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Model Export with Ultralytics YOLO


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.


  • 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.

from ultralytics import YOLO

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

# Export the model
yolo export format=onnx  # export official model
yolo export model=path/to/ format=onnx  # export custom trained model


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 - -
TorchScript torchscript yolov8n.torchscript imgsz, optimize
ONNX onnx yolov8n.onnx imgsz, half, dynamic, simplify, opset
OpenVINO openvino yolov8n_openvino_model/ imgsz, half
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
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
PaddlePaddle paddle yolov8n_paddle_model/ imgsz
ncnn ncnn yolov8n_ncnn_model/ imgsz, half

Created 2023-03-12, Updated 2023-09-13
Authors: glenn-jocher (12), sergiuwaxmann (1)