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Improved CLI error reporting for users (#458)

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If you want to train, validate or run inference on models and don't need to make any modifications to the code, using
YOLO command line interface is the easiest way to get started.
The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting
YOLOv8 models.
!!! tip "Syntax"
The `yolo` command is used for all actions:
```bash
yolo task=detect mode=train model=yolov8n.yaml args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
!!! example ""
The default arguments can be overridden directly by passing custom `arg=val` covered in the next section. You can run
any supported task by setting `task` and `mode` in CLI.
=== "Training"
=== "CLI"
```bash
yolo TASK MODE ARGS
```
| | `task` | snippet |
|------------------|------------|------------------------------------------------------------|
| Detection | `detect` | <pre><code>yolo detect train </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo segment train </code></pre> |
| Classification | `classify` | <pre><code>yolo classify train </code></pre> |
Where:
=== "Prediction"
| | `task` | snippet |
|------------------|------------|--------------------------------------------------------------|
| Detection | `detect` | <pre><code>yolo detect predict </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo segment predict </code></pre> |
| Classification | `classify` | <pre><code>yolo classify predict </code></pre> |
=== "Validation"
| | `task` | snippet |
|------------------|------------|-----------------------------------------------------------|
| Detection | `detect` | <pre><code>yolo detect val </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo segment val </code></pre> |
| Classification | `classify` | <pre><code>yolo classify val </code></pre> |
- `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](config.md) page.
!!! note ""
<b>Note:</b> The arguments don't require `'--'` prefix. These are reserved for special commands covered later
<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` &nbsp; ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` &nbsp; ❌
## Train
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
the [Configuration](config.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 config arguments
## Overriding default arguments
Default arguments can be overriden by simply passing them as arguments in the CLI.
Default arguments can be overriden by simply passing them as arguments in the CLI in `arg=value` pairs.
!!! tip ""
=== "Syntax"
=== "Example 1"
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo task mode arg=val...
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
=== "Example"
Perform detection training for `10 epochs` with `learning_rate` of `0.01`
=== "Example 2"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo detect train epochs=10 lr0=0.01
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 config file entirely by passing a new file. You can create a copy of default config file in your
current working dir as follows:
You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments,
i.e. `cfg=custom.yaml`.
```bash
yolo copy-config
```
To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-config` command.
You can then use `cfg=default_copy.yaml` command to pass the new config file along with any addition args:
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:
```bash
yolo cfg=default_copy.yaml args...
```
!!! example ""
??? example
=== "Command"
=== "CLI"
```bash
yolo copy-config
yolo cfg=default_copy.yaml args...
```
yolo cfg=default_copy.yaml imgsz=320
```