Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -16,6 +16,17 @@ Welcome to the Ultralytics documentation on how to use YOLOv8 with [SAHI](https:
SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimize object detection algorithms for large-scale and high-resolution imagery. Its core functionality lies in partitioning images into manageable slices, running object detection on each slice, and then stitching the results back together. SAHI is compatible with a range of object detection models, including the YOLO series, thereby offering flexibility while ensuring optimized use of computational resources. SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimize object detection algorithms for large-scale and high-resolution imagery. Its core functionality lies in partitioning images into manageable slices, running object detection on each slice, and then stitching the results back together. SAHI is compatible with a range of object detection models, including the YOLO series, thereby offering flexibility while ensuring optimized use of computational resources.
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/tq3FU_QczxE"
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<strong>Watch:</strong> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLOv8
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### Key Features of SAHI ### Key Features of SAHI
- **Seamless Integration**: SAHI integrates effortlessly with YOLO models, meaning you can start slicing and detecting without a lot of code modification. - **Seamless Integration**: SAHI integrates effortlessly with YOLO models, meaning you can start slicing and detecting without a lot of code modification.