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---
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comments: true
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description: Step-by-step guide on exporting your YOLOv8 models to various format like ONNX, TensorRT, CoreML and more for deployment. Explore now!.
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keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, TensorFlow SavedModel, OpenVINO, PyTorch, export model
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---
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
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**Export mode** is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the
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model is converted to a format that can be used by other software applications or hardware devices. This mode is useful
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when deploying the model to production environments.
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!!! tip "Tip"
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* Export to ONNX or OpenVINO for up to 3x CPU speedup.
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* Export to TensorRT for up to 5x GPU speedup.
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## Usage Examples
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Export a YOLOv8n model to a different format like ONNX or TensorRT. See Arguments section below for a full list of
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export arguments.
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!!! example ""
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=== "Python"
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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.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format='onnx')
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```
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=== "CLI"
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```bash
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yolo export model=yolov8n.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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## Arguments
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Export settings for YOLO models refer to the various configurations and options used to save or
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export the model for use in other environments or platforms. These settings can affect the model's performance, size,
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and compatibility with different systems. Some common YOLO export settings include the format of the exported model
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file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
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additional features such as masks or multiple labels per box. Other factors that may affect the export process include
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the specific task the model is being used for and the requirements or constraints of the target environment or platform.
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It is important to carefully consider and configure these settings to ensure that the exported model is optimized for
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the intended use case and can be used effectively in the target environment.
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| Key | Value | Description |
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|-------------|-----------------|------------------------------------------------------|
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| `format` | `'torchscript'` | format to export to |
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| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
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| `keras` | `False` | use Keras for TF SavedModel export |
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| `optimize` | `False` | TorchScript: optimize for mobile |
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| `half` | `False` | FP16 quantization |
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| `int8` | `False` | INT8 quantization |
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| `dynamic` | `False` | ONNX/TensorRT: dynamic axes |
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| `simplify` | `False` | ONNX/TensorRT: simplify model |
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| `opset` | `None` | ONNX: opset version (optional, defaults to latest) |
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| `workspace` | `4` | TensorRT: workspace size (GB) |
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| `nms` | `False` | CoreML: add NMS |
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## Export Formats
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Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument,
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i.e. `format='onnx'` or `format='engine'`.
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| Format | `format` Argument | Model | Metadata | Arguments |
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|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |
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