Update YOLO11 Actions and Docs (#16596)
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---
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comments: true
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description: Learn how to implement YOLOv8 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
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keywords: YOLOv8, SAHI, Sliced Inference, Object Detection, Ultralytics, High-resolution Images, Computational Efficiency, Integration Guide
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description: Learn how to implement YOLO11 with SAHI for sliced inference. Optimize memory usage and enhance detection accuracy for large-scale applications.
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keywords: YOLO11, SAHI, Sliced Inference, Object Detection, Ultralytics, High-resolution Images, Computational Efficiency, Integration Guide
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---
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# Ultralytics Docs: Using YOLOv8 with SAHI for Sliced Inference
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# Ultralytics Docs: Using YOLO11 with SAHI for Sliced Inference
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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](https://www.ultralytics.com/glossary/object-detection) performance.
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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.
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<p align="center">
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<img width="1024" src="https://github.com/ultralytics/docs/releases/download/0/sahi-sliced-inference-overview.avif" alt="SAHI Sliced Inference Overview">
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@ -24,7 +24,7 @@ SAHI (Slicing Aided Hyper Inference) is an innovative library designed to optimi
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allowfullscreen>
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</iframe>
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<br>
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<strong>Watch:</strong> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLOv8
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<strong>Watch:</strong> Inference with SAHI (Slicing Aided Hyper Inference) using Ultralytics YOLO11
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</p>
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### Key Features of SAHI
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@ -47,12 +47,12 @@ Sliced Inference refers to the practice of subdividing a large or high-resolutio
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<table border="0">
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<tr>
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<th>YOLOv8 without SAHI</th>
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<th>YOLOv8 with SAHI</th>
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<th>YOLO11 without SAHI</th>
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<th>YOLO11 with SAHI</th>
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</tr>
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<tr>
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<td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-without-sahi.avif" alt="YOLOv8 without SAHI" width="640"></td>
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<td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-sahi.avif" alt="YOLOv8 with SAHI" width="640"></td>
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<td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-without-sahi.avif" alt="YOLO11 without SAHI" width="640"></td>
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<td><img src="https://github.com/ultralytics/docs/releases/download/0/yolov8-with-sahi.avif" alt="YOLO11 with SAHI" width="640"></td>
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</tr>
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</table>
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@ -68,15 +68,15 @@ pip install -U ultralytics sahi
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### Import Modules and Download Resources
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Here's how to import the necessary modules and download a YOLOv8 model and some test images:
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Here's how to import the necessary modules and download a YOLO11 model and some test images:
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```python
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from sahi.utils.file import download_from_url
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from sahi.utils.yolov8 import download_yolov8s_model
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# Download YOLOv8 model
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yolov8_model_path = "models/yolov8s.pt"
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download_yolov8s_model(yolov8_model_path)
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# Download YOLO11 model
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model_path = "models/yolo11s.pt"
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download_yolov8s_model(model_path)
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# Download test images
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download_from_url(
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@ -89,11 +89,11 @@ download_from_url(
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)
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```
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## Standard Inference with YOLOv8
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## Standard Inference with YOLO11
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### Instantiate the Model
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You can instantiate a YOLOv8 model for object detection like this:
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You can instantiate a YOLO11 model for object detection like this:
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```python
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from sahi import AutoDetectionModel
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@ -129,7 +129,7 @@ result.export_visuals(export_dir="demo_data/")
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Image("demo_data/prediction_visual.png")
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```
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## Sliced Inference with YOLOv8
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## Sliced Inference with YOLO11
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Perform sliced inference by specifying the slice dimensions and overlap ratios:
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@ -170,7 +170,7 @@ from sahi.predict import predict
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predict(
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model_type="yolov8",
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model_path="path/to/yolov8n.pt",
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model_path="path/to/yolo11n.pt",
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model_device="cpu", # or 'cuda:0'
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model_confidence_threshold=0.4,
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source="path/to/dir",
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@ -181,7 +181,7 @@ predict(
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)
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```
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That's it! Now you're equipped to use YOLOv8 with SAHI for both standard and sliced inference.
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That's it! Now you're equipped to use YOLO11 with SAHI for both standard and sliced inference.
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## Citations and Acknowledgments
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@ -206,23 +206,23 @@ We extend our thanks to the SAHI research group for creating and maintaining thi
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## FAQ
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### How can I integrate YOLOv8 with SAHI for sliced inference in object detection?
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### How can I integrate YOLO11 with SAHI for sliced inference in object detection?
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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:
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Integrating Ultralytics YOLO11 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:
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```bash
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pip install -U ultralytics sahi
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```
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Then, download a YOLOv8 model and test images:
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Then, download a YOLO11 model and test images:
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```python
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from sahi.utils.file import download_from_url
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from sahi.utils.yolov8 import download_yolov8s_model
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# Download YOLOv8 model
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yolov8_model_path = "models/yolov8s.pt"
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download_yolov8s_model(yolov8_model_path)
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# Download YOLO11 model
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model_path = "models/yolo11s.pt"
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download_yolov8s_model(model_path)
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# Download test images
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download_from_url(
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```
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For more detailed instructions, refer to our [Sliced Inference guide](#sliced-inference-with-yolov8).
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For more detailed instructions, refer to our [Sliced Inference guide](#sliced-inference-with-yolo11).
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### Why should I use SAHI with YOLOv8 for object detection on large images?
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### Why should I use SAHI with YOLO11 for object detection on large images?
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Using SAHI with Ultralytics YOLOv8 for object detection on large images offers several benefits:
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Using SAHI with Ultralytics YOLO11 for object detection on large images offers several benefits:
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- **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.
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- **Maintained Detection Accuracy**: SAHI uses intelligent algorithms to merge overlapping boxes, preserving the detection quality.
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@ -243,9 +243,9 @@ Using SAHI with Ultralytics YOLOv8 for object detection on large images offers s
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Learn more about the [benefits of sliced inference](#benefits-of-sliced-inference) in our documentation.
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### Can I visualize prediction results when using YOLOv8 with SAHI?
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### Can I visualize prediction results when using YOLO11 with SAHI?
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Yes, you can visualize prediction results when using YOLOv8 with SAHI. Here's how you can export and visualize the results:
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Yes, you can visualize prediction results when using YOLO11 with SAHI. Here's how you can export and visualize the results:
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```python
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from IPython.display import Image
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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).
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### What features does SAHI offer for improving YOLOv8 object detection?
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### What features does SAHI offer for improving YOLO11 object detection?
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SAHI (Slicing Aided Hyper Inference) offers several features that complement Ultralytics YOLOv8 for object detection:
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SAHI (Slicing Aided Hyper Inference) offers several features that complement Ultralytics YOLO11 for object detection:
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- **Seamless Integration**: SAHI easily integrates with YOLO models, requiring minimal code adjustments.
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- **Resource Efficiency**: It partitions large images into smaller slices, which optimizes memory usage and speed.
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@ -266,9 +266,9 @@ SAHI (Slicing Aided Hyper Inference) offers several features that complement Ult
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For a deeper understanding, read about SAHI's [key features](#key-features-of-sahi).
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### How do I handle large-scale inference projects using YOLOv8 and SAHI?
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### How do I handle large-scale inference projects using YOLO11 and SAHI?
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To handle large-scale inference projects using YOLOv8 and SAHI, follow these best practices:
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To handle large-scale inference projects using YOLO11 and SAHI, follow these best practices:
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1. **Install Required Libraries**: Ensure that you have the latest versions of ultralytics and sahi.
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2. **Configure Sliced Inference**: Determine the optimal slice dimensions and overlap ratios for your specific project.
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@ -281,7 +281,7 @@ from sahi.predict import predict
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predict(
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model_type="yolov8",
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model_path="path/to/yolov8n.pt",
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model_path="path/to/yolo11n.pt",
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model_device="cpu", # or 'cuda:0'
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model_confidence_threshold=0.4,
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source="path/to/dir",
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