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README.md

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+---
+license: agpl-3.0
+pipeline_tag: object-detection
+tags:
+- ultralytics
+- tracking
+- instance-segmentation
+- image-classification
+- pose-estimation
+- obb
+- object-detection
+- yolo
+- yolov8
+- license_plate
+- Iran
+- veichle_lisence_plate
+
+---
+
+## <div align="center">Documentation</div>
+
+See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com/) for full documentation on training, validation, prediction and deployment.
+
+<details open>
+<summary>Install</summary>
+
+Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
+
+[![PyPI - Version](https://img.shields.io/pypi/v/ultralytics?logo=pypi&logoColor=white)](https://pypi.org/project/ultralytics/) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ultralytics?logo=python&logoColor=gold)](https://pypi.org/project/ultralytics/)
+
+```bash
+pip install ultralytics
+```
+
+For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart/).
+
+[![Conda Version](https://img.shields.io/conda/vn/conda-forge/ultralytics?logo=condaforge)](https://anaconda.org/conda-forge/ultralytics) [![Docker Image Version](https://img.shields.io/docker/v/ultralytics/ultralytics?sort=semver&logo=docker)](https://hub.docker.com/r/ultralytics/ultralytics)
+
+</details>
+
+<details open>
+<summary>Usage</summary>
+
+### CLI
+
+YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
+
+```bash
+yolo predict model=YOLOv8m_Iran_license_plate_detection.pt source='your_image.jpg'
+```
+
+`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8 [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples.
+
+### Python
+
+YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+
+```python
+from ultralytics import YOLO
+# Load a model
+model = YOLO("YOLOv8m_Iran_license_plate_detection.pt")
+# Train the model
+train_results = model.train(
+    data="Iran_license_plate.yaml",  # path to dataset YAML
+    epochs=100,  # number of training epochs
+    imgsz=640,  # training image size
+    device="cpu",  # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu
+)
+# Evaluate model performance on the validation set
+metrics = model.val()
+# Perform object detection on an image
+results = model("path/to/image.jpg")
+results[0].show()
+# Export the model to ONNX format
+path = model.export(format="onnx")  # return path to exported model
+```
+
+See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.
+
+</details>