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CLI Simplification (#449)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
2023-01-17 23:00:33 +01:00
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50
docs/cli.md
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@ -4,37 +4,37 @@ YOLO command line interface is the easiest way to get started.
!!! tip "Syntax"
```bash
yolo task=detect mode=train model=yolov8n.yaml epochs=1 ...
... ... ...
segment predict yolov8n-seg.pt
classify val yolov8n-cls.pt
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...
```
The experiment arguments can be overridden directly by pass `arg=val` covered in the next section. You can run any
supported task by setting `task` and `mode` in cli.
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"
| | `task` | snippet |
|------------------|------------|------------------------------------------------------------|
| Detection | `detect` | <pre><code>yolo task=detect mode=train </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo task=segment mode=train </code></pre> |
| Classification | `classify` | <pre><code>yolo task=classify mode=train </code></pre> |
| 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> |
=== "Prediction"
| | `task` | snippet |
|------------------|------------|--------------------------------------------------------------|
| Detection | `detect` | <pre><code>yolo task=detect mode=predict </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo task=segment mode=predict </code></pre> |
| Classification | `classify` | <pre><code>yolo task=classify mode=predict </code></pre> |
| 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 task=detect mode=val </code></pre> |
| Instance Segment | `segment` | <pre><code>yolo task=segment mode=val </code></pre> |
| Classification | `classify` | <pre><code>yolo task=classify mode=val </code></pre> |
| 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> |
!!! note ""
@ -44,19 +44,19 @@ supported task by setting `task` and `mode` in cli.
## Overriding default config arguments
All global 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.
!!! tip ""
=== "Syntax"
```bash
yolo task= ... mode= ... {++ arg=val ++}
yolo task mode arg=val...
```
=== "Example"
Perform detection training for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo task=detect mode=train {++ epochs=10 lr0=0.01 ++}
yolo detect train epochs=10 lr0=0.01
```
---
@ -67,23 +67,19 @@ You can override config file entirely by passing a new file. You can create a co
current working dir as follows:
```bash
yolo task=init
yolo copy-config
```
You can then use `cfg=name.yaml` command to pass the new config file
You can then use `cfg=default_copy.yaml` command to pass the new config file along with any addition args:
```bash
yolo cfg=default.yaml
yolo cfg=default_copy.yaml args...
```
??? example
=== "Command"
```bash
yolo task=init
yolo cfg=default.yaml
yolo copy-config
yolo cfg=default_copy.yaml args...
```
=== "Results"
TODO: add terminal output

18
docs/config.md
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@ -38,13 +38,13 @@ include train, val, and predict.
- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
production or when deploying the model to users.
| Key | Value | Description |
|--------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
| Key | Value | Description |
|--------|----------|--------------------------------------------------------------------------------------------------------|
| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify` |
| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict`, `export` |
| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
### Training
@ -197,6 +197,6 @@ it easier to debug and optimize the training process.
|-----------|---------|---------------------------------------------------------------------------------------------|
| project: | 'runs' | The project name |
| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
| exist_ok: | `False` | ??? |
| exist_ok: | `False` | Will replace current directory contents if set to True and output directory exists. |
| plots | `False` | **Validation**: Save plots while validation |
| nosave | `False` | Don't save any plots, models or files |
| save | `False` | Save any plots, models or files |

0
docs/home.md → docs/index.md
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4
docs/quickstart.md
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@ -36,11 +36,11 @@ CLI requires no customization or code. You can simply run all tasks from the ter
=== "Example training"
```bash
yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=0
yolo detect train model=yolov8n.pt data=coco128.yaml device=0
```
=== "Example Multi-GPU training"
```bash
yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
yolo detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
```
[CLI Guide](cli.md){ .md-button .md-button--primary}

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docs/tasks/classification.md
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@ -35,7 +35,7 @@ see the [Configuration](../config.md) page.
=== "CLI"
```bash
yolo task=classify mode=train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
yolo classify train data=mnist160 model=yolov8n-cls.pt epochs=100 imgsz=64
```
## Val
@ -60,8 +60,8 @@ it's training `data` and arguments as model attributes.
=== "CLI"
```bash
yolo task=classify mode=val model=yolov8n-cls.pt # val official model
yolo task=classify mode=val model=path/to/best.pt # val custom model
yolo classify val model=yolov8n-cls.pt # val official model
yolo classify val model=path/to/best.pt # val custom model
```
## Predict
@ -85,8 +85,8 @@ Use a trained YOLOv8n-cls model to run predictions on images.
=== "CLI"
```bash
yolo task=classify mode=predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo task=classify mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
yolo classify predict model=yolov8n-cls.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
## Export
@ -110,8 +110,8 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo mode=export model=yolov8n-cls.pt format=onnx # export official model
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
yolo export model=yolov8n-cls.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-cls export formats include:

14
docs/tasks/detection.md
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@ -35,7 +35,7 @@ the [Configuration](../config.md) page.
=== "CLI"
```bash
yolo task=detect mode=train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
```
## Val
@ -60,8 +60,8 @@ training `data` and arguments as model attributes.
=== "CLI"
```bash
yolo task=detect mode=val model=yolov8n.pt # val official model
yolo task=detect mode=val model=path/to/best.pt # val custom model
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
## Predict
@ -85,8 +85,8 @@ Use a trained YOLOv8n model to run predictions on images.
=== "CLI"
```bash
yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo task=detect mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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
```
## Export
@ -110,8 +110,8 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo mode=export model=yolov8n.pt format=onnx # export official model
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8 export formats include:

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docs/tasks/segmentation.md
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@ -35,7 +35,7 @@ arguments see the [Configuration](../config.md) page.
=== "CLI"
```bash
yolo task=segment mode=train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
```
## Val
@ -60,8 +60,8 @@ retains it's training `data` and arguments as model attributes.
=== "CLI"
```bash
yolo task=segment mode=val model=yolov8n-seg.pt # val official model
yolo task=segment mode=val model=path/to/best.pt # val custom model
yolo segment val model=yolov8n-seg.pt # val official model
yolo segment val model=path/to/best.pt # val custom model
```
## Predict
@ -85,8 +85,8 @@ Use a trained YOLOv8n-seg model to run predictions on images.
=== "CLI"
```bash
yolo task=segment mode=predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo task=segment mode=predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
```
## Export
@ -110,8 +110,8 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
=== "CLI"
```bash
yolo mode=export model=yolov8n-seg.pt format=onnx # export official model
yolo mode=export model=path/to/best.pt format=onnx # export custom trained model
yolo export model=yolov8n-seg.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
Available YOLOv8-seg export formats include: