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Docs improvements and redirect fixes (#16287)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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10
docs/en/modes/export.md
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@ -46,7 +46,7 @@ Here are some of the standout functionalities:
## 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.
Export a YOLOv8n model to a different format like ONNX or TensorRT. See the Arguments section below for a full list of export arguments.
!!! example
@ -112,7 +112,7 @@ Exporting a YOLOv8 model to ONNX format is straightforward with Ultralytics. It
yolo export model=path/to/best.pt format=onnx # export custom trained model
```
For more details on the process, including advanced options like handling different input sizes, refer to the [ONNX](../integrations/onnx.md) section.
For more details on the process, including advanced options like handling different input sizes, refer to the [ONNX section](../integrations/onnx.md).
### What are the benefits of using TensorRT for model export?
@ -122,7 +122,7 @@ Using TensorRT for model export offers significant performance improvements. YOL
- **Speed:** Achieve faster inference through advanced optimizations.
- **Compatibility:** Integrate smoothly with NVIDIA hardware.
To learn more about integrating TensorRT, see the [TensorRT](../integrations/tensorrt.md) integration guide.
To learn more about integrating TensorRT, see the [TensorRT integration guide](../integrations/tensorrt.md).
### How do I enable INT8 quantization when exporting my YOLOv8 model?
@ -145,7 +145,7 @@ INT8 quantization is an excellent way to compress the model and speed up inferen
yolo export model=yolov8n.pt format=onnx int8=True # export model with INT8 quantization
```
INT8 quantization can be applied to various formats, such as TensorRT and CoreML. More details can be found in the [Export](../modes/export.md) section.
INT8 quantization can be applied to various formats, such as TensorRT and CoreML. More details can be found in the [Export section](../modes/export.md).
### Why is dynamic input size important when exporting models?
@ -182,4 +182,4 @@ Understanding and configuring export arguments is crucial for optimizing model p
- **`optimize:`** Applies specific optimizations for mobile or constrained environments.
- **`int8:`** Enables INT8 quantization, highly beneficial for edge deployments.
For a detailed list and explanations of all the export arguments, visit the [Export Arguments](#arguments) section.
For a detailed list and explanations of all the export arguments, visit the [Export Arguments section](#arguments).