Linux command
yolo 命令
文本
复制后可按需替换文件名、目录或参数。
常用示例
Run object detection
yolo detect predict model=[yolo11n.pt] source=[image.jpg]
Train a model
yolo detect train data=[data.yaml] model=[yolo11n.pt] epochs=[100] imgsz=[640]
Validate model
yolo detect val model=[best.pt] data=[data.yaml]
Export model
yolo export model=[best.pt] format=[onnx]
Track objects
yolo detect track model=[yolo11n.pt] source=[video.mp4]
Run pose estimation
yolo pose predict model=[yolo11n-pose.pt] source=[image.jpg]
Benchmark
yolo benchmark model=[yolo11n.pt] imgsz=[640]
说明
yolo is the CLI for Ultralytics YOLO, a state-of-the-art computer vision framework. It provides commands for object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection from the terminal. The tool supports a complete workflow: train builds models from datasets, val evaluates model accuracy, predict runs inference on images or video, export converts models to deployment formats like ONNX and TensorRT, track performs multi-object tracking on video streams, and benchmark tests model performance across formats. Each command accepts an optional task type (detect, segment, classify, pose, obb) and a required mode. Arguments are passed as key=value pairs. Pre-trained models can be used directly for inference or fine-tuned on custom datasets. GPU acceleration is supported through PyTorch.
参数
- detect
- Object detection.
- segment
- Instance segmentation.
- classify
- Image classification.
- pose
- Pose estimation.
- obb
- Oriented bounding box detection.
- train
- Train a model on a dataset.
- val
- Validate model accuracy.
- predict
- Run inference on images, video, or streams.
- export
- Convert model to deployment formats (ONNX, TensorRT, CoreML, etc.).
- track
- Multi-object tracking on video.
- benchmark
- Benchmark model speed and accuracy across export formats.
- model=_path_
- Model file path (e.g., yolo11n.pt).
- data=_path_
- Dataset configuration YAML file.
- source=_path_
- Input source: image, video, directory, URL, or webcam (0).
- epochs=_n_
- Number of training epochs.
- imgsz=_size_
- Input image size (default: 640).
- batch=_n_
- Batch size.
- device=_id_
- Device: GPU id (0, 0,1) or cpu.
- format=_fmt_
- Export format: onnx, engine, coreml, tflite, etc.
- conf=_threshold_
- Confidence threshold for predictions.
FAQ
What is the yolo command used for?
yolo is the CLI for Ultralytics YOLO, a state-of-the-art computer vision framework. It provides commands for object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection from the terminal. The tool supports a complete workflow: train builds models from datasets, val evaluates model accuracy, predict runs inference on images or video, export converts models to deployment formats like ONNX and TensorRT, track performs multi-object tracking on video streams, and benchmark tests model performance across formats. Each command accepts an optional task type (detect, segment, classify, pose, obb) and a required mode. Arguments are passed as key=value pairs. Pre-trained models can be used directly for inference or fine-tuned on custom datasets. GPU acceleration is supported through PyTorch.
How do I run a basic yolo example?
Run `yolo detect predict model=[yolo11n.pt] source=[image.jpg]` in a terminal, then adjust file names, paths, flags, or remote targets for your system.
What does detect do in yolo?
Object detection.