yolov8_test/docs/en/modes/export.md

comments	description	keywords
true	Step-by-step guide on exporting your YOLOv8 models to various format like ONNX, TensorRT, CoreML and more for deployment. Explore now!.	YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, TensorFlow SavedModel, OpenVINO, PyTorch, export model

Model Export with Ultralytics YOLO

Introduction

The ultimate goal of training a model is to deploy it for real-world applications. Export mode in Ultralytics YOLOv8 offers a versatile range of options for exporting your trained model to different formats, making it deployable across various platforms and devices. This comprehensive guide aims to walk you through the nuances of model exporting, showcasing how to achieve maximum compatibility and performance.

Watch: How To Export Custom Trained Ultralytics YOLOv8 Model and Run Live Inference on Webcam.

Why Choose YOLOv8's Export Mode?

• Versatility: Export to multiple formats including ONNX, TensorRT, CoreML, and more.
• Performance: Gain up to 5x GPU speedup with TensorRT and 3x CPU speedup with ONNX or OpenVINO.
• Compatibility: Make your model universally deployable across numerous hardware and software environments.
• Ease of Use: Simple CLI and Python API for quick and straightforward model exporting.

Key Features of Export Mode

Here are some of the standout functionalities:

• One-Click Export: Simple commands for exporting to different formats.
• Batch Export: Export batched-inference capable models.
• Optimized Inference: Exported models are optimized for quicker inference times.
• Tutorial Videos: In-depth guides and tutorials for a smooth exporting experience.

!!! Tip "Tip"

* Export to ONNX or OpenVINO for up to 3x CPU speedup.
* Export to TensorRT for up to 5x GPU speedup.

Usage Examples

Export a YOLOv8n model to a different format like ONNX or TensorRT. See Arguments section below for a full list of export arguments.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a model
    model = YOLO('yolov8n.pt')  # load an official model
    model = YOLO('path/to/best.pt')  # load a custom trained model

    # Export the model
    model.export(format='onnx')
    ```

=== "CLI"

    ```bash
    yolo export model=yolov8n.pt format=onnx  # export official model
    yolo export model=path/to/best.pt format=onnx  # export custom trained model
    ```

Arguments

Export settings for YOLO models refer to the various configurations and options used to save or export the model for use in other environments or platforms. These settings can affect the model's performance, size, and compatibility with different systems. Some common YOLO export settings include the format of the exported model file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the export process include the specific task the model is being used for and the requirements or constraints of the target environment or platform. It is important to carefully consider and configure these settings to ensure that the exported model is optimized for the intended use case and can be used effectively in the target environment.

Key	Value	Description
format	'torchscript'	format to export to
imgsz	640	image size as scalar or (h, w) list, i.e. (640, 480)
keras	False	use Keras for TF SavedModel export
optimize	False	TorchScript: optimize for mobile
half	False	FP16 quantization
int8	False	INT8 quantization
dynamic	False	ONNX/TensorRT: dynamic axes
simplify	False	ONNX/TensorRT: simplify model
opset	None	ONNX: opset version (optional, defaults to latest)
workspace	4	TensorRT: workspace size (GB)
nms	False	CoreML: add NMS

Export Formats

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	Arguments
PyTorch	-	yolov8n.pt	✅	-
TorchScript	torchscript	yolov8n.torchscript	✅	imgsz, optimize
ONNX	onnx	yolov8n.onnx	✅	imgsz, half, dynamic, simplify, opset
OpenVINO	openvino	yolov8n_openvino_model/	✅	imgsz, half, int8
TensorRT	engine	yolov8n.engine	✅	imgsz, half, dynamic, simplify, workspace
CoreML	coreml	yolov8n.mlpackage	✅	imgsz, half, int8, nms
TF SavedModel	saved_model	yolov8n_saved_model/	✅	imgsz, keras, int8
TF GraphDef	pb	yolov8n.pb	❌	imgsz
TF Lite	tflite	yolov8n.tflite	✅	imgsz, half, int8
TF Edge TPU	edgetpu	yolov8n_edgetpu.tflite	✅	imgsz
TF.js	tfjs	yolov8n_web_model/	✅	imgsz, half, int8
PaddlePaddle	paddle	yolov8n_paddle_model/	✅	imgsz
ncnn	ncnn	yolov8n_ncnn_model/	✅	imgsz, half