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YOLO11 Tasks, Modes, Usage, Macros and Solutions Updates (#16593)

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docs/en/usage/cli.md
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@ -1,7 +1,7 @@
---
comments: true
description: Explore the YOLOv8 command line interface (CLI) for easy execution of detection tasks without needing a Python environment.
keywords: YOLOv8 CLI, command line interface, YOLOv8 commands, detection tasks, Ultralytics, model training, model prediction
description: Explore the YOLO11 command line interface (CLI) for easy execution of detection tasks without needing a Python environment.
keywords: YOLO11 CLI, command line interface, YOLO11 commands, detection tasks, Ultralytics, model training, model prediction
---
# Command Line Interface Usage
@ -16,7 +16,7 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Mastering Ultralytics YOLOv8: CLI
<strong>Watch:</strong> Mastering Ultralytics YOLO: CLI
</p>
!!! example
@ -37,28 +37,28 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
Train a detection model for 10 [epochs](https://www.ultralytics.com/glossary/epoch) with an initial learning_rate of 0.01
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
=== "Predict"
Predict a YouTube video using a pretrained segmentation model at image size 320:
```bash
yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Val a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
=== "Export"
Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
```bash
yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
```
=== "Special"
@ -75,7 +75,7 @@ The YOLO command line interface (CLI) allows for simple single-line commands wit
Where:
- `TASK` (optional) is one of `[detect, segment, classify, pose, obb]`. If it is not passed explicitly YOLOv8 will try to guess the `TASK` from the model type.
- `TASK` (optional) is one of `[detect, segment, classify, pose, obb]`. If it is not passed explicitly YOLO11 will try to guess the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
- `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`
@ -83,21 +83,21 @@ Where:
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 `,` between arguments.
- `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; ❌
- `yolo predict model=yolo11n.pt imgsz=640 conf=0.25` &nbsp; ✅
- `yolo predict model yolo11n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolo11n.pt --imgsz 640 --conf 0.25` &nbsp; ❌
## Train
Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](cfg.md) page.
Train YOLO11n on the COCO8 dataset for 100 epochs at image size 640. For a full list of available arguments see the [Configuration](cfg.md) page.
!!! example
=== "Train"
Start training YOLOv8n on COCO8 for 100 epochs at image-size 640.
Start training YOLO11n on COCO8 for 100 epochs at image-size 640.
```bash
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=100 imgsz=640
```
=== "Resume"
@ -109,15 +109,15 @@ Train YOLOv8n on the COCO8 dataset for 100 epochs at image size 640. For a full
## Val
Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
Validate trained YOLO11n model [accuracy](https://www.ultralytics.com/glossary/accuracy) on the COCO8 dataset. No arguments are needed as the `model` retains its training `data` and arguments as model attributes.
!!! example
=== "Official"
Validate an official YOLOv8n model.
Validate an official YOLO11n model.
```bash
yolo detect val model=yolov8n.pt
yolo detect val model=yolo11n.pt
```
=== "Custom"
@ -129,15 +129,15 @@ Validate trained YOLOv8n model [accuracy](https://www.ultralytics.com/glossary/a
## Predict
Use a trained YOLOv8n model to run predictions on images.
Use a trained YOLO11n model to run predictions on images.
!!! example
=== "Official"
Predict with an official YOLOv8n model.
Predict with an official YOLO11n model.
```bash
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
yolo detect predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg'
```
=== "Custom"
@ -149,15 +149,15 @@ Use a trained YOLOv8n model to run predictions on images.
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
Export a YOLO11n model to a different format like ONNX, CoreML, etc.
!!! example
=== "Official"
Export an official YOLOv8n model to ONNX format.
Export an official YOLO11n model to ONNX format.
```bash
yolo export model=yolov8n.pt format=onnx
yolo export model=yolo11n.pt format=onnx
```
=== "Custom"
@ -167,7 +167,7 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx
```
Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`.
Available YOLO11 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`.
{% include "macros/export-table.md" %}
@ -183,21 +183,21 @@ Default arguments can be overridden by simply passing them as arguments in the C
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo detect train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
=== "Predict"
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/LNwODJXcvt4' imgsz=320
yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
```
=== "Val"
Validate a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo detect val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
## Overriding default config file
@ -219,19 +219,19 @@ This will create `default_copy.yaml`, which you can then pass as `cfg=default_co
## FAQ
### How do I use the Ultralytics YOLOv8 command line interface (CLI) for model training?
### How do I use the Ultralytics YOLO11 command line interface (CLI) for model training?
To train a YOLOv8 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, you would run:
To train a YOLO11 model using the CLI, you can execute a simple one-line command in the terminal. For example, to train a detection model for 10 epochs with a [learning rate](https://www.ultralytics.com/glossary/learning-rate) of 0.01, you would run:
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
This command uses the `train` mode with specific arguments. Refer to the full list of available arguments in the [Configuration Guide](cfg.md).
### What tasks can I perform with the Ultralytics YOLOv8 CLI?
### What tasks can I perform with the Ultralytics YOLO11 CLI?
The Ultralytics YOLOv8 CLI supports a variety of tasks including detection, segmentation, classification, validation, prediction, export, and tracking. For instance:
The Ultralytics YOLO11 CLI supports a variety of tasks including detection, segmentation, classification, validation, prediction, export, and tracking. For instance:
- **Train a Model**: Run `yolo train data=<data.yaml> model=<model.pt> epochs=<num>`.
- **Run Predictions**: Use `yolo predict model=<model.pt> source=<data_source> imgsz=<image_size>`.
@ -239,32 +239,32 @@ The Ultralytics YOLOv8 CLI supports a variety of tasks including detection, segm
Each task can be customized with various arguments. For detailed syntax and examples, see the respective sections like [Train](#train), [Predict](#predict), and [Export](#export).
### How can I validate the accuracy of a trained YOLOv8 model using the CLI?
### How can I validate the accuracy of a trained YOLO11 model using the CLI?
To validate a YOLOv8 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size of 640, run:
To validate a YOLO11 model's accuracy, use the `val` mode. For example, to validate a pretrained detection model with a [batch size](https://www.ultralytics.com/glossary/batch-size) of 1 and image size of 640, run:
```bash
yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
```
This command evaluates the model on the specified dataset and provides performance metrics. For more details, refer to the [Val](#val) section.
### What formats can I export my YOLOv8 models to using the CLI?
### What formats can I export my YOLO11 models to using the CLI?
YOLOv8 models can be exported to various formats such as ONNX, CoreML, TensorRT, and more. For instance, to export a model to ONNX format, run:
YOLO11 models can be exported to various formats such as ONNX, CoreML, TensorRT, and more. For instance, to export a model to ONNX format, run:
```bash
yolo export model=yolov8n.pt format=onnx
yolo export model=yolo11n.pt format=onnx
```
For complete details, visit the [Export](../modes/export.md) page.
### How do I customize YOLOv8 CLI commands to override default arguments?
### How do I customize YOLO11 CLI commands to override default arguments?
To override default arguments in YOLOv8 CLI commands, pass them as `arg=value` pairs. For example, to train a model with custom arguments, use:
To override default arguments in YOLO11 CLI commands, pass them as `arg=value` pairs. For example, to train a model with custom arguments, use:
```bash
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
```
For a full list of available arguments and their descriptions, refer to the [Configuration Guide](cfg.md). Ensure arguments are formatted correctly, as shown in the [Overriding default arguments](#overriding-default-arguments) section.