Ultralytics YOLO11 Modes
Introdução
Ultralytics YOLO11 is not just another object detection model; it's a versatile framework designed to cover the entire lifecycle of machine learning models—from data ingestion and model training to validation, deployment, and real-world tracking. Each mode serves a specific purpose and is engineered to offer you the flexibility and efficiency required for different tasks and use-cases.
Observa: Ultralytics Tutorial de modos: Treina, valida, prevê, exporta e faz benchmark.
Modos num relance
Understanding the different modes that Ultralytics YOLO11 supports is critical to getting the most out of your models:
- Modo de treino: Afina o teu modelo em conjuntos de dados personalizados ou pré-carregados.
- Modo Val: Um ponto de controlo pós-formação para validar o desempenho do modelo.
- Modo de previsão: Liberta o poder de previsão do teu modelo em dados do mundo real.
- Export mode: Make your model deployment-ready in various formats.
- Modo de rastreamento: Amplia o teu modelo de deteção de objectos para aplicações de seguimento em tempo real.
- Modo de referência: Analisa a velocidade e a precisão do teu modelo em diversos ambientes de implementação.
This comprehensive guide aims to give you an overview and practical insights into each mode, helping you harness the full potential of YOLO11.
Comboio
Train mode is used for training a YOLO11 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
Val
Val mode is used for validating a YOLO11 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
Prevê
Predict mode is used for making predictions using a trained YOLO11 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
Exportação
Export mode is used for exporting a YOLO11 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
Acompanha
Track mode is used for tracking objects in real-time using a YOLO11 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
Referência
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLO11. The benchmarks provide information on the size of the exported format, its mAP50-95
metrics (for object detection, segmentation, and pose) or accuracy_top5
metrics (for classification), and the inference time in milliseconds per image across various formats like ONNX, OpenVINO, TensorRT, and others. This information can help users choose the optimal export format for their specific use case based on their requirements for speed and accuracy.
FAQ
How do I train a custom object detection model with Ultralytics YOLO11?
Training a custom object detection model with Ultralytics YOLO11 involves using the train mode. You need a dataset formatted in YOLO format, containing images and corresponding annotation files. Use the following command to start the training process:
Exemplo
Para obteres instruções mais detalhadas, podes consultar o Ultralytics Train Guide.
What metrics does Ultralytics YOLO11 use to validate the model's performance?
Ultralytics YOLO11 uses various metrics during the validation process to assess model performance. These include:
- mAP (precisão média média): Avalia a precisão da deteção de objectos.
- IOU (Intersecção sobre União): Mede a sobreposição entre as caixas delimitadoras previstas e as da verdade terrestre.
- Precision and Recall: Precision measures the ratio of true positive detections to the total detected positives, while recall measures the ratio of true positive detections to the total actual positives.
Podes executar o seguinte comando para iniciar a validação:
Exemplo
Para mais informações, consulta o Guia de Validação.
How can I export my YOLO11 model for deployment?
Ultralytics YOLO11 offers export functionality to convert your trained model into various deployment formats such as ONNX, TensorRT, CoreML, and more. Use the following example to export your model:
Exemplo
Podes encontrar passos detalhados para cada formato de exportação no Guia de Exportação.
What is the purpose of the benchmark mode in Ultralytics YOLO11?
Benchmark mode in Ultralytics YOLO11 is used to analyze the speed and accuracy of various export formats such as ONNX, TensorRT, and OpenVINO. It provides metrics like model size, mAP50-95
para deteção de objectos e tempo de inferência em diferentes configurações de hardware, ajudando-te a escolher o formato mais adequado às tuas necessidades de implementação.
Exemplo
Para mais detalhes, consulta o Guia de Referência.
How can I perform real-time object tracking using Ultralytics YOLO11?
Real-time object tracking can be achieved using the track mode in Ultralytics YOLO11. This mode extends object detection capabilities to track objects across video frames or live feeds. Use the following example to enable tracking:
Exemplo
Para obteres instruções detalhadas, visita o Track Guide.