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Add YOLOv5 tutorials to docs.ultralytics.com (#1657)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
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Co-authored-by: Sergiu Waxmann <47978446+sergiuwaxmann@users.noreply.github.com>
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The YOLO Command Line Interface (CLI) is the easiest way to get started training, validating, predicting and exporting
YOLOv8 models.
# Command Line Interface Usage
The `yolo` command is used for all actions:
The YOLO command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command.
!!! example ""
!!! example
=== "CLI"
=== "Syntax"
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify]
MODE (required) is one of [train, val, predict, export, track]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
```
See all ARGS in the full [Configuration Guide](./cfg.md) or with `yolo cfg`
=== "Train"
Train a detection model for 10 epochs with an initial learning_rate of 0.01
```bash
yolo train data=coco128.yaml model=yolov8n.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/Zgi9g1ksQHc' imgsz=320
```
=== "Val"
Val a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
```
=== "Export"
Export a YOLOv8n 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
```
=== "Special"
Run special commands to see version, view settings, run checks and more:
```bash
yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg
```
Where:
@ -20,9 +66,9 @@ Where:
For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
!!! note ""
!!! warning "Warning"
<b>Note:</b> Arguments MUST be passed as `arg=val` with an equals sign and a space between `arg=val` pairs
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.
- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` &nbsp; ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` &nbsp; ❌
@ -33,63 +79,100 @@ Where:
Train YOLOv8n on the COCO128 dataset for 100 epochs at image size 640. For a full list of available arguments see
the [Configuration](cfg.md) page.
!!! example ""
!!! example "Example"
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
yolo detect train resume model=last.pt # resume training
```
=== "Train"
Start training YOLOv8n on COCO128 for 100 epochs at image-size 640.
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=100 imgsz=640
```
=== "Resume"
Resume an interrupted training.
```bash
yolo detect train resume model=last.pt
```
## Val
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
training `data` and arguments as model attributes.
!!! example ""
!!! example "Example"
```bash
yolo detect val model=yolov8n.pt # val official model
yolo detect val model=path/to/best.pt # val custom model
```
=== "Official"
Validate an official YOLOv8n model.
```bash
yolo detect val model=yolov8n.pt
```
=== "Custom"
Validate a custom-trained model.
```bash
yolo detect val model=path/to/best.pt
```
## Predict
Use a trained YOLOv8n model to run predictions on images.
!!! example ""
!!! example "Example"
```bash
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
```
=== "Official"
Predict with an official YOLOv8n model.
```bash
yolo detect predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
```
=== "Custom"
Predict with a custom model.
```bash
yolo detect predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg'
```
## Export
Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
!!! example ""
!!! example "Example"
```bash
yolo export model=yolov8n.pt format=onnx # export official model
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
=== "Official"
Available YOLOv8 export formats include:
| Format | `format=` | Model |
|----------------------------------------------------------------------------|--------------------|---------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` |
Export an official YOLOv8n model to ONNX format.
```bash
yolo export model=yolov8n.pt format=onnx
```
=== "Custom"
Export a custom-trained model to ONNX format.
```bash
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'`.
| Format | `format` Argument | Model | Metadata |
|--------------------------------------------------------------------|-------------------|---------------------------|----------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ |
---
@ -99,19 +182,19 @@ Default arguments can be overridden by simply passing them as arguments in the C
!!! tip ""
=== "Example 1"
=== "Train"
Train a detection model for `10 epochs` with `learning_rate` of `0.01`
```bash
yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
```
=== "Example 2"
=== "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/Zgi9g1ksQHc' imgsz=320
```
=== "Example 3"
=== "Val"
Validate a pretrained detection model at batch-size 1 and image size 640:
```bash
yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640

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The simplest way of simply using YOLOv8 directly in a Python environment.
# Python Usage
Welcome to the YOLOv8 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLOv8 into
your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use
pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable
resource for anyone looking to incorporate YOLOv8 into their Python projects, allowing you to quickly implement advanced
object detection capabilities. Let's get started!
For example, users can load a model, train it, evaluate its performance on a validation set, and even export it to ONNX
format with just a few lines of code.
!!! example "Python"
```python
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.yaml', epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model('https://ultralytics.com/images/bus.jpg')
# Export the model to ONNX format
success = model.export(format='onnx')
```
## [Train](../modes/train.md)
Train mode is used for training a YOLOv8 model on a custom dataset. In this mode, the model is trained using the
specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can
accurately predict the classes and locations of objects in an image.
!!! example "Train"
@ -25,6 +64,14 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
model.train(resume=True)
```
[Train Examples](../modes/train.md){ .md-button .md-button--primary}
## [Val](../modes/val.md)
Val mode is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a
validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters
of the model to improve its performance.
!!! example "Val"
=== "Val after training"
@ -47,6 +94,14 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
model.val(data='coco128.yaml')
```
[Val Examples](../modes/val.md){ .md-button .md-button--primary}
## [Predict](../modes/predict.md)
Predict mode is used for making predictions using a trained YOLOv8 model on new images or videos. In this mode, the
model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model
predicts the classes and locations of objects in the input images or videos.
!!! example "Predict"
=== "From source"
@ -108,30 +163,86 @@ The simplest way of simply using YOLOv8 directly in a Python environment.
result = result.numpy()
```
!!! note "Export and Deployment"
[Predict Examples](../modes/predict.md){ .md-button .md-button--primary}
=== "Export, Fuse & info"
## [Export](../modes/export.md)
Export mode is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the model is
converted to a format that can be used by other software applications or hardware devices. This mode is useful when
deploying the model to production environments.
!!! example "Export"
=== "Export to ONNX"
Export an official YOLOv8n model to ONNX with dynamic batch-size and image-size.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.export(format='onnx', dynamic=True)
```
=== "Export to TensorRT"
Export an official YOLOv8n model to TensorRT on `device=0` for acceleration on CUDA devices.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.export(format='onnx', device=0)
```
[Export Examples](../modes/export.md){ .md-button .md-button--primary}
## [Track](../modes/track.md)
Track mode is used for tracking objects in real-time using a YOLOv8 model. In this mode, the model is loaded from a
checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful
for applications such as surveillance systems or self-driving cars.
!!! example "Track"
=== "Python"
```python
from ultralytics import YOLO
model = YOLO("model.pt")
model.fuse()
model.info(verbose=True) # Print model information
model.export(format=) # TODO:
# Load a model
model = YOLO('yolov8n.pt') # load an official detection model
model = YOLO('yolov8n-seg.pt') # load an official segmentation model
model = YOLO('path/to/best.pt') # load a custom model
# Track with the model
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True)
results = model.track(source="https://youtu.be/Zgi9g1ksQHc", show=True, tracker="bytetrack.yaml")
```
=== "Deployment"
[Track Examples](../modes/track.md){ .md-button .md-button--primary}
More functionality coming soon
## [Benchmark](../modes/benchmark.md)
To know more about using `YOLO` models, refer Model class Reference
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks provide
information on the size of the exported format, its `mAP50-95` metrics (for object detection and segmentation)
or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
their specific use case based on their requirements for speed and accuracy.
[Model reference](../reference/model.md){ .md-button .md-button--primary}
!!! example "Benchmark"
---
=== "Python"
Benchmark an official YOLOv8n model across all export formats.
```python
from ultralytics.yolo.utils.benchmarks import benchmark
# Benchmark
benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
```
### Using Trainers
[Benchmark Examples](../modes/benchmark.md){ .md-button .md-button--primary}
## Using Trainers
`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits
from `BaseTrainer`.