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Improved CLI error reporting for users (#458)

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@ -56,11 +56,17 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
<div align="center">
[Ultralytics Live Session 3](https://youtu.be/IPcpYO5ITa8) ✨ is here! Join us on January 24th at 18 CET as we dive into the latest advancements in YOLOv8, and demonstrate how to use this cutting-edge, SOTA model to improve your object detection, instance segmentation, and image classification projects. See firsthand how YOLOv8's speed, accuracy, and ease of use make it a top choice for professionals and researchers alike.
[Ultralytics Live Session 3](https://youtu.be/IPcpYO5ITa8) ✨ is here! Join us on January 24th at 18 CET as we dive into
the latest advancements in YOLOv8, and demonstrate how to use this cutting-edge, SOTA model to improve your object
detection, instance segmentation, and image classification projects. See firsthand how YOLOv8's speed, accuracy, and
ease of use make it a top choice for professionals and researchers alike.
In addition to learning about the exciting new features and improvements of Ultralytics YOLOv8, you will also have the opportunity to ask questions and interact with our team during the live Q&A session. We encourage you to come prepared with any questions you may have.
In addition to learning about the exciting new features and improvements of Ultralytics YOLOv8, you will also have the
opportunity to ask questions and interact with our team during the live Q&A session. We encourage you to come prepared
with any questions you may have.
To join the webinar, visit our YouTube [Channel](https://www.youtube.com/@Ultralytics/streams) and turn on your notifications!
To join the webinar, visit our YouTube [Channel](https://www.youtube.com/@Ultralytics/streams) and turn on your
notifications!
<a align="center" href="https://youtu.be/IPcpYO5ITa8" target="_blank">
<img width="80%" src="https://user-images.githubusercontent.com/107626595/212887899-e94b006c-5192-40fa-8b24-7b5428e065e8.png"></a>
@ -68,8 +74,8 @@ To join the webinar, visit our YouTube [Channel](https://www.youtube.com/@Ultral
## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full
documentation on training, validation, prediction and deployment.
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for
full documentation on training, validation, prediction and deployment.
<details open>
<summary>Install</summary>
@ -88,22 +94,18 @@ pip install ultralytics
<details open>
<summary>Usage</summary>
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
```
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list
of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the
YOLOv8 [Docs](https://docs.ultralytics.com).
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
[CLI Docs](https://docs.ultralytics.com/cli) for examples.
```bash
yolo task=detect mode=train model=yolov8n.pt args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the
same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
@ -123,9 +125,10 @@ success = model.export(format="onnx") # export the model to ONNX format
```
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases).
Ultralytics [release](https://github.com/ultralytics/assets/releases). See
YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.
### Known Issues / TODOs
#### Known Issues / TODOs
We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we