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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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Glenn Jocher 2024-09-15 00:27:46 +02:00 • committed by GitHub
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docs/en/guides/triton-inference-server.md
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@ -147,7 +147,7 @@ By following the above steps, you can deploy and run Ultralytics YOLOv8 models e
### How do I set up Ultralytics YOLOv8 with NVIDIA Triton Inference Server?
Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
Setting up [Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) with [NVIDIA Triton Inference Server](https://developer.nvidia.com/triton-inference-server) involves a few key steps:
1. **Export YOLOv8 to ONNX format**:
@ -258,7 +258,7 @@ For an in-depth guide on setting up and running Triton Server with YOLOv8, refer
### How does Ultralytics YOLOv8 compare to TensorFlow and PyTorch models for deployment?
[Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
[Ultralytics YOLOv8](https://docs.ultralytics.com/models/yolov8/) offers several unique advantages compared to TensorFlow and PyTorch models for deployment:
- **Real-time Performance**: Optimized for real-time object detection tasks, YOLOv8 provides state-of-the-art accuracy and speed, making it ideal for applications requiring live video analytics.
- **Ease of Use**: YOLOv8 integrates seamlessly with Triton Inference Server and supports diverse export formats (ONNX, TensorRT, CoreML), making it flexible for various deployment scenarios.