# YOLO11 with SAHI for Video Inference

[Slicing Aided Hyper Inference (SAHI)](https://github.com/obss/sahi) is a powerful technique designed to optimize [object detection](https://en.wikipedia.org/wiki/Object_detection) algorithms, particularly for large-scale and [high-resolution imagery](https://en.wikipedia.org/wiki/Image_resolution). It works by partitioning images or video frames into manageable slices, performing detection on each slice using models like [Ultralytics YOLO11](https://docs.ultralytics.com/models/yolo11), and then intelligently merging the results. This approach significantly improves detection accuracy for small objects and maintains performance on high-resolution inputs.

This tutorial guides you through running Ultralytics YOLO11 inference on video files using the SAHI library for enhanced detection capabilities. For a detailed guide on using SAHI with Ultralytics models, see the [SAHI Tiled Inference guide](https://docs.ultralytics.com/guides/sahi-tiled-inference).

## 📋 Table of Contents

- [Step 1: Install Required Libraries](#-step-1-install-required-libraries)
- [Step 2: Run Inference with SAHI using Ultralytics YOLO11](#-step-2-run-inference-with-sahi-using-ultralytics-yolo11)
- [Usage Options](#-usage-options)
- [Contribute](#-contribute)

## ⚙️ Step 1: Install Required Libraries

First, clone the [Ultralytics repository](https://github.com/ultralytics/ultralytics) to access the example script. Then, install the necessary [Python](https://www.python.org/) packages, including `sahi` and `ultralytics`, using [pip](https://pip.pypa.io/en/stable/). Finally, navigate into the example directory.

```bash
# Clone the ultralytics repository
git clone https://github.com/ultralytics/ultralytics

# Install dependencies
# Ensure you have Python 3.8 or later installed
pip install -U sahi ultralytics opencv-python

# Change directory to the example folder
cd ultralytics/examples/YOLOv8-SAHI-Inference-Video
```

## 🚀 Step 2: Run Inference with SAHI using Ultralytics YOLO11

Once the setup is complete, you can run inference on your video file. The provided script `yolov8_sahi.py` leverages SAHI for tiled inference with a specified YOLO11 model.

Execute the script using the command line, specifying the path to your video file. You can also choose different YOLO11 model weights.

```bash
# Run inference and save annotated output frames with bounding boxes
python yolov8_sahi.py --source "path/to/your/video.mp4" --save-img

# Run inference using a specific YOLO11 model (e.g., yolo11n.pt) and save results
python yolov8_sahi.py --source "path/to/your/video.mp4" --save-img --weights "yolo11n.pt"

# Run inference with smaller slices for potentially better small object detection
python yolov8_sahi.py --source "path/to/your/video.mp4" --save-img --slice-height 512 --slice-width 512
```

This script processes the video frame by frame, applying SAHI's slicing and inference logic. When saving is enabled, it exports annotated frames to `runs/detect/predict`. Learn more about prediction with Ultralytics models in the [Predict mode documentation](https://docs.ultralytics.com/modes/predict).

## 🛠️ Usage Options

The script `yolov8_sahi.py` accepts several command-line arguments to customize the inference process:

- `--source`: **Required**. Path to the input video file (e.g., `"../path/to/video.mp4"`).
- `--weights`: Optional. Path to the YOLO11 model weights file (e.g., `"yolo11n.pt"`, `"yolo11s.pt"`). Defaults to `"yolo11n.pt"`. You can download various models or use your custom-trained ones. See [Ultralytics YOLO models](https://docs.ultralytics.com/models) for more options.
- `--save-img`: Optional. Flag to export annotated frames. Saved to `runs/detect/predict`.
- `--slice-height`: Optional. Height of each image slice for SAHI. Defaults to `512`.
- `--slice-width`: Optional. Width of each image slice for SAHI. Defaults to `512`.

Experiment with these options, especially slice dimensions, to optimize detection performance for your specific [video processing](https://en.wikipedia.org/wiki/Video_processing) task and target object sizes. Using appropriate [datasets](https://docs.ultralytics.com/datasets) for training can also significantly impact performance.

## ✨ Contribute

Contributions to enhance this example or add new features are welcome! If you encounter issues or have suggestions, please open an issue or submit a pull request in the [Ultralytics GitHub repository](https://github.com/ultralytics/ultralytics). Check out our [contribution guide](https://docs.ultralytics.com/help/contributing) for more details.
