Add YOLOv5 tutorials to docs.ultralytics.com (#1657)
Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Sergiu Waxmann <47978446+sergiuwaxmann@users.noreply.github.com>
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Glenn Jocher
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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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# Command Line Interface Usage
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The `yolo` command is used for all actions:
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The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
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CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command.
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!!! example ""
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!!! example
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=== "CLI"
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=== "Syntax"
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Ultralytics `yolo` commands use the following syntax:
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```bash
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yolo TASK MODE ARGS
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Where TASK (optional) is one of [detect, segment, classify]
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MODE (required) is one of [train, val, predict, export, track]
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ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
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```
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See all ARGS in the full [Configuration Guide](./cfg.md) or with `yolo cfg`
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=== "Train"
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Train a detection model for 10 epochs with an initial learning_rate of 0.01
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```bash
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yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
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```
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=== "Predict"
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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 predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
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```
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=== "Val"
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Val a pretrained detection model at batch-size 1 and image size 640:
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```bash
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yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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```
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=== "Export"
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Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
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```bash
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yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
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```
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=== "Special"
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Run special commands to see version, view settings, run checks and more:
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```bash
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yolo help
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yolo checks
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yolo version
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yolo settings
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yolo copy-cfg
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yolo cfg
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```
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Where:
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@ -20,9 +66,9 @@ Where:
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For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
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GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
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!!! note ""
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!!! warning "Warning"
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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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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 `,` beteen arguments.
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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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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](cfg.md) page.
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!!! example ""
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!!! example "Example"
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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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yolo detect train resume model=last.pt # resume training
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```
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=== "Train"
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Start training YOLOv8n on COCO128 for 100 epochs at image-size 640.
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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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=== "Resume"
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Resume an interrupted training.
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```bash
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yolo detect train resume model=last.pt
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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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!!! example "Example"
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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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=== "Official"
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Validate an official YOLOv8n model.
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```bash
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yolo detect val model=yolov8n.pt
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```
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=== "Custom"
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Validate a custom-trained model.
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```bash
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yolo detect val model=path/to/best.pt
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```
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## Predict
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Use a trained YOLOv8n model to run predictions on images.
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!!! example ""
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!!! example "Example"
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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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=== "Official"
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Predict with an official YOLOv8n model.
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```bash
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yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
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```
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=== "Custom"
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Predict with a custom model.
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```bash
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yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'
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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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!!! example "Example"
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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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=== "Official"
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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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Export an official YOLOv8n model to ONNX format.
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```bash
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yolo export model=yolov8n.pt format=onnx
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```
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=== "Custom"
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Export a custom-trained model to ONNX format.
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```bash
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yolo export model=path/to/best.pt format=onnx
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```
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument,
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i.e. `format='onnx'` or `format='engine'`.
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| Format | `format` Argument | Model | Metadata |
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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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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
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| [TF.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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@ -99,19 +182,19 @@ Default arguments can be overridden by simply passing them as arguments in the C
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!!! tip ""
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=== "Example 1"
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=== "Train"
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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"
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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 segment predict model=yolov8n-seg.pt source='https://youtu.be/Zgi9g1ksQHc' imgsz=320
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```
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=== "Example 3"
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=== "Val"
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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 val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
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