YOLO | BestHub

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.

YOLOdeep learningmedical imaging

0 likes · 17 min read

How YOLO Transforms Medical Report Screening and Occlusion Detection

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-NASModel Distillation

0 likes · 15 min read

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

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.

Model TrainingPythonUltralytics

0 likes · 14 min read

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

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.

NASYOLOcomputer vision

0 likes · 15 min read

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

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.

BaiduModel DistillationPP-YOLOE

0 likes · 14 min read

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

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.

R-CNNYOLOcomputer vision

0 likes · 29 min read

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

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

Taobao Frontend Technology

Dec 12, 2019 · Frontend Development

How Alibaba Generates Frontend Code Automatically with AI Design2Code

This article explains Alibaba's front‑end intelligent system that automatically generates UI code by extracting design metadata, recognizing basic components with a YOLO‑based model, and refining predictions, detailing the pipeline from sample creation to model evaluation and future enhancements.

AIAutomationFrontend

0 likes · 9 min read

How Alibaba Generates Frontend Code Automatically with AI Design2Code

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

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-CNNYOLOcomputer vision

0 likes · 30 min read

Survey of Deep Learning Based Object Detection Algorithms

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.

R-CNNSSDYOLO

0 likes · 17 min read

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