description: Learn how to implement YOLOv8 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
Welcome to the Ultralytics documentation on how to use YOLOv8 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 YOLOv8. We'll deep-dive into what SAHI is, why sliced inference is critical for large-scale applications, and how to integrate these functionalities with YOLOv8 for enhanced object detection performance.
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.
- **Seamless Integration**: SAHI integrates effortlessly with YOLO models, meaning you can start slicing and detecting without a lot of code modification.
- **Resource Efficiency**: By breaking down large images into smaller parts, SAHI optimizes the memory usage, allowing you to run high-quality detection on hardware with limited resources.
- **High Accuracy**: SAHI maintains the detection accuracy by employing smart algorithms to merge overlapping detection boxes during the stitching process.
Sliced Inference refers to the practice of subdividing a large or high-resolution image into smaller segments (slices), conducting object detection on these slices, and then recompiling the slices to reconstruct the object locations on the original image. This technique is invaluable in scenarios where computational resources are limited or when working with extremely high-resolution images that could otherwise lead to memory issues.
### Benefits of Sliced Inference
- **Reduced Computational Burden**: Smaller image slices are faster to process, and they consume less memory, enabling smoother operation on lower-end hardware.
- **Preserved Detection Quality**: Since each slice is treated independently, there is no reduction in the quality of object detection, provided the slices are large enough to capture the objects of interest.
- **Enhanced Scalability**: The technique allows for object detection to be more easily scaled across different sizes and resolutions of images, making it ideal for a wide range of applications from satellite imagery to medical diagnostics.
We extend our thanks to the SAHI research group for creating and maintaining this invaluable resource for the computer vision community. For more information about SAHI and its creators, visit the [SAHI GitHub repository](https://github.com/obss/sahi).
### How can I integrate YOLOv8 with SAHI for sliced inference in object detection?
Integrating Ultralytics YOLOv8 with SAHI (Slicing Aided Hyper Inference) for sliced inference optimizes your object detection tasks on high-resolution images by partitioning them into manageable slices. This approach improves memory usage and ensures high detection accuracy. To get started, you need to install the ultralytics and sahi libraries:
```bash
pip install -U ultralytics sahi
```
Then, download a YOLOv8 model and test images:
```python
from sahi.utils.file import download_from_url
from sahi.utils.yolov8 import download_yolov8s_model
For more detailed instructions, refer to our [Sliced Inference guide](#sliced-inference-with-yolov8).
### Why should I use SAHI with YOLOv8 for object detection on large images?
Using SAHI with Ultralytics YOLOv8 for object detection on large images offers several benefits:
- **Reduced Computational Burden**: Smaller slices are faster to process and consume less memory, making it feasible to run high-quality detections on hardware with limited resources.
- **Maintained Detection Accuracy**: SAHI uses intelligent algorithms to merge overlapping boxes, preserving the detection quality.
- **Enhanced Scalability**: By scaling object detection tasks across different image sizes and resolutions, SAHI becomes ideal for various applications, such as satellite imagery analysis and medical diagnostics.
Learn more about the [benefits of sliced inference](#benefits-of-sliced-inference) in our documentation.
### Can I visualize prediction results when using YOLOv8 with SAHI?
Yes, you can visualize prediction results when using YOLOv8 with SAHI. Here's how you can export and visualize the results:
This command will save the visualized predictions to the specified directory and you can then load the image to view it in your notebook or application. For a detailed guide, check out the [Standard Inference section](#visualize-results).
### What features does SAHI offer for improving YOLOv8 object detection?
SAHI (Slicing Aided Hyper Inference) offers several features that complement Ultralytics YOLOv8 for object detection:
