<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

**Export mode** is used for exporting a YOLOv8 model to a format that can be used for deployment. In this mode, the
model is converted to a format that can be used by other software applications or hardware devices. This mode is useful
when deploying the model to production environments.

!!! 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
        
        # 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/TF/TensorRT: dynamic axes                       |
| `simplify`  | `False`         | ONNX: 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 |
|--------------------------------------------------------------------|-------------------|---------------------------|----------|
| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        |
| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        |
| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        |
| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        |
| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        |
| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        |
| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        |
| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        |
| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        |