You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
134 lines
5.0 KiB
134 lines
5.0 KiB
## Install
|
|
|
|
Install YOLOv8 via the `ultralytics` pip package for the latest stable release or by cloning
|
|
the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
|
|
up-to-date version.
|
|
|
|
!!! example "Install"
|
|
|
|
=== "pip install (recommended)"
|
|
```bash
|
|
pip install ultralytics
|
|
```
|
|
|
|
=== "git clone (for development)"
|
|
```bash
|
|
git clone https://github.com/ultralytics/ultralytics
|
|
cd ultralytics
|
|
pip install -e .
|
|
```
|
|
|
|
See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) file for a list of dependencies. Note that `pip` automatically installs all required dependencies.
|
|
|
|
!!! tip "Tip"
|
|
|
|
PyTorch requirements vary by operating system and CUDA requirements, so it's recommended to install PyTorch first following instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).
|
|
|
|
<a href="https://pytorch.org/get-started/locally/">
|
|
<img width="800" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
|
|
</a>
|
|
|
|
|
|
## Use with CLI
|
|
|
|
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. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
|
|
|
|
|
|
!!! example
|
|
|
|
=== "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](usage/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
|
|
```
|
|
|
|
|
|
!!! warning "Warning"
|
|
|
|
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` ✅
|
|
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` ❌
|
|
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌
|
|
|
|
[CLI Guide](usage/cli.md){ .md-button .md-button--primary}
|
|
|
|
## Use with Python
|
|
|
|
YOLOv8's Python interface allows for seamless integration into your Python projects, making it easy to load, run, and process the model's output. Designed with simplicity and ease of use in mind, the Python interface enables users to quickly implement object detection, segmentation, and classification in their projects. This makes YOLOv8's Python interface an invaluable tool for anyone looking to incorporate these functionalities into their Python projects.
|
|
|
|
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. Check out the [Python Guide](usage/python.md) to learn more about using YOLOv8 within your Python projects.
|
|
|
|
!!! example
|
|
|
|
```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')
|
|
```
|
|
|
|
[Python Guide](usage/python.md){.md-button .md-button--primary}
|