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136 lines
5.6 KiB
136 lines
5.6 KiB
The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting
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YOLOv8 models.
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The `yolo` command is used for all actions:
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!!! example ""
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=== "CLI"
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```bash
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yolo TASK MODE ARGS
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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]`
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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/yolo/cfg/default.yaml).
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!!! note ""
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<b>Note:</b> Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs
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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 ""
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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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yolo detect train resume model=last.pt # resume training
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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 ""
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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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 ""
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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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 ""
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8 export formats include:
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| Format | `format=` | Model |
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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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| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |
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| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |
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| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |
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| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |
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| [TensorFlow.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 overriden by simply passing them as arguments in the CLI in `arg=value` pairs.
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!!! tip ""
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=== "Example 1"
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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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=== "Example 2"
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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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=== "Example 3"
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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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```
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