The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting
YOLOv8 models.
The `yolo` command is used for all actions:
!!! example ""
=== "CLI"
```bash
yolo TASK MODE ARGS
```
Where:
- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess
the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export]`
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
!!! note ""
<b>Note:</b> Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs
- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` ❌
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌
## Train
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
the [Configuration](cfg.md) page.
!!! example ""
=== "CLI"
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
```
## Val
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
training `data` and arguments as model attributes.
!!! example ""
=== "CLI"
```bash
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
results = model.val() # no arguments needed, dataset and settings remembered
```
## Predict
Use a trained YOLOv8n model to run predictions on images.
!!! example ""
=== "CLI"
```bash
yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
```
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example ""
=== "CLI"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom trained
# Export the model
model.export(format="onnx")
```
Available YOLOv8 export formats include:
| Format | `format=` | Model |
|----------------------------------------------------------------------------|--------------------|---------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` |
---
## Overriding default arguments
Default arguments can be overriden by simply passing them as arguments in the CLI in `arg=value` pairs.
!!! tip ""
=== "Example 1"
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
=== "Example 2"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
```
=== "Example 3"
Validate a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
```
---
## Overriding default config file
You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments,
i.e. `cfg=custom.yaml`.
To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-cfg` command.
This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args,
like `imgsz=320` in this example:
!!! example ""
=== "CLI"
```bash
yolo copy-cfg
yolo cfg=default_copy.yaml imgsz=320
```