Optimizing Ultralytics YOLO models for latency and throughput with OpenVINO can significantly enhance your application's performance. By carefully applying the strategies outlined in this guide, developers can ensure their models run efficiently, meeting the demands of various deployment scenarios. Remember, the choice between optimizing for latency or throughput depends on your specific application needs and the characteristics of the deployment environment.
For more detailed technical information and the latest updates, refer to the [OpenVINO documentation](https://docs.openvino.ai/latest/index.html) and [Ultralytics YOLO repository](https://github.com/ultralytics/ultralytics). These resources provide in-depth guides, tutorials, and community support to help you get the most out of your deep learning models.
For more detailed technical information and the latest updates, refer to the [OpenVINO documentation](https://docs.openvino.ai/2024/index.html) and [Ultralytics YOLO repository](https://github.com/ultralytics/ultralytics). These resources provide in-depth guides, tutorials, and community support to help you get the most out of your deep learning models.
description: Learn how to deploy Ultralytics YOLO11 on Raspberry Pi with our comprehensive guide. Get performance benchmarks, setup instructions, and best practices.
| `bgr` | `float` | `0.0` | `0.0 - 1.0` | Flips the image channels from RGB to BGR with the specified probability, useful for increasing robustness to incorrect channel ordering. |
| `mosaic` | `float` | `1.0` | `0.0 - 1.0` | Combines four training images into one, simulating different scene compositions and object interactions. Highly effective for complex scene understanding. |
| `mixup` | `float` | `0.0` | `0.0 - 1.0` | Blends two images and their labels, creating a composite image. Enhances the model's ability to generalize by introducing label noise and visual variability. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies objects from one image and pastes them onto another, useful for increasing object instances and learning object occlusion. |
| `copy_paste` | `float` | `0.0` | `0.0 - 1.0` | Copies and pastes objects across images, useful for increasing object instances and learning object occlusion. Requires segmentation labels. |
| `copy_paste_mode` | `str` | `flip` | - | Copy-Paste augmentation method selection among the options of (`"flip"`, `"mixup"`). |
| `auto_augment` | `str` | `randaugment` | - | Automatically applies a predefined augmentation policy (`randaugment`, `autoaugment`, `augmix`), optimizing for classification tasks by diversifying the visual features. |
| `erasing` | `float` | `0.4` | `0.0 - 0.9` | Randomly erases a portion of the image during classification training, encouraging the model to focus on less obvious features for recognition. |
| `imgsz` | `int or tuple` | `640` | Defines the image size for inference. Can be a single integer `640` for square resizing or a (height, width) tuple. Proper sizing can improve detection [accuracy](https://www.ultralytics.com/glossary/accuracy) and processing speed. |
| `half` | `bool` | `False` | Enables half-[precision](https://www.ultralytics.com/glossary/precision) (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy. |
| `device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. |
| `batch` | `int` | `1` | Specifies the batch size for inference (only works when the source is [a directory, video file or `.txt` file](/modes/predict.md/#inference-sources)). A larger batch size can provide higher throughput, shortening the total amount of time required for inference. |
| `max_det` | `int` | `300` | Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes. |
| `vid_stride` | `int` | `1` | Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames. |
| `stream_buffer` | `bool` | `False` | Determines whether to queue incoming frames for video streams. If `False`, old frames get dropped to accomodate new frames (optimized for real-time applications). If `True', queues new frames in a buffer, ensuring no frames get skipped, but will cause latency if inference FPS is lower than stream FPS. |
@ -29,7 +29,6 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
- [Parking Management](../guides/parking-management.md) 🚀: Organize and direct vehicle flow in parking areas with YOLO11, optimizing space utilization and user experience.
- [Analytics](../guides/analytics.md) 📊: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
- [Live Inference with Streamlit](../guides/streamlit-live-inference.md) 🚀: Leverage the power of YOLO11 for real-time [object detection](https://www.ultralytics.com/glossary/object-detection) directly through your web browser with a user-friendly Streamlit interface.
- [Track Objects in Zone](../guides/trackzone.md) 🎯 NEW: Learn how to track objects within specific zones of video frames using YOLO11 for precise and efficient monitoring.
## Solutions Usage
@ -39,7 +38,7 @@ Here's our curated list of Ultralytics solutions that can be used to create awes
`yolo SOLUTIONS SOLUTION_NAME ARGS`
- **SOLUTIONS** is a required keyword.
- **SOLUTION_NAME** (optional) is one of: `['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics']`.
- **SOLUTION_NAME** (optional) is one of: `['count', 'heatmap', 'queue', 'speed', 'workout', 'analytics', 'trackzone']`.
- **ARGS** (optional) are custom `arg=value` pairs, such as `show_in=True`, to override default settings.
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