PyTorch 生态#
在 ecosystem 中，PyTorch 社区有很多优质开源项目，可以帮助你更好地利用 PyTorch 进行深度学习。
- PyTorch 教程
- PyTorch 基础
- TorchScript
torch.compile()torchvision变换- 模型检查的特征提取
- 使用 NumPy 和 SciPy 创建扩展
- 无参数示例
- 参数化示例
- 临时教程
- 量化
torch.fx- 中间表示
- A guide on good usage of
non_blockingandpin_memory()in PyTorch - Background
- A PyTorch perspective
non_blocking=True- Synergies
- Other copy directions (GPU -> CPU, CPU -> MPS)
- Practical recommendations
- Additional considerations
- Conclusion
- Additional resources
- 模型剪枝
- 参数化教程
- 使用 FX 构建简单的 CPU 性能分析器
- 在pytorch中通道最后的内存格式
- Forward-mode 自动微分
- (beta) Building a Convolution/Batch Norm fuser in FX
- A guide on good usage of
- ONNX 接口
- Detectron2
- TorchMetrics
- DDP
- ExecuTorch
- ExecuTorch 简介
- ExecuTorch 概念
- 提前编译
- ATen
- ATen 方言
- ATen 模式
- Autograd 安全的 ATen 方言
- 后端
- 后端方言
- 后端注册表
- 后端特定算子
- Buck2
- CMake
- 代码生成
- 核心 ATen 方言
- 核心 ATen 算子 / 规范 ATen 算子集
- 核心 ATen 分解表
- 自定义算子
- DataLoader
- 委托
- DSP
- dtype
- 动态量化
- 动态形状
- Eager 模式
- Edge 方言
- 边缘算子
- ExecuTorch
- ExecuTorch 方法
- ExecuTorch 程序
- executor_runner
- EXIR
- ExportedProgram
- flatbuffer
- Framework tax
- Functional ATen operators
- Graph
- Graph mode
- 高阶算子
- Hybrid Quantization
- Intermediate Representation (IR)
- Kernel
- Kernel registry / Operator registry
- Lowering
- Memory planning
- Node
- Operator
- Operator fusion
- Out variant
- PAL
- 部分内核
- 分区器
- 便携式模式（精简模式）
- 便携式内核
- 程序
- 程序源代码
- PTQ（Post Training Quantization）
- QAT（Quantization Aware Training）
- 量化
- 运行时
- SDK（Software Development Kit）
- 选择性构建
- 静态量化
- XNNPACK
- ExecuTorch 工作原理
- ExecuTorch 技术架构
- 配置 ExecuTorch
- PyTorch 原生架构优化：
torchao - 混沌
- Autograd 机制
- 自动微分
- PyTorch Benchmark
- Extending PyTorch
- 其他临时区
- PyTorch 参数管理
- 性能评估
- Pytorch 梯度归零
- ExecuTorch
torchaoultralytics框架- Ultralytics 简介
ultralytics模式ultralytics任务ultralytics解决方案: 解决现实世界问题- Ultralytics YOLO 教程
- Ultralytics YOLO 教程概览
- 解决YOLO常见问题
- 性能指标深度剖析
- 部署
- Understanding YOLO11's Deployment Options
- Introduction
- How to Select the Right Deployment Option for Your YOLO11 Model
- Comparative Analysis of YOLO11 Deployment Options
- Community and Support
- Conclusion
- FAQ
- What are the deployment options available for YOLO11 on different hardware platforms?
- How do I improve the inference speed of my YOLO11 model on an Intel CPU?
- Can I deploy YOLO11 models on mobile devices?
- What factors should I consider when choosing a deployment format for my YOLO11 model?
- How can I deploy YOLO11 models in a web application?
- Best Practices for Model Deployment
- Introduction
- Model Deployment Options
- Model Optimization Techniques
- Troubleshooting Deployment Issues
- Security Considerations in Model Deployment
- Share Ideas With Your Peers
- Conclusion and Next Steps
- FAQ
- What are the best practices for deploying a machine learning model using Ultralytics YOLO11?
- How can I troubleshoot common deployment issues with Ultralytics YOLO11 models?
- How does Ultralytics YOLO11 optimization enhance model performance on edge devices?
- What are the security considerations for deploying machine learning models with Ultralytics YOLO11?
- How do I choose the right deployment environment for my Ultralytics YOLO11 model?
- Understanding YOLO11's Deployment Options
- 功能特性
- K-Fold Cross Validation with Ultralytics
- Introduction
- Setup
- Generating Feature Vectors for Object Detection Dataset
- K-Fold Dataset Split
- Save Records (Optional)
- Train YOLO using K-Fold Data Splits
- Conclusion
- FAQ
- What is K-Fold Cross Validation and why is it useful in object detection?
- How do I implement K-Fold Cross Validation using Ultralytics YOLO?
- Why should I use Ultralytics YOLO for object detection?
- How can I ensure my annotations are in the correct format for Ultralytics YOLO?
- Can I use K-Fold Cross Validation with custom datasets other than Fruit Detection?
- Ultralytics YOLO Hyperparameter Tuning Guide
- Introduction
- Preparing for Hyperparameter Tuning
- Steps Involved
- Usage Example
- Results
- Conclusion
- FAQ
- How do I optimize the learning rate for Ultralytics YOLO during hyperparameter tuning?
- What are the benefits of using genetic algorithms for hyperparameter tuning in YOLO11?
- How long does the hyperparameter tuning process take for Ultralytics YOLO?
- What metrics should I use to evaluate model performance during hyperparameter tuning in YOLO?
- Can I use Ultralytics HUB for hyperparameter tuning of YOLO models?
- Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
- Introduction to SAHI
- What is Sliced Inference?
- Installation and Preparation
- Standard Inference with YOLO11
- Sliced Inference with YOLO11
- Handling Prediction Results
- Batch Prediction
- Citations and Acknowledgments
- FAQ
- How can I integrate YOLO11 with SAHI for sliced inference in object detection?
- Why should I use SAHI with YOLO11 for object detection on large images?
- Can I visualize prediction results when using YOLO11 with SAHI?
- What features does SAHI offer for improving YOLO11 object detection?
- How do I handle large-scale inference projects using YOLO11 and SAHI?
- YOLO11 🚀 on AzureML
- What is Azure?
- What is Azure Machine Learning (AzureML)?
- How Does AzureML Benefit YOLO Users?
- Prerequisites
- Create a compute instance
- Quickstart from Terminal
- Quickstart from a Notebook
- Explore More with AzureML
- FAQ
- How do I run YOLO11 on AzureML for model training?
- What are the benefits of using AzureML for YOLO11 training?
- How do I troubleshoot common issues when running YOLO11 on AzureML?
- Can I use both the Ultralytics CLI and Python interface on AzureML?
- What is the advantage of using Ultralytics YOLO11 over other object detection models?
- Conda Quickstart Guide for Ultralytics
- What You Will Learn
- Prerequisites
- Setting up a Conda Environment
- Installing Ultralytics
- Using Ultralytics
- Ultralytics Conda Docker Image
- Speeding Up Installation with Libmamba
- FAQ
- What is the process for setting up a Conda environment for Ultralytics projects?
- Why should I use Conda over pip for managing dependencies in Ultralytics projects?
- Can I use Ultralytics YOLO in a CUDA-enabled environment for faster performance?
- What are the benefits of using Ultralytics Docker images with a Conda environment?
- How can I speed up Conda package installation in my Ultralytics environment?
- Docker Quickstart Guide for Ultralytics
- What You Will Learn
- Prerequisites
- Setting up Docker with NVIDIA Support
- Installing Ultralytics Docker Images
- Running Ultralytics in Docker Container
- Running Ultralytics in Docker Container
- Run graphical user interface (GUI) applications in a Docker Container
- FAQ
- How do I set up Ultralytics with Docker?
- What are the benefits of using Ultralytics Docker images for machine learning projects?
- How can I run Ultralytics YOLO in a Docker container with GPU support?
- How do I visualize YOLO prediction results in a Docker container with a display server?
- Can I mount local directories into the Ultralytics Docker container?
- 边缘计算
- Quick Start Guide: Raspberry Pi with Ultralytics YOLO11
- What is Raspberry Pi?
- Raspberry Pi Series Comparison
- What is Raspberry Pi OS?
- Flash Raspberry Pi OS to Raspberry Pi
- Set Up Ultralytics
- Use NCNN on Raspberry Pi
- Convert Model to NCNN and Run Inference
- Raspberry Pi 5 YOLO11 Benchmarks
- Reproduce Our Results
- Use Raspberry Pi Camera
- Best Practices when using Raspberry Pi
- Next Steps
- Acknowledgements and Citations
- FAQ
- How do I set up Ultralytics YOLO11 on a Raspberry Pi without using Docker?
- Why should I use Ultralytics YOLO11's NCNN format on Raspberry Pi for AI tasks?
- How can I convert a YOLO11 model to NCNN format for use on Raspberry Pi?
- What are the hardware differences between Raspberry Pi 4 and Raspberry Pi 5 relevant to running YOLO11?
- How can I set up a Raspberry Pi Camera Module to work with Ultralytics YOLO11?
- Quick Start Guide: NVIDIA Jetson with Ultralytics YOLO11
- What is NVIDIA Jetson?
- NVIDIA Jetson Series Comparison
- What is NVIDIA JetPack?
- Flash JetPack to NVIDIA Jetson
- JetPack Support Based on Jetson Device
- Quick Start with Docker
- Start with Native Installation
- Use TensorRT on NVIDIA Jetson
- NVIDIA Jetson Orin YOLO11 Benchmarks
- Reproduce Our Results
- Best Practices when using NVIDIA Jetson
- Next Steps
- FAQ
- How do I deploy Ultralytics YOLO11 on NVIDIA Jetson devices?
- What performance benchmarks can I expect from YOLO11 models on NVIDIA Jetson devices?
- Why should I use TensorRT for deploying YOLO11 on NVIDIA Jetson?
- How can I install PyTorch and Torchvision on NVIDIA Jetson?
- What are the best practices for maximizing performance on NVIDIA Jetson when using YOLO11?
- Ultralytics YOLO11 on NVIDIA Jetson using DeepStream SDK and TensorRT
- What is NVIDIA DeepStream?
- Prerequisites
- DeepStream Configuration for YOLO11
- INT8 Calibration
- MultiStream Setup
- Benchmark Results
- FAQ
- How do I set up Ultralytics YOLO11 on an NVIDIA Jetson device?
- What is the benefit of using TensorRT with YOLO11 on NVIDIA Jetson?
- Can I run Ultralytics YOLO11 with DeepStream SDK across different NVIDIA Jetson hardware?
- How can I convert a YOLO11 model to ONNX for DeepStream?
- What are the performance benchmarks for YOLO on NVIDIA Jetson Orin NX?
- Coral Edge TPU on a Raspberry Pi with Ultralytics YOLO11 🚀
- What is a Coral Edge TPU?
- Boost Raspberry Pi Model Performance with Coral Edge TPU
- Edge TPU on Raspberry Pi with TensorFlow Lite (New)⭐
- Prerequisites
- Installation Walkthrough
- Export your model to a Edge TPU compatible model
- Running the model
- FAQ
- What is a Coral Edge TPU and how does it enhance Raspberry Pi's performance with Ultralytics YOLO11?
- How do I install the Coral Edge TPU runtime on a Raspberry Pi?
- Can I export my Ultralytics YOLO11 model to be compatible with Coral Edge TPU?
- What should I do if TensorFlow is already installed on my Raspberry Pi but I want to use tflite-runtime instead?
- How do I run inference with an exported YOLO11 model on a Raspberry Pi using the Coral Edge TPU?
- Quick Start Guide: Raspberry Pi with Ultralytics YOLO11
- Triton Inference Server with Ultralytics YOLO11
- What is Triton Inference Server?
- Prerequisites
- Exporting YOLO11 to ONNX Format
- Setting Up Triton Model Repository
- Running Triton Inference Server
- FAQ
- How do I set up Ultralytics YOLO11 with NVIDIA Triton Inference Server?
- What benefits does using Ultralytics YOLO11 with NVIDIA Triton Inference Server offer?
- Why should I export my YOLO11 model to ONNX format before using Triton Inference Server?
- Can I run inference using the Ultralytics YOLO11 model on Triton Inference Server?
- How does Ultralytics YOLO11 compare to TensorFlow and PyTorch models for deployment?
- Thread-Safe Inference with YOLO Models
- Understanding Python Threading
- The Danger of Shared Model Instances
- Thread-Safe Inference
- Conclusion
- FAQ
- How can I avoid race conditions when using YOLO models in a multi-threaded Python environment?
- What are the best practices for running multi-threaded YOLO model inference in Python?
- Why should each thread have its own YOLO model instance?
- How does Python's Global Interpreter Lock (GIL) affect YOLO model inference?
- Is it safer to use process-based parallelism instead of threading for YOLO model inference?
- Isolating Segmentation Objects
- Recipe Walk Through
- Full Example code
- FAQ
- How do I isolate objects using Ultralytics YOLO11 for segmentation tasks?
- What options are available for saving the isolated objects after segmentation?
- How can I crop isolated objects to their bounding boxes using Ultralytics YOLO11?
- Why should I use Ultralytics YOLO11 for object isolation in segmentation tasks?
- Can I save isolated objects including the background using Ultralytics YOLO11?
- Viewing Inference Results in a Terminal
- Motivation
- Process
- Example Inference Results
- Full Code Example
- FAQ
- How can I view YOLO inference results in a VSCode terminal on macOS or Linux?
- Why does the sixel protocol only work on Linux and macOS?
- What if I encounter issues with displaying images in the VSCode terminal?
- Can YOLO display video inference results in the terminal using sixel?
- How can I troubleshoot issues with the
python-sixellibrary?
- Optimizing OpenVINO Inference for Ultralytics YOLO Models: A Comprehensive Guide
- Introduction
- Optimizing for Latency
- Optimizing for Throughput
- Conclusion
- FAQ
- How do I optimize Ultralytics YOLO models for low latency using OpenVINO?
- Why should I use OpenVINO for optimizing Ultralytics YOLO throughput?
- What is the best practice for reducing first-inference latency in OpenVINO?
- How do I balance optimizing for latency and throughput with Ultralytics YOLO and OpenVINO?
- Can I use Ultralytics YOLO models with other AI frameworks besides OpenVINO?
- K-Fold Cross Validation with Ultralytics
- 项目
- Understanding the Key Steps in a Computer Vision Project
- Introduction
- An Overview of a Computer Vision Project
- Step 1: Defining Your Project's Goals
- Step 2: Data Collection and Data Annotation
- Step 3: Data Augmentation and Splitting Your Dataset
- Step 4: Model Training
- Step 5: Model Evaluation and Model Finetuning
- Step 6: Model Testing
- Step 7: Model Deployment
- Step 8: Monitoring, Maintenance, and Documentation
- Engaging with the Community
- Kickstart Your Computer Vision Project Today!
- FAQ
- How do I choose the right computer vision task for my project?
- Why is data annotation crucial in computer vision projects?
- What steps should I follow to augment and split my dataset effectively?
- How can I export my trained computer vision model for deployment?
- What are the best practices for monitoring and maintaining a deployed computer vision model?
- A Practical Guide for Defining Your Computer Vision Project
- Introduction
- Defining A Clear Problem Statement
- The Connection Between The Problem Statement and The Computer Vision Tasks
- Which Comes First: Model Selection, Dataset Preparation, or Model Training Approach?
- Common Discussion Points in the Community
- Connecting with the Community
- Conclusion
- FAQ
- How do I define a clear problem statement for my Ultralytics computer vision project?
- Why should I use Ultralytics YOLO11 for speed estimation in my computer vision project?
- How do I set effective measurable objectives for my computer vision project with Ultralytics YOLO11?
- How do deployment options affect the performance of my Ultralytics YOLO models?
- What are the most common challenges in defining the problem for a computer vision project with Ultralytics?
- Data Collection and Annotation Strategies for Computer Vision
- Introduction
- Setting Up Classes and Collecting Data
- What is Data Annotation?
- Share Your Thoughts with the Community
- Conclusion
- FAQ
- What is the best way to avoid bias in data collection for computer vision projects?
- How can I ensure high consistency and accuracy in data annotation?
- How many images do I need for training Ultralytics YOLO models?
- What are some popular tools for data annotation?
- What types of data annotation are commonly used in computer vision?
- Data Preprocessing Techniques for Annotated Computer Vision Data
- Introduction
- Importance of Data Preprocessing
- Data Preprocessing Techniques
- A Case Study of Preprocessing
- Exploratory Data Analysis Techniques
- Reach Out and Connect
- Your Dataset Is Ready!
- FAQ
- What is the importance of data preprocessing in computer vision projects?
- How can I use Ultralytics YOLO for data augmentation?
- What are the best data normalization techniques for computer vision data?
- How should I split my annotated dataset for training?
- Can I handle varying image sizes in YOLO11 without manual resizing?
- Machine Learning Best Practices and Tips for Model Training
- Introduction
- How to Train a Machine Learning Model
- Training on Large Datasets
- The Number of Epochs To Train For
- Early Stopping
- Choosing Between Cloud and Local Training
- Selecting an Optimizer
- Connecting with the Community
- Key Takeaways
- FAQ
- How can I improve GPU utilization when training a large dataset with Ultralytics YOLO?
- What is mixed precision training, and how do I enable it in YOLO11?
- How does multiscale training enhance YOLO11 model performance?
- How can I use pre-trained weights to speed up training in YOLO11?
- What is the recommended number of epochs for training a model, and how do I set this in YOLO11?
- Insights on Model Evaluation and Fine-Tuning
- Introduction
- Evaluating Model Performance Using Metrics
- Evaluating YOLO11 Model Performance
- How Does Fine-Tuning Work?
- Tips for Fine-Tuning Your Model
- Engage with the Community
- Final Thoughts
- FAQ
- What are the key metrics for evaluating YOLO11 model performance?
- How can I fine-tune a pre-trained YOLO11 model for my specific dataset?
- How can I handle variable image sizes when evaluating my YOLO11 model?
- What practical steps can I take to improve mean average precision for my YOLO11 model?
- How do I access YOLO11 model evaluation metrics in Python?
- A Guide on Model Testing
- Introduction
- Model Testing Vs. Model Evaluation
- Preparing for Model Testing
- Testing Your Computer Vision Model
- Testing Your YOLO11 Model
- Using YOLO11 to Predict on Multiple Test Images
- Running YOLO11 Predictions Without Custom Training
- Overfitting and Underfitting in Machine Learning
- Data Leakage in Computer Vision and How to Avoid It
- What Comes After Model Testing
- Join the AI Conversation
- In Summary
- FAQ
- What are the key differences between model evaluation and model testing in computer vision?
- How can I test my Ultralytics YOLO11 model on multiple images?
- What should I do if my computer vision model shows signs of overfitting or underfitting?
- How can I detect and avoid data leakage in computer vision?
- What steps should I take after testing my computer vision model?
- How do I run YOLO11 predictions without custom training?
- Maintaining Your Computer Vision Models After Deployment
- Introduction
- Model Monitoring is Key
- Model Maintenance
- Documentation
- Connect with the Community
- Key Takeaways
- FAQ
- How do I monitor the performance of my deployed computer vision model?
- What are the best practices for maintaining computer vision models after deployment?
- Why is data drift detection important for AI models?
- What tools can I use for anomaly detection in computer vision models?
- How can I document my computer vision project effectively?
- Understanding the Key Steps in a Computer Vision Project
- ROS (Robot Operating System) quickstart guide
torchtune- Captum
- 统一半监督学习基准
- Ray
- Renate
- TorchOpt
- Intel® Neural Compressor
- OpenMMLab
- Flower 联邦学习框架
- LightlySSL
- TorchDrug
- PyTorchVideo
- TorchIO
- depyf