Ultralytics yolo Ultralytics YOLO serisi gerçek zamanlı nesne dedektörleri, son teknoloji doğruluk, hız ve verimlilikle neyin mümkün olduğunu yeniden tanımlıyor. YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. YOLO11 é a mais recente iteração da série Ultralytics YOLO série de detectores de objectos em tempo real, redefinindo o que é possível com precisão, velocidade e eficiência de ponta. Instalar Ultralytics. Train mode in Ultralytics YOLO11 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. update(dict(model=model. Welcome to the Brain-tumor detection using Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. , Jetson) or CPUs at lower resolutions. Apr 1, 2025 · from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n model model = YOLO ("yolov8n. Ultralytics oferece uma variedade de métodos de instalação, incluindo pip, conda e Docker. 对于那些希望在自己的项目中实施对象检测的人来说，YOLOv4 仍然是一个强有力的竞争者，尤其是当实时性能成为优先考虑的因素时。Ultralytics 目前主要支持较新的YOLO 版本，如 YOLOv8和 YOLO11等较新的 YOLO 版本，但 YOLOv4 中引入的架构创新影响了这些较后模型的开发。 Mar 24, 2025 · Ultralytics YOLO11, represents the newest evolution in the YOLO series. Introduction. ckpt["ema"] model. 78, the release that introduces YOLO12, and see examples of its performance. YOLOv8 incorporates new architectural features like an anchor-free 踏上迷人之旅，重温YOLO VISION 2023 的亮点。 Ultralytics 的创始人兼首席执行官格伦-乔彻（Glenn Jocher）将在我们的主题演讲中与我们一起，从粒子物理学的深奥领域领略视觉人工智能（AI）和人工通用智能（AGI）的前沿。 Mar 19, 2025 · Simply download your desired . # Display the results results [0]. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. Ultralytics HUB是Ultralytics' 无代码、用户友好型平台，旨在简化从训练到部署YOLO 模型（包括新推出的Ultralytics YOLO11 模型）的整个过程。 无论您是人工智能专家还是计算机视觉新手，HUB 都能提供直观的界面，让您上传 数据集 ，选择 预训练模型 ，并根据您的特定 Apr 5, 2025 · Model Training with Ultralytics YOLO. YOLO Problemas comuns ⭐ RECOMENDADO: Soluções práticas e dicas de resolução de problemas para os problemas mais frequentemente encontrados quando se trabalha com os modelos Ultralytics YOLO . from ultralytics import YOLO # Build a YOLOv6n model from scratch model = YOLO ("yolov6n. pt file to load into the Ultralytics YOLO class. YOLO Common Issues ⭐ RECOMMENDED: Practical solutions and troubleshooting tips to the most frequently encountered issues when working with Ultralytics YOLO models. I tried to follow these intructions to achieve this: The approach seems to work on my local machine 由Ultralytics 提供支持的YOLO Vision 2024（YV24）诞生于一个面向社区的社区，它将开源视觉人工智能社区团结在一起。 #YV24 是一个专注于数据、ML 和计算机视觉进步的会议。 見るんだ： Ultralytics ｜工業用パッケージデータセットを使用したカスタムデータでのYOLOv9トレーニング YOLOv9の紹介. Small models run efficiently on edge GPUs (e. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv5n model on the 'bus. Hardware Requirements: Inference: Recommended GPU (NVIDIA with ≥4-8GB VRAM). YOLO12 introduce una arquitectura centrada en la atención que se aleja de los enfoques tradicionales basados en CNN utilizados en modelos YOLO anteriores, pero conserva la velocidad de inferencia en tiempo real esencial para muchas aplicaciones. ckpt: del model. YOLO12: Detección de objetos centrada en la atención Visión general. Aqui está uma compilação de guias detalhados para o ajudar a dominar diferentes aspectos de Ultralytics YOLO . Architectures dorsale et cervicale avancées : YOLOv8 utilise des architectures dorsales et cervicales de pointe, ce qui permet d'améliorer les performances en matière d'extraction de caractéristiques et de détection d'objets. It is designed as a versatile framework supporting a full range of vision AI tasks, including detection, segmentation, classification, pose estimation, and oriented bounding boxes (OBB). pt") # n, s, m, l, x versions available # Perform object detection on an image results = model. 9 月 30 日星期一，Ultralytics 正式发布了 Ultralytics YOLO11继在Ultralytics的年度混合盛会YOLO Vision 2024（YV24）上首次亮相后，计算机视觉领域的最新进展--人工智能模型正式发布。人工智能界兴奋不已，争相探索该模型的功能。 総合ガイドUltralytics YOLOv5. YOLO11 실시간 물체 감지기의 최신 버전인 Ultralytics YOLO 시리즈의 최신 버전으로, 최첨단 정확도, 속도, 효율성으로 가능성의 한계를 재정의합니다. Explore the features, capabilities, and licenses of YOLO11 and other YOLO models, and find resources for installation, training, and prediction. 見るんだ： Ultralytics YOLO11 オブジェクト検出とトラッキングの使い方｜ベンチマーク方法｜YOLO11 RELEASED🚀 主な特徴. yolo12 引入了一种以注意力为中心的架构，它不同于以往yolo 模型中使用的基于 cnn 的传统方法，但仍保持了许多应用所必需的实时推理速度。 YOLO11 是 UltralyticsYOLO 是实时物体检测器系列中的最新产品，以最先进的精度、速度和效率重新定义了可能实现的目标。在之前YOLO 版本令人印象深刻的进步基础上，YOLO11 在架构和训练方法上进行了重大改进，使其成为广泛的计算机视觉任务的多功能选择。 将Ultralytics YOLO 集成到您的应用程序中，或利用我们的无代码解决方案优化 ML 模型管道。 无论您是初创企业还是大型企业，YOLO 都能为计算机视觉问题提供高效、可扩展的解决方案。 Ultralytics YOLO11 개요. modules import Detect model = YOLO("yolov10n. 3. Ultralytics YOLO models, like the latest Ultralytics YOLO11, support a variety of computer vision tasks such as object detection, image classification, and instance segmentation. 6 days ago · An overview of Ultralytics YOLO11. Ultralytics-Snippets 扩展用于 VS Code，旨在帮助数据科学家和机器学习工程师更高效地使用Ultralytics YOLO 构建计算机视觉应用程序。通过提供预构建的代码片段和有用的示例，我们可以帮助您专注于最重要的事情：创建创新的解决方案。 Nov 27, 2024 · Feel free to review Ultralytics YOLOv8 classification documentation for more insights: YOLOv8 Classification Docs. from ultralytics import YOLO, ASSETS from ultralytics. Each of these tasks aims to replicate a specific aspect of human vision, making it possible for machines to see and interpret the world around them. from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n model model = YOLO ("yolov8n. YOLO11 en son yinelemedir. YOLO12 introduit une architecture centrée sur l'attention qui s'écarte des approches traditionnelles basées sur le CNN utilisées dans les modèles YOLO précédents, tout en conservant la vitesse d'inférence en temps réel essentielle pour de nombreuses applications. Mar 23, 2025 · Welcome to the Ultralytics Model Comparison Hub! This page centralizes detailed technical comparisons between state-of-the-art object detection models, focusing on the latest Ultralytics YOLO versions alongside other leading architectures like RTDETR, EfficientDet, and more. Ultralytics 由于模型的快速发展，YOLOv8 还没有发表正式的研究论文。我们专注于推进技术发展，使其更易于使用，而不是制作静态文档。有关YOLO 架构、功能和使用方法的最新信息，请参阅我们的GitHub 存储库和文档。 由Ultralytics YOLO - 最先进的人工智能提供支持. YOLO12 : Détection d'objets centrée sur l'attention Vue d'ensemble. Training a deep learning model involves feeding it data and adjusting its parameters so that it can make accurate predictions. jpg' image Ultralytics YOLOv8：功能多样、用户友好的检测系统. Learn initialization, model mapping, and more. Learn about its features, performance, and supported tasks and modes in this web page. 现在就在您的移动设备上下载Ultralytics HUB 应用程序，随时随地释放YOLO 模型的潜能。欲了解更多关于Ultralytics HUB平台的培训、部署和使用自定义模型的信息，请查看我们全面的HUB文档。 常见问题 我可以在Ultralytics HUB 应用程序上运行哪些模型？ Ultralytics YOLO Python -NAS 型号可通过我们的 ultralytics python 软件包。该软件包提供了用户友好的Python API，以简化流程。 下面的示例展示了如何使用YOLO-NAS 模型与 ultralytics 用于推理和验证的软件包： 推理和验证示例. Ultralytics YOLO 是我们的智能工具，它就像哈佛大学的学生--高智商，永远渴望学习。只需创建课程表，它就能成长！ Feb 20, 2025 · YOLO12: Attention-Centric Object Detection Overview. See full list on github. pt") # Display model information (optional) model. model. jpg' image Ultralytics YOLO11 Tổng quan. pt") for m in model. com 4 days ago · Learn how to install Ultralytics, a Python package for YOLO models, using pip, conda, Git, or Docker. Ultralytics YOLO 、物体検出をどのように向上させるのか？ YOLO のインストールとセットアップはどのように始められますか？ 自分のデータセットでカスタムモデル（YOLO ）をトレーニングするには？ Ultralytics YOLO で利用可能なライセンスオプションは何ですか？ Apr 1, 2025 · from ultralytics import YOLO # Load a COCO-pretrained YOLOv5n model model = YOLO ("yolov5n. 最適なリアルタイムの物体検出を追求する中で、YOLOv9は、ディープニューラルネットワークに特有の情報損失の課題を克服する革新的なアプローチで際立っています。 from ultralytics import YOLO # Load a pre-trained YOLO model (adjust model type as needed) model = YOLO ("yolo11n. yolo. Mar 21, 2025 · Ultralytics YOLOv8 represents the latest iteration in the YOLO series, designed as a versatile framework supporting a full range of vision AI tasks, including object detection, instance segmentation, image classification, pose estimation, and oriented bounding boxes (OBB). It is engineered for enhanced accuracy and efficiency in object detection and other vision tasks like instance segmentation , image classification , pose estimation , and oriented bounding boxes (OBB). If you’d like, you can also look into the model implementation by downloading the source code to verify how the logits are processed during training and validation. 強化された特徴抽出： YOLO11 、改良されたバックボーンとネックアーキテクチャを採用し、より正確な物体検出と複雑なタスクのパフォーマンスを実現するための特徴抽出機能を Apr 14, 2025 · Watch: Ultralytics YOLO11 Guides Overview Guides. YOLOv10: 실시간 엔드투엔드 객체 감지. As my results so far are only moderate, I would like to try another approach and use a weighted data loader in order to have balanced classes during training. I try to train a yolov11 model with a certain subset of classes from the CityScapes dataset. model module for YOLO object detection. YOLO 许可证：Ultralytics YOLO 如何获得许可证？ 物体检测的演变 常见问题 什么是Ultralytics YOLO ，它如何改进物体检测？ 如何开始YOLO 安装和设置？ 如何在数据集上训练自定义YOLO 模型？ Ultralytics YOLO 有哪些许可选项？ Ultralytics YOLO 如何用于实时物体跟踪？ yolo12：以注意力为中心的物体检测 概述. jpg' image Ultralytics YOLO models, including YOLO11, come with versatile modes like train, predict, and export, making them adaptable to a variety of AI workflows. Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO. g. Find out how to use Ultralytics v8. yaml") # Display model information (optional) model. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv6n model on the 'bus. Ultralytics YOLO 模型，包括YOLO11 ，具有训练、预测和输出等多种模式，使其能够适应各种 AI 工作流程。 例如，在训练模式下，Ultralytics YOLO 模型可在定制数据集上进行微调和训练，以实现特定应用，如检测独特物体或优化特定环境。 Ultralytics YOLO11 Genel Bakış. Mar 31, 2025 · Explore the ultralytics. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv8n model on the 'bus. info # Train the model on the COCO8 example dataset for 100 epochs results = model. Ultralytics YOLOv8 出版物. Ultralytics YOLOv8YOLO 是YOLO 系列的最新迭代产品，它是一个多功能框架，支持各种视觉人工智能任务，包括对象检测、实例分割、图像分类、姿态估计和定向边界框 (OBB)。YOLOv8 融合了新的架构特性，例如无锚检测头和经过改进的 C2f 颈部，从而提高了性能和灵活性。. We hope that Feb 20, 2025 · Join the conversation about the latest research and development of YOLO (v)12, a real-time object detection model with attention mechanisms. It improves upon earlier YOLO model versions and is packed with features that can help solve real-world problems. Ultralytics YOLO11 is a reliable Vision AI model that is designed to perform various computer vision tasks accurately. modules(): if hasattr(m, "num_batches_tracked"): del m. See examples of CLI commands for training, predicting, exporting, and more. Vereinfache den ML-Entwicklungsprozess und verbessere die Zusammenarbeit zwischen den Teammitgliedern mit unserer no-code Plattform. ckpt. Simplifica o processo de desenvolvimento de ML e melhora a colaboração entre os membros da equipa utilizando a nossa plataforma sem código. 了解如何使用 pip、conda 或 Docker 安装Ultralytics 。请按照我们的分步指南，通过详尽的说明无缝安装YOLO 。 Ultralytics YOLO11 Visão geral. 在本例中，我们在 COCO8 数据集上验证了YOLO-NAS-s。 如何将Ultralytics YOLO 与 ROS 集成以进行实时物体检测？ 什么是 ROS 主题，如何在Ultralytics YOLO 中使用？ 为什么要在 ROS 中使用Ultralytics YOLO 深度图像？ 如何在 ROS 中使用YOLO 可视化 3D 点云？ 计算机视觉项目的步骤 确定计算机视觉项目的目标 数据收集和注释 如何使用Ultralytics YOLO11 训练自定义对象检测模型？ 使用Ultralytics YOLO11 训练自定义对象检测模型需要使用训练模式。您需要一个YOLO 格式的数据集，其中包含图像和相应的注释文件。使用以下命令启动训练过程： Oct 25, 2024 · You can remove the postprocessing from the model and export. predict (source = "image. nn. Feb 26, 2025 · YOLOv7: Trainable Bag-of-Freebies. Você pode instalar YOLO através do ultralytics pip para a última versão estável, ou clonando o pacote Ultralytics Repositório GitHub para obter a versão mais atual. pt") model = YOLO YOLOv3-Ultralytics 是Ultralytics' YOLOv3 的改良版，增加了对更多预训练模型的支持，并方便了模型定制。YOLOv3u 是 YOLOv3-Ultralytics 的升级变体，集成了YOLOv8 的无锚点、无对象性分割头，提高了各种对象大小的检测稳健性和准确性。有关变体的详细信息，请参阅YOLOv3 系列。 Mar 22, 2025 · Ultralytics YOLOv8, introduced in January 2023, represents the latest iteration in the Ultralytics YOLO series at the time of its release. jpg") # Can also use video, directory, URL, etc. YOLO11 es la última iteración de la serie Ultralytics YOLO de detectores de objetos en tiempo real, que redefine lo que es posible con una precisión, velocidad y eficacia de vanguardia. 見るんだ： カスタム学習Ultralytics YOLO モデルをエクスポートし、ウェブカメラ上でライブ推論を実行する方法。 なぜYOLO11 のエクスポート・モードを選ぶのか？ 要将Ultralytics YOLO 模型与Gradio一起部署，进行交互式物体检测演示，您可以按照Gradio集成页面上的步骤进行操作。Gradio 可以为实时模型推理创建简单易用的网络界面，是以适合开发人员和终端用户的友好格式展示YOLO 模型功能的绝佳工具。 Ultralytics YOLO ist ein effizientes Werkzeug für Fachleute aus den Bereichen Computer Vision und ML, mit dem genaue Modelle zur Objekterkennung erstellt werden können. train (data = "coco8. Regarder : Ultralytics YOLOv8 Aperçu du modèle Principales caractéristiques de YOLOv8. model)) if "ema" in model. show # Show the first image Ver: Ultralytics YOLO11 Visão geral dos guias Guias. For example, in the training mode, Ultralytics YOLO models can be fine-tuned and trained on custom datasets for specific applications, such as detecting unique objects or optimizing for Jan 16, 2025 · Hello and thank you for this help forum. Sep 30, 2024 · Ultralytics YOLO11 is a series of versatile and efficient models for various computer vision tasks, such as detection, segmentation, pose estimation, and classification. save("model. Extensive open-vocabulary 探索YOLO-World 模型，利用Ultralytics YOLOv8 先进技术实现高效、实时的开放词汇对象检测。以最少的计算量实现最高的性能。 Découvrez Ultralytics YOLO - le dernier cri en matière de détection d'objets et de segmentation d'images en temps réel. YOLO11 là phiên bản mới nhất của Ultralytics YOLO loạt các máy dò vật thể thời gian thực, định nghĩa lại những gì có thể với độ chính xác, tốc độ và hiệu quả tiên tiến. Ultralytics ようこそ YOLOv5ドキュメントへようこそ！ Ultralytics YOLOv55は、革新的な "You Only Look Once "物体検出モデルの5番目のイテレーションで、リアルタイムで高速、高精度の結果を提供するように設計されています。 Ultralytics YOLO11 Visión general. Ultralytics YOLOv8是Ultralytics 开发的最先进的型号，代表了YOLO 系列的最新进展。它注重多功能性、性能和易用性。 作者：Glenn Jocher、Ayush Chaurasia 和 Jing QiuGlenn Jocher、Ayush Chaurasia 和 Jing Qiu; 组织机构 Ultralytics; 日期 Mar 17, 2025 · YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. 패키지를 기반으로 구축된 YOLOv10은 Ultralytics Python 패키지를 기반으로 구축된 YOLOv10은 실시간 객체 감지에 대한 새로운 접근 방식을 도입하여 이전 YOLO 버전에서 발견된 후처리 및 모델 아키텍처의 결함을 모두 해결했습니다. num_batches_tracked model. jpg' image Ultralytics YOLO é uma ferramenta eficiente para profissionais que trabalham em visão computacional e ML que pode ajudar a criar modelos precisos de deteção de objectos. Apr 14, 2025 · Learn how to use Ultralytics YOLO, the latest version of the acclaimed YOLO model for real-time object detection and image segmentation. Training: Fine-tuning YOLOE on custom data typically requires just one GPU. YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. Découvrez ses fonctionnalités et optimisez son potentiel dans vos projets. models. amiprykyxjtrfcnhhyxkzwcytymwosmbshjinejjwpphxcsptoznzrkpvmfqkpggxlwgpwrgi
Ultralytics yolo Ultralytics YOLO serisi gerçek zamanlı nesne dedektörleri, son teknoloji doğruluk, hız ve verimlilikle neyin mümkün olduğunu yeniden tanımlıyor. YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. YOLO11 é a mais recente iteração da série Ultralytics YOLO série de detectores de objectos em tempo real, redefinindo o que é possível com precisão, velocidade e eficiência de ponta. Instalar Ultralytics. Train mode in Ultralytics YOLO11 is engineered for effective and efficient training of object detection models, fully utilizing modern hardware capabilities. update(dict(model=model. Welcome to the Brain-tumor detection using Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. , Jetson) or CPUs at lower resolutions. Apr 1, 2025 · from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n model model = YOLO ("yolov8n. Ultralytics oferece uma variedade de métodos de instalação, incluindo pip, conda e Docker. 对于那些希望在自己的项目中实施对象检测的人来说，YOLOv4 仍然是一个强有力的竞争者，尤其是当实时性能成为优先考虑的因素时。Ultralytics 目前主要支持较新的YOLO 版本，如 YOLOv8和 YOLO11等较新的 YOLO 版本，但 YOLOv4 中引入的架构创新影响了这些较后模型的开发。 Mar 24, 2025 · Ultralytics YOLO11, represents the newest evolution in the YOLO series. Introduction. ckpt["ema"] model. 78, the release that introduces YOLO12, and see examples of its performance. YOLOv8 incorporates new architectural features like an anchor-free 踏上迷人之旅，重温YOLO VISION 2023 的亮点。 Ultralytics 的创始人兼首席执行官格伦-乔彻（Glenn Jocher）将在我们的主题演讲中与我们一起，从粒子物理学的深奥领域领略视觉人工智能（AI）和人工通用智能（AGI）的前沿。 Mar 19, 2025 · Simply download your desired . # Display the results results [0]. YOLOv3u is an upgraded variant of YOLOv3-Ultralytics, integrating the anchor-free, objectness-free split head from YOLOv8, improving detection robustness and accuracy for various object sizes. Ultralytics HUB是Ultralytics' 无代码、用户友好型平台，旨在简化从训练到部署YOLO 模型（包括新推出的Ultralytics YOLO11 模型）的整个过程。 无论您是人工智能专家还是计算机视觉新手，HUB 都能提供直观的界面，让您上传 数据集 ，选择 预训练模型 ，并根据您的特定 Apr 5, 2025 · Model Training with Ultralytics YOLO. YOLO Problemas comuns ⭐ RECOMENDADO: Soluções práticas e dicas de resolução de problemas para os problemas mais frequentemente encontrados quando se trabalha com os modelos Ultralytics YOLO . from ultralytics import YOLO # Build a YOLOv6n model from scratch model = YOLO ("yolov6n. pt file to load into the Ultralytics YOLO class. YOLO Common Issues ⭐ RECOMMENDED: Practical solutions and troubleshooting tips to the most frequently encountered issues when working with Ultralytics YOLO models. I tried to follow these intructions to achieve this: The approach seems to work on my local machine 由Ultralytics 提供支持的YOLO Vision 2024（YV24）诞生于一个面向社区的社区，它将开源视觉人工智能社区团结在一起。 #YV24 是一个专注于数据、ML 和计算机视觉进步的会议。 見るんだ： Ultralytics ｜工業用パッケージデータセットを使用したカスタムデータでのYOLOv9トレーニング YOLOv9の紹介. Small models run efficiently on edge GPUs (e. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv5n model on the 'bus. Hardware Requirements: Inference: Recommended GPU (NVIDIA with ≥4-8GB VRAM). YOLO12 introduce una arquitectura centrada en la atención que se aleja de los enfoques tradicionales basados en CNN utilizados en modelos YOLO anteriores, pero conserva la velocidad de inferencia en tiempo real esencial para muchas aplicaciones. ckpt: del model. YOLO12: Detección de objetos centrada en la atención Visión general. Aqui está uma compilação de guias detalhados para o ajudar a dominar diferentes aspectos de Ultralytics YOLO . Architectures dorsale et cervicale avancées : YOLOv8 utilise des architectures dorsales et cervicales de pointe, ce qui permet d'améliorer les performances en matière d'extraction de caractéristiques et de détection d'objets. It is designed as a versatile framework supporting a full range of vision AI tasks, including detection, segmentation, classification, pose estimation, and oriented bounding boxes (OBB). pt") # n, s, m, l, x versions available # Perform object detection on an image results = model. 9 月 30 日星期一，Ultralytics 正式发布了 Ultralytics YOLO11继在Ultralytics的年度混合盛会YOLO Vision 2024（YV24）上首次亮相后，计算机视觉领域的最新进展--人工智能模型正式发布。人工智能界兴奋不已，争相探索该模型的功能。 総合ガイドUltralytics YOLOv5. YOLO11 실시간 물체 감지기의 최신 버전인 Ultralytics YOLO 시리즈의 최신 버전으로, 최첨단 정확도, 속도, 효율성으로 가능성의 한계를 재정의합니다. Explore the features, capabilities, and licenses of YOLO11 and other YOLO models, and find resources for installation, training, and prediction. 見るんだ： Ultralytics YOLO11 オブジェクト検出とトラッキングの使い方｜ベンチマーク方法｜YOLO11 RELEASED🚀 主な特徴. yolo12 引入了一种以注意力为中心的架构，它不同于以往yolo 模型中使用的基于 cnn 的传统方法，但仍保持了许多应用所必需的实时推理速度。 YOLO11 是 UltralyticsYOLO 是实时物体检测器系列中的最新产品，以最先进的精度、速度和效率重新定义了可能实现的目标。在之前YOLO 版本令人印象深刻的进步基础上，YOLO11 在架构和训练方法上进行了重大改进，使其成为广泛的计算机视觉任务的多功能选择。 将Ultralytics YOLO 集成到您的应用程序中，或利用我们的无代码解决方案优化 ML 模型管道。 无论您是初创企业还是大型企业，YOLO 都能为计算机视觉问题提供高效、可扩展的解决方案。 Ultralytics YOLO11 개요. modules import Detect model = YOLO("yolov10n. 3. Ultralytics YOLO models, like the latest Ultralytics YOLO11, support a variety of computer vision tasks such as object detection, image classification, and instance segmentation. 6 days ago · An overview of Ultralytics YOLO11. Ultralytics-Snippets 扩展用于 VS Code，旨在帮助数据科学家和机器学习工程师更高效地使用Ultralytics YOLO 构建计算机视觉应用程序。通过提供预构建的代码片段和有用的示例，我们可以帮助您专注于最重要的事情：创建创新的解决方案。 Nov 27, 2024 · Feel free to review Ultralytics YOLOv8 classification documentation for more insights: YOLOv8 Classification Docs. from ultralytics import YOLO, ASSETS from ultralytics. Each of these tasks aims to replicate a specific aspect of human vision, making it possible for machines to see and interpret the world around them. from ultralytics import YOLO # Load a COCO-pretrained YOLOv8n model model = YOLO ("yolov8n. YOLO11 en son yinelemedir. YOLO12 introduit une architecture centrée sur l'attention qui s'écarte des approches traditionnelles basées sur le CNN utilisées dans les modèles YOLO précédents, tout en conservant la vitesse d'inférence en temps réel essentielle pour de nombreuses applications. Mar 23, 2025 · Welcome to the Ultralytics Model Comparison Hub! This page centralizes detailed technical comparisons between state-of-the-art object detection models, focusing on the latest Ultralytics YOLO versions alongside other leading architectures like RTDETR, EfficientDet, and more. Ultralytics 由于模型的快速发展，YOLOv8 还没有发表正式的研究论文。我们专注于推进技术发展，使其更易于使用，而不是制作静态文档。有关YOLO 架构、功能和使用方法的最新信息，请参阅我们的GitHub 存储库和文档。 由Ultralytics YOLO - 最先进的人工智能提供支持. YOLO12 : Détection d'objets centrée sur l'attention Vue d'ensemble. Training a deep learning model involves feeding it data and adjusting its parameters so that it can make accurate predictions. jpg' image Ultralytics YOLOv8：功能多样、用户友好的检测系统. Learn initialization, model mapping, and more. Learn about its features, performance, and supported tasks and modes in this web page. 现在就在您的移动设备上下载Ultralytics HUB 应用程序，随时随地释放YOLO 模型的潜能。欲了解更多关于Ultralytics HUB平台的培训、部署和使用自定义模型的信息，请查看我们全面的HUB文档。 常见问题 我可以在Ultralytics HUB 应用程序上运行哪些模型？ Ultralytics YOLO Python -NAS 型号可通过我们的 ultralytics python 软件包。该软件包提供了用户友好的Python API，以简化流程。 下面的示例展示了如何使用YOLO-NAS 模型与 ultralytics 用于推理和验证的软件包： 推理和验证示例. Ultralytics YOLO 是我们的智能工具，它就像哈佛大学的学生--高智商，永远渴望学习。只需创建课程表，它就能成长！ Feb 20, 2025 · YOLO12: Attention-Centric Object Detection Overview. See full list on github. pt") # Display model information (optional) model. model. jpg' image Ultralytics YOLO11 Tổng quan. pt") for m in model. com 4 days ago · Learn how to install Ultralytics, a Python package for YOLO models, using pip, conda, Git, or Docker. Ultralytics YOLO 、物体検出をどのように向上させるのか？ YOLO のインストールとセットアップはどのように始められますか？ 自分のデータセットでカスタムモデル（YOLO ）をトレーニングするには？ Ultralytics YOLO で利用可能なライセンスオプションは何ですか？ Apr 1, 2025 · from ultralytics import YOLO # Load a COCO-pretrained YOLOv5n model model = YOLO ("yolov5n. 最適なリアルタイムの物体検出を追求する中で、YOLOv9は、ディープニューラルネットワークに特有の情報損失の課題を克服する革新的なアプローチで際立っています。 from ultralytics import YOLO # Load a pre-trained YOLO model (adjust model type as needed) model = YOLO ("yolo11n. yolo. Mar 21, 2025 · Ultralytics YOLOv8 represents the latest iteration in the YOLO series, designed as a versatile framework supporting a full range of vision AI tasks, including object detection, instance segmentation, image classification, pose estimation, and oriented bounding boxes (OBB). It is engineered for enhanced accuracy and efficiency in object detection and other vision tasks like instance segmentation , image classification , pose estimation , and oriented bounding boxes (OBB). If you’d like, you can also look into the model implementation by downloading the source code to verify how the logits are processed during training and validation. 強化された特徴抽出： YOLO11 、改良されたバックボーンとネックアーキテクチャを採用し、より正確な物体検出と複雑なタスクのパフォーマンスを実現するための特徴抽出機能を Apr 14, 2025 · Watch: Ultralytics YOLO11 Guides Overview Guides. YOLOv10: 실시간 엔드투엔드 객체 감지. As my results so far are only moderate, I would like to try another approach and use a weighted data loader in order to have balanced classes during training. I try to train a yolov11 model with a certain subset of classes from the CityScapes dataset. model module for YOLO object detection. YOLO 许可证：Ultralytics YOLO 如何获得许可证？ 物体检测的演变 常见问题 什么是Ultralytics YOLO ，它如何改进物体检测？ 如何开始YOLO 安装和设置？ 如何在数据集上训练自定义YOLO 模型？ Ultralytics YOLO 有哪些许可选项？ Ultralytics YOLO 如何用于实时物体跟踪？ yolo12：以注意力为中心的物体检测 概述. jpg' image Ultralytics YOLO models, including YOLO11, come with versatile modes like train, predict, and export, making them adaptable to a variety of AI workflows. Here's a compilation of in-depth guides to help you master different aspects of Ultralytics YOLO. g. Find out how to use Ultralytics v8. yaml") # Display model information (optional) model. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv6n model on the 'bus. Ultralytics YOLO 模型，包括YOLO11 ，具有训练、预测和输出等多种模式，使其能够适应各种 AI 工作流程。 例如，在训练模式下，Ultralytics YOLO 模型可在定制数据集上进行微调和训练，以实现特定应用，如检测独特物体或优化特定环境。 Ultralytics YOLO11 Genel Bakış. Mar 31, 2025 · Explore the ultralytics. yaml", epochs = 100, imgsz = 640) # Run inference with the YOLOv8n model on the 'bus. info # Train the model on the COCO8 example dataset for 100 epochs results = model. Ultralytics YOLOv8 出版物. Ultralytics YOLOv8YOLO 是YOLO 系列的最新迭代产品，它是一个多功能框架，支持各种视觉人工智能任务，包括对象检测、实例分割、图像分类、姿态估计和定向边界框 (OBB)。YOLOv8 融合了新的架构特性，例如无锚检测头和经过改进的 C2f 颈部，从而提高了性能和灵活性。. We hope that Feb 20, 2025 · Join the conversation about the latest research and development of YOLO (v)12, a real-time object detection model with attention mechanisms. It improves upon earlier YOLO model versions and is packed with features that can help solve real-world problems. Ultralytics YOLO11 is a reliable Vision AI model that is designed to perform various computer vision tasks accurately. modules(): if hasattr(m, "num_batches_tracked"): del m. See examples of CLI commands for training, predicting, exporting, and more. Vereinfache den ML-Entwicklungsprozess und verbessere die Zusammenarbeit zwischen den Teammitgliedern mit unserer no-code Plattform. ckpt. Simplifica o processo de desenvolvimento de ML e melhora a colaboração entre os membros da equipa utilizando a nossa plataforma sem código. 了解如何使用 pip、conda 或 Docker 安装Ultralytics 。请按照我们的分步指南，通过详尽的说明无缝安装YOLO 。 Ultralytics YOLO11 Visão geral. 在本例中，我们在 COCO8 数据集上验证了YOLO-NAS-s。 如何将Ultralytics YOLO 与 ROS 集成以进行实时物体检测？ 什么是 ROS 主题，如何在Ultralytics YOLO 中使用？ 为什么要在 ROS 中使用Ultralytics YOLO 深度图像？ 如何在 ROS 中使用YOLO 可视化 3D 点云？ 计算机视觉项目的步骤 确定计算机视觉项目的目标 数据收集和注释 如何使用Ultralytics YOLO11 训练自定义对象检测模型？ 使用Ultralytics YOLO11 训练自定义对象检测模型需要使用训练模式。您需要一个YOLO 格式的数据集，其中包含图像和相应的注释文件。使用以下命令启动训练过程： Oct 25, 2024 · You can remove the postprocessing from the model and export. predict (source = "image. nn. Feb 26, 2025 · YOLOv7: Trainable Bag-of-Freebies. Você pode instalar YOLO através do ultralytics pip para a última versão estável, ou clonando o pacote Ultralytics Repositório GitHub para obter a versão mais atual. pt") model = YOLO YOLOv3-Ultralytics 是Ultralytics' YOLOv3 的改良版，增加了对更多预训练模型的支持，并方便了模型定制。YOLOv3u 是 YOLOv3-Ultralytics 的升级变体，集成了YOLOv8 的无锚点、无对象性分割头，提高了各种对象大小的检测稳健性和准确性。有关变体的详细信息，请参阅YOLOv3 系列。 Mar 22, 2025 · Ultralytics YOLOv8, introduced in January 2023, represents the latest iteration in the Ultralytics YOLO series at the time of its release. jpg") # Can also use video, directory, URL, etc. YOLO11 es la última iteración de la serie Ultralytics YOLO de detectores de objetos en tiempo real, que redefine lo que es posible con una precisión, velocidad y eficacia de vanguardia. 見るんだ： カスタム学習Ultralytics YOLO モデルをエクスポートし、ウェブカメラ上でライブ推論を実行する方法。 なぜYOLO11 のエクスポート・モードを選ぶのか？ 要将Ultralytics YOLO 模型与Gradio一起部署，进行交互式物体检测演示，您可以按照Gradio集成页面上的步骤进行操作。Gradio 可以为实时模型推理创建简单易用的网络界面，是以适合开发人员和终端用户的友好格式展示YOLO 模型功能的绝佳工具。 Ultralytics YOLO ist ein effizientes Werkzeug für Fachleute aus den Bereichen Computer Vision und ML, mit dem genaue Modelle zur Objekterkennung erstellt werden können. train (data = "coco8. Regarder : Ultralytics YOLOv8 Aperçu du modèle Principales caractéristiques de YOLOv8. model)) if "ema" in model. show # Show the first image Ver: Ultralytics YOLO11 Visão geral dos guias Guias. For example, in the training mode, Ultralytics YOLO models can be fine-tuned and trained on custom datasets for specific applications, such as detecting unique objects or optimizing for Jan 16, 2025 · Hello and thank you for this help forum. Sep 30, 2024 · Ultralytics YOLO11 is a series of versatile and efficient models for various computer vision tasks, such as detection, segmentation, pose estimation, and classification. save("model. Extensive open-vocabulary 探索YOLO-World 模型，利用Ultralytics YOLOv8 先进技术实现高效、实时的开放词汇对象检测。以最少的计算量实现最高的性能。 Découvrez Ultralytics YOLO - le dernier cri en matière de détection d'objets et de segmentation d'images en temps réel. YOLO11 là phiên bản mới nhất của Ultralytics YOLO loạt các máy dò vật thể thời gian thực, định nghĩa lại những gì có thể với độ chính xác, tốc độ và hiệu quả tiên tiến. Ultralytics ようこそ YOLOv5ドキュメントへようこそ！ Ultralytics YOLOv55は、革新的な "You Only Look Once "物体検出モデルの5番目のイテレーションで、リアルタイムで高速、高精度の結果を提供するように設計されています。 Ultralytics YOLO11 Visión general. Ultralytics YOLOv8是Ultralytics 开发的最先进的型号，代表了YOLO 系列的最新进展。它注重多功能性、性能和易用性。 作者：Glenn Jocher、Ayush Chaurasia 和 Jing QiuGlenn Jocher、Ayush Chaurasia 和 Jing Qiu; 组织机构 Ultralytics; 日期 Mar 17, 2025 · YOLOv3-Ultralytics is Ultralytics' adaptation of YOLOv3 that adds support for more pre-trained models and facilitates easier model customization. 패키지를 기반으로 구축된 YOLOv10은 Ultralytics Python 패키지를 기반으로 구축된 YOLOv10은 실시간 객체 감지에 대한 새로운 접근 방식을 도입하여 이전 YOLO 버전에서 발견된 후처리 및 모델 아키텍처의 결함을 모두 해결했습니다. num_batches_tracked model. jpg' image Ultralytics YOLO é uma ferramenta eficiente para profissionais que trabalham em visão computacional e ML que pode ajudar a criar modelos precisos de deteção de objectos. Apr 14, 2025 · Learn how to use Ultralytics YOLO, the latest version of the acclaimed YOLO model for real-time object detection and image segmentation. Training: Fine-tuning YOLOE on custom data typically requires just one GPU. YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. Découvrez ses fonctionnalités et optimisez son potentiel dans vos projets. models. ami pry kyxjtrfc nhhy xkzwc ytymwo smbshjin ejj wpphx cspt oznz rkpvmf qkp ggxlw gpwrgi