systematic Review of Versions and Use Cases

This article systematically reviews every YOLO version, classifies five major improvement directions—architecture enhancements, efficiency optimizations, multi‑task learning, temporal modeling, and domain‑specific customizations—provides concrete paper references, code links, and dataset resources to help researchers and engineers quickly locate and apply the most effective techniques.

Deep LearningYOLOmodel improvement
0 likes · 8 min read
Easy Ways to Boost YOLO: Systematic Review of Versions and Use Cases
AIWalker
AIWalker
May 14, 2025 · Artificial Intelligence

How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters

This paper presents HGO‑YOLO, a lightweight real‑time anomaly‑behavior detector that integrates HGNetv2 and GhostConv into YOLOv8, achieving 87.4% mAP with just 4.6 MB of parameters and 56 FPS on CPU, and validates its performance across multiple datasets and hardware platforms.

Computer VisionLightweight ModelsYOLO
0 likes · 25 min read
How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters
160 Technical Team
160 Technical Team
Jul 29, 2024 · Artificial Intelligence

How YOLO Transforms Medical Report Screening and Occlusion Detection

Leveraging the YOLO family of deep‑learning models, this study demonstrates efficient filtering of irrelevant medical images, accurate classification of textual reports, and robust detection of occluding objects, achieving high precision and speed on both CPU and GPU, while outlining training details, performance metrics, and future improvements.

Deep LearningYOLOmedical imaging
0 likes · 17 min read
How YOLO Transforms Medical Report Screening and Occlusion Detection
DataFunTalk
DataFunTalk
Oct 2, 2023 · Artificial Intelligence

DAMO-YOLO: A High‑Efficiency, High‑Accuracy Object Detection Framework

DAMO‑YOLO is an open‑source, high‑speed and high‑precision object detection framework that leverages MAE‑NAS for low‑cost model customization, Efficient RepGFPN and HeavyNeck for enhanced multi‑scale detection, and a universal distillation technique to boost performance across model scales.

Efficient RepGFPNMAE-NASYOLO
0 likes · 15 min read
DAMO-YOLO: A High‑Efficiency, High‑Accuracy Object Detection Framework
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Aug 17, 2023 · Artificial Intelligence

Getting Started with YOLOv8 on the Ultralytics Platform: Installation, Command‑Line Usage, and Model Training

This article introduces the YOLOv8 object‑detection framework on the Ultralytics platform, covering environment setup, command‑line and Python APIs for inference, model‑file options, result interpretation, data annotation, training procedures, and exporting models to various deployment formats.

Computer VisionModel TrainingPython
0 likes · 14 min read
Getting Started with YOLOv8 on the Ultralytics Platform: Installation, Command‑Line Usage, and Model Training
DataFunTalk
DataFunTalk
Apr 25, 2023 · Artificial Intelligence

DAMO-YOLO: An Efficient Target Detection Framework with NAS, Multi‑Scale Fusion, and Full‑Scale Distillation

This article introduces DAMO‑YOLO, a high‑performance object detection framework that combines low‑cost model customization via MAE‑NAS, an Efficient RepGFPN with HeavyNeck for superior multi‑scale detection, and a full‑scale distillation technique, delivering faster inference, lower FLOPs, and higher accuracy across diverse industrial scenarios.

DistillationModel OptimizationNAS
0 likes · 15 min read
DAMO-YOLO: An Efficient Target Detection Framework with NAS, Multi‑Scale Fusion, and Full‑Scale Distillation
Baidu Geek Talk
Baidu Geek Talk
Mar 16, 2023 · Artificial Intelligence

PaddleDetection v2.6 Release: PP-YOLOE Family Expansion and Advanced Detection Algorithms

PaddleDetection v2.6 expands the PP‑YOLOE family with rotating, small‑object, dense‑object, and ultra‑lightweight edge‑GPU models, upgrades PP‑Human and PP‑Vehicle toolboxes, releases semi‑supervised, few‑shot and distillation learning methods, adds numerous state‑of‑the‑art algorithms, and improves infrastructure with Python 3.10, EMA filtering and AdamW support.

BaiduComputer VisionDeep Learning
0 likes · 14 min read
PaddleDetection v2.6 Release: PP-YOLOE Family Expansion and Advanced Detection Algorithms
政采云技术
政采云技术
Mar 9, 2023 · Artificial Intelligence

Comprehensive Overview of Object Detection: From Traditional Methods to Modern Deep Learning Models

This article provides a comprehensive overview of object detection, describing traditional sliding‑window approaches, deep‑learning based two‑stage and one‑stage models such as R‑CNN, Faster R‑CNN, YOLO series, and discusses current challenges, improvement directions, and future research trends in the field.

Computer VisionDeep LearningR-CNN
0 likes · 29 min read
Comprehensive Overview of Object Detection: From Traditional Methods to Modern Deep Learning Models
政采云技术
政采云技术
Mar 9, 2023 · Artificial Intelligence

Comprehensive Overview of Object Detection: From Traditional Methods to Modern Deep Learning Models

This article provides a comprehensive overview of object detection, detailing traditional sliding‑window approaches, deep‑learning based two‑stage and one‑stage models such as R‑CNN, Fast R‑CNN, Faster R‑CNN, Mask R‑CNN, and the YOLO family, and discusses current challenges and future research directions.

R-CNNYOLO
0 likes · 26 min read
Comprehensive Overview of Object Detection: From Traditional Methods to Modern Deep Learning Models
21CTO
21CTO
Feb 1, 2020 · Artificial Intelligence

How to Build a Personalized Fashion Recommendation System with Deep Learning

This article explains how to create a clothing recommendation engine by integrating multiple deep‑learning models—AlphaPose for pose estimation, YOLO v3 for garment classification, Dlib for face detection, and Keras‑based age, gender, and BMI predictors—complete with Python code examples.

YOLOfashion recommendationpose estimation
0 likes · 7 min read
How to Build a Personalized Fashion Recommendation System with Deep Learning
Alibaba Terminal Technology
Alibaba Terminal Technology
Dec 10, 2019 · Frontend Development

How AI Powers Automatic Frontend Code Generation

This article explains how Alibaba's Frontend Intelligent project uses design‑to‑code techniques, component recognition with YOLO, and a full pipeline of sample creation, model training, evaluation, and prediction refinement to automatically generate a large portion of the Double‑11 event code.

AIYOLOcode-generation
0 likes · 8 min read
How AI Powers Automatic Frontend Code Generation
360 Quality & Efficiency
360 Quality & Efficiency
Dec 6, 2019 · Artificial Intelligence

Deploying YOLO V3 with TensorFlow Serving: Environment Setup, Model Conversion, Service Deployment, and Performance Comparison

This article explains how to prepare the Docker environment, install TensorFlow Serving (CPU and GPU versions), convert a YOLO V3 checkpoint to SavedModel, deploy the model as a service, warm‑up and manage versions, invoke it via gRPC and HTTP, and compare CPU versus GPU inference performance.

AIDockerGPU
0 likes · 9 min read
Deploying YOLO V3 with TensorFlow Serving: Environment Setup, Model Conversion, Service Deployment, and Performance Comparison
Meitu Technology
Meitu Technology
Aug 14, 2018 · Artificial Intelligence

Survey of Deep Learning Based Object Detection Algorithms

This survey reviews two‑stage and one‑stage deep learning object detection methods—from early R‑CNN and OverFeat to modern Faster R‑CNN, Mask R‑CNN, SSD, and YOLO variants—detailing their architectural advances, training strategies, speed‑accuracy trade‑offs, and benchmark performance for researchers and industry practitioners.

R-CNNYOLOobject detection
0 likes · 30 min read
Survey of Deep Learning Based Object Detection Algorithms
HomeTech
HomeTech
Aug 7, 2018 · Artificial Intelligence

Overview of Object Detection Algorithms: Two‑Stage and One‑Stage Methods

This article reviews the evolution of visual object detection, explaining traditional region‑based approaches, the rise of deep‑learning two‑stage frameworks such as R‑CNN, Fast R‑CNN and Faster R‑CNN, and the faster one‑stage models like Overfeat, YOLO, SSD and RetinaNet, together with their design choices, training strategies and loss functions.

Computer VisionR-CNNSSD
0 likes · 17 min read
Overview of Object Detection Algorithms: Two‑Stage and One‑Stage Methods