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Model Export with Ultralytics YOLO

Ultralytics YOLO ecosystem and integrations

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

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

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

Arguments

This table details the configurations and options available for exporting YOLO models to different formats. These settings are critical for optimizing the exported model's performance, size, and compatibility across various platforms and environments. Proper configuration ensures that the model is ready for deployment in the intended application with optimal efficiency.

Argument Type Default Description
format str 'torchscript' Target format for the exported model, such as 'onnx', 'torchscript', 'tensorflow', or others, defining compatibility with various deployment environments.
imgsz int or tuple 640 Desired image size for the model input. Can be an integer for square images or a tuple (height, width) for specific dimensions.
keras bool False Enables export to Keras format for TensorFlow SavedModel, providing compatibility with TensorFlow serving and APIs.
optimize bool False Applies optimization for mobile devices when exporting to TorchScript, potentially reducing model size and improving performance.
half bool False Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware.
int8 bool False Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices.
dynamic bool False Allows dynamic input sizes for ONNX and TensorRT exports, enhancing flexibility in handling varying image dimensions.
simplify bool False Simplifies the model graph for ONNX exports with onnxslim, potentially improving performance and compatibility.
opset int None Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version.
workspace float 4.0 Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance.
nms bool False Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing.
batch int 1 Specifies export model batch inference size or the max number of images the exported model will process concurrently in predict mode.

Adjusting these parameters allows for customization of the export process to fit specific requirements, such as deployment environment, hardware constraints, and performance targets. Selecting the appropriate format and settings is essential for achieving the best balance between model size, speed, and accuracy.

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'. You can predict or validate directly on exported models, i.e. yolo predict model=yolov8n.onnx. Usage examples are shown for your model after export completes.

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


Created 2023-11-12, Updated 2024-06-10
Authors: glenn-jocher (15), Burhan-Q (4), Kayzwer (2)

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