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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@ -4,70 +4,130 @@ Install YOLOv8 via the `ultralytics` pip package for the latest stable release o
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the [https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics) repository for the most
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up-to-date version.
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!!! example "Pip install method (recommended)"
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!!! example "Install"
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```bash
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pip install ultralytics
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```
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=== "pip install (recommended)"
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```bash
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pip install ultralytics
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```
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!!! example "Git clone method (for development)"
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=== "git clone (for development)"
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```bash
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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pip install -e .
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```
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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.
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!!! tip "Tip"
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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).
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<a href="https://pytorch.org/get-started/locally/">
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<img width="100%" alt="PyTorch Installation Instructions" src="https://user-images.githubusercontent.com/26833433/228650108-ab0ec98a-b328-4f40-a40d-95355e8a84e3.png">
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</a>
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```bash
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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pip install -e '.[dev]'
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```
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See contributing section to know more about contributing to the project
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## Use with CLI
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The YOLO command line interface (CLI) lets you simply train, validate or infer models on various tasks and versions.
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CLI requires no customization or code. You can simply run all tasks from the terminal with the `yolo` command.
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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. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
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!!! example
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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=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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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](usage/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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=== "Example training"
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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 detect train model=yolov8n.pt data=coco128.yaml device=0
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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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=== "Example Multi-GPU training"
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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 detect train model=yolov8n.pt data=coco128.yaml device=\'0,1,2,3\'
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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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!!! warning "Warning"
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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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- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌
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[CLI Guide](usage/cli.md){ .md-button .md-button--primary}
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## Use with Python
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Python usage allows users to easily use YOLOv8 inside their Python projects. It provides functions for loading and
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running the model, as well as for processing the model's output. The interface is designed to be easy to use, so that
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users can quickly implement object detection in their projects.
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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.
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Overall, the Python interface is a useful tool for anyone looking to incorporate object detection, segmentation or
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classification into their Python projects using YOLOv8.
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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.
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!!! example
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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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# Use the model
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results = model.train(data='coco128.yaml', epochs=3) # train the model
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results = model.val() # evaluate model performance on the validation set
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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success = model.export(format='onnx') # export the model to ONNX format
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# Create a new YOLO model from scratch
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model = YOLO('yolov8n.yaml')
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# Load a pretrained YOLO model (recommended for training)
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model = YOLO('yolov8n.pt')
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# Train the model using the 'coco128.yaml' dataset for 3 epochs
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results = model.train(data='coco128.yaml', epochs=3)
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# Evaluate the model's performance on the validation set
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results = model.val()
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# Perform object detection on an image using the model
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results = model('https://ultralytics.com/images/bus.jpg')
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# Export the model to ONNX format
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success = model.export(format='onnx')
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```
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[Python Guide](usage/python.md){.md-button .md-button--primary}
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