You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
6.1 KiB
86 lines
6.1 KiB
---
|
|
comments: true
|
|
description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
|
|
---
|
|
|
|
<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/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](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
|
|
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |
|
|
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
|
|
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
|
|
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
|
|
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |
|
|
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
|
|
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
|
|
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
|
|
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
|
|
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
|
|
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` | |