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227 lines
8.2 KiB
227 lines
8.2 KiB
--- |
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comments: true |
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description: 'Learn how to use Ultralytics YOLO through Command Line: train models, run predictions and exports models to different formats easily using terminal commands.' |
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keywords: Ultralytics, YOLO, CLI, train, validation, prediction, command line interface, YOLO CLI, YOLO terminal, model training, prediction, exporting |
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--- |
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# Command Line Interface Usage |
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The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment. |
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CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. |
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!!! example |
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=== "Syntax" |
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Ultralytics `yolo` commands use the following syntax: |
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```bash |
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yolo TASK MODE ARGS |
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Where TASK (optional) is one of [detect, segment, classify] |
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MODE (required) is one of [train, val, predict, export, track] |
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ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults. |
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``` |
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See all ARGS in the full [Configuration Guide](./cfg.md) or with `yolo cfg` |
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=== "Train" |
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Train a detection model for 10 epochs with an initial learning_rate of 0.01 |
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```bash |
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01 |
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``` |
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=== "Predict" |
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Predict a YouTube video using a pretrained segmentation model at image size 320: |
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```bash |
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yolo predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 |
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``` |
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=== "Val" |
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Val a pretrained detection model at batch-size 1 and image size 640: |
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```bash |
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yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640 |
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``` |
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=== "Export" |
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Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required) |
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```bash |
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128 |
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``` |
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=== "Special" |
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Run special commands to see version, view settings, run checks and more: |
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```bash |
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yolo help |
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yolo checks |
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yolo version |
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yolo settings |
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yolo copy-cfg |
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yolo cfg |
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``` |
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Where: |
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- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess |
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the `TASK` from the model type. |
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- `MODE` (required) is one of `[train, val, predict, export, track]` |
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- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults. |
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For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml` |
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GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml). |
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!!! warning "Warning" |
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Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` beteen arguments. |
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- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` ✅ |
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- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` ❌ |
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- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌ |
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## Train |
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Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see |
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the [Configuration](cfg.md) page. |
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!!! example "Example" |
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=== "Train" |
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Start training YOLOv8n on COCO128 for 100 epochs at image-size 640. |
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```bash |
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640 |
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``` |
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=== "Resume" |
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Resume an interrupted training. |
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```bash |
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yolo detect train resume model=last.pt |
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``` |
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## Val |
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's |
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training `data` and arguments as model attributes. |
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!!! example "Example" |
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=== "Official" |
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Validate an official YOLOv8n model. |
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```bash |
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yolo detect val model=yolov8n.pt |
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``` |
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=== "Custom" |
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Validate a custom-trained model. |
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```bash |
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yolo detect val model=path/to/best.pt |
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``` |
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## Predict |
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Use a trained YOLOv8n model to run predictions on images. |
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!!! example "Example" |
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=== "Official" |
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Predict with an official YOLOv8n model. |
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```bash |
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg' |
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``` |
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=== "Custom" |
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Predict with a custom model. |
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```bash |
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' |
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``` |
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## Export |
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc. |
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!!! example "Example" |
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=== "Official" |
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Export an official YOLOv8n model to ONNX format. |
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```bash |
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yolo export model=yolov8n.pt format=onnx |
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``` |
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=== "Custom" |
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Export a custom-trained model to ONNX format. |
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```bash |
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yolo export model=path/to/best.pt format=onnx |
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``` |
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, |
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i.e. `format='onnx'` or `format='engine'`. |
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| Format | `format` Argument | Model | Metadata | |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------| |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | |
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--- |
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## Overriding default arguments |
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Default arguments can be overridden by simply passing them as arguments in the CLI in `arg=value` pairs. |
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!!! tip "" |
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=== "Train" |
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Train a detection model for `10 epochs` with `learning_rate` of `0.01` |
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```bash |
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01 |
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``` |
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=== "Predict" |
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Predict a YouTube video using a pretrained segmentation model at image size 320: |
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```bash |
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yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320 |
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``` |
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=== "Val" |
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Validate a pretrained detection model at batch-size 1 and image size 640: |
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```bash |
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yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640 |
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``` |
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--- |
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## Overriding default config file |
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You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments, |
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i.e. `cfg=custom.yaml`. |
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To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-cfg` command. |
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This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args, |
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like `imgsz=320` in this example: |
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!!! example "" |
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=== "CLI" |
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```bash |
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yolo copy-cfg |
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yolo cfg=default_copy.yaml imgsz=320 |
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``` |