Improved CLI error reporting for users (#458)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>single_channel
Glenn Jocher
2 years ago
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cc3c774bde
@ -1,85 +1,196 @@
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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
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YOLO command line interface is the easiest way to get started.
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The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting
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YOLOv8 models.
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!!! tip "Syntax"
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The `yolo` command is used for all actions:
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!!! example ""
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=== "CLI"
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```bash
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yolo task=detect mode=train model=yolov8n.yaml args...
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classify predict yolov8n-cls.yaml args...
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segment val yolov8n-seg.yaml args...
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export yolov8n.pt format=onnx args...
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yolo TASK MODE ARGS
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```
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The default arguments can be overridden directly by passing custom `arg=val` covered in the next section. You can run
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any supported task by setting `task` and `mode` in CLI.
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=== "Training"
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Where:
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| | `task` | snippet |
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|------------------|------------|------------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo detect train </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment train </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify train </code></pre> |
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- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess
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the `TASK` from the model type.
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- `MODE` (required) is one of `[train, val, predict, export]`
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- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
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For a full list of available `ARGS` see the [Configuration](config.md) page.
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=== "Prediction"
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!!! note ""
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| | `task` | snippet |
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|------------------|------------|--------------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo detect predict </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment predict </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify predict </code></pre> |
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<b>Note:</b> Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs
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=== "Validation"
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- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` ✅
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- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` ❌
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- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌
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| | `task` | snippet |
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|------------------|------------|-----------------------------------------------------------|
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| Detection | `detect` | <pre><code>yolo detect val </code></pre> |
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| Instance Segment | `segment` | <pre><code>yolo segment val </code></pre> |
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| Classification | `classify` | <pre><code>yolo classify val </code></pre> |
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## Train
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!!! note ""
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Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
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the [Configuration](config.md) page.
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!!! example ""
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=== "CLI"
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```bash
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
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```
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Train the model
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results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
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```
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## Val
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Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
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training `data` and arguments as model attributes.
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!!! example ""
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=== "CLI"
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```bash
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yolo detect val model=yolov8n.pt # val official model
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yolo detect val model=path/to/best.pt # val custom model
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```
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Validate the model
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results = model.val() # no arguments needed, dataset and settings remembered
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```
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## Predict
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<b>Note:</b> The arguments don't require `'--'` prefix. These are reserved for special commands covered later
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Use a trained YOLOv8n model to run predictions on images.
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!!! example ""
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=== "CLI"
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```bash
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yolo detect predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" # predict with official model
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yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model
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```
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom model
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# Predict with the model
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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```
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## Export
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Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
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!!! example ""
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=== "CLI"
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.pt") # load an official model
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model = YOLO("path/to/best.pt") # load a custom trained
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# Export the model
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model.export(format="onnx")
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```
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Available YOLOv8 export formats include:
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| Format | `format=` | Model |
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|----------------------------------------------------------------------------|--------------------|---------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` |
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| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |
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| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |
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| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |
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| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |
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| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` |
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---
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## Overriding default config arguments
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## Overriding default arguments
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Default arguments can be overriden by simply passing them as arguments in the CLI.
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Default arguments can be overriden by simply passing them as arguments in the CLI in `arg=value` pairs.
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!!! tip ""
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=== "Syntax"
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=== "Example 1"
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Train a detection model for `10 epochs` with `learning_rate` of `0.01`
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```bash
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yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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```
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=== "Example 2"
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Predict a YouTube video using a pretrained segmentation model at image size 320:
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```bash
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yolo task mode arg=val...
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yolo segment predict model=yolov8n-seg.pt source=https://youtu.be/Zgi9g1ksQHc imgsz=320
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```
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=== "Example"
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Perform detection training for `10 epochs` with `learning_rate` of `0.01`
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=== "Example 3"
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Validate a pretrained detection model at batch-size 1 and image size 640:
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```bash
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yolo detect train epochs=10 lr0=0.01
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yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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```
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---
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## Overriding default config file
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You can override config file entirely by passing a new file. You can create a copy of default config file in your
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current working dir as follows:
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```bash
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yolo copy-config
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```
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You can override the `default.yaml` config file entirely by passing a new file with the `cfg` arguments,
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i.e. `cfg=custom.yaml`.
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You can then use `cfg=default_copy.yaml` command to pass the new config file along with any addition args:
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To do this first create a copy of `default.yaml` in your current working dir with the `yolo copy-config` command.
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```bash
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yolo cfg=default_copy.yaml args...
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```
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This will create `default_copy.yaml`, which you can then pass as `cfg=default_copy.yaml` along with any additional args,
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like `imgsz=320` in this example:
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??? example
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!!! example ""
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=== "Command"
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=== "CLI"
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```bash
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yolo copy-config
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yolo cfg=default_copy.yaml args...
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yolo cfg=default_copy.yaml imgsz=320
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```
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@ -1,9 +1,9 @@
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# Ultralytics YOLO 🚀, GPL-3.0 license
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__version__ = "8.0.7"
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__version__ = "8.0.8"
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from ultralytics.hub import checks
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from ultralytics.yolo.engine.model import YOLO
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from ultralytics.yolo.utils import ops
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from ultralytics.yolo.utils.checks import check_yolo as checks
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__all__ = ["__version__", "YOLO", "hub", "checks"] # allow simpler import
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