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Add new Colab Notebooks badges to Docs (#18575)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
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Muhammad Rizwan Munawar 2025-01-10 05:10:59 +05:00 • committed by GitHub
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docs/en/guides/sahi-tiled-inference.md
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@ -6,6 +6,8 @@ keywords: YOLO11, SAHI, Sliced Inference, Object Detection, Ultralytics, High-re
# Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
<a href="https://colab.research.google.com/github/ultralytics/notebooks/blob/main/notebooks/how-to-use-ultralytics-yolo-with-sahi.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open SAHI for Sliced Inference In Colab"></a>
Welcome to the Ultralytics documentation on how to use YOLO11 with [SAHI](https://github.com/obss/sahi) (Slicing Aided Hyper Inference). This comprehensive guide aims to furnish you with all the essential knowledge you'll need to implement SAHI alongside YOLO11. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLO11 for enhanced [object detection](https://www.ultralytics.com/glossary/object-detection) performance.
<p align="center">
@ -117,7 +119,7 @@ from sahi.predict import get_prediction
result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
# With a numpy image
result = get_prediction(read_image("demo_data/small-vehicles1.jpeg"), detection_model)
result_with_np_image = get_prediction(read_image("demo_data/small-vehicles1.jpeg"), detection_model)
```
### Visualize Results