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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/streamlit-live-inference.md
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@ -152,7 +152,7 @@ Real-time object detection using Streamlit and Ultralytics YOLOv8 can be applied
- **Retail**: Customer counting, shelf management, and more.
- **Wildlife and Agriculture**: Monitoring animals and crop conditions.
For more in-depth use cases and examples, explore [Ultralytics Solutions](https://docs.ultralytics.com/solutions).
For more in-depth use cases and examples, explore [Ultralytics Solutions](https://docs.ultralytics.com/solutions/).
### How does Ultralytics YOLOv8 compare to other object detection models like YOLOv5 and RCNNs?
@ -162,4 +162,4 @@ Ultralytics YOLOv8 provides several enhancements over prior models like YOLOv5 a
- **Ease of Use**: Simplified interfaces and deployment.
- **Resource Efficiency**: Optimized for better speed with minimal computational requirements.
For a comprehensive comparison, check [Ultralytics YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8) and related blog posts discussing model performance.
For a comprehensive comparison, check [Ultralytics YOLOv8 Documentation](https://docs.ultralytics.com/models/yolov8/) and related blog posts discussing model performance.