> For the complete documentation index, see [llms.txt](https://campus-rover.gitbook.io/lab-notebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://campus-rover.gitbook.io/lab-notebook/zzz/computer-vision-with-model-yolo8.md).

# Computer Vision With Yolo8a

Yolo8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. It is a powerful model that can be used to detect multiple objects in an image.

It has been wrapped into a user-friendly python package Ultralytics (<https://docs.ultralytics.com/>). To detect objects of interest, the pre-trained model yolo8 can be used. Or one can customize the yolo8 model by training it with provided train image data. Here the website Roboflow (<https://roboflow.com/>) has a variety of object datasets, e.g. traffic sign dataset (<https://universe.roboflow.com/usmanchaudhry622-gmail-com/traffic-and-road-signs/model/1>). Once the dataset is downloaded and the Ultralytics package is installed, the yolo8 model can be trained easily:

```
yolo detect train model=yolov8n.pt data=traffic_sign.yaml
```

Where the traffic\_sign.yaml is the path to the yaml file inside your downloaded dataset from roboflow. You have the options to use various yolo8 models. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examples with these models.

| Model                                                                                | <p>size<br>(pixels)</p> | <p>mAPval<br>50-95</p> | <p>Speed<br>CPU ONNX<br>(ms)</p> | <p>Speed<br>A100 TensorRT<br>(ms)</p> | <p>params<br>(M)</p> | <p>FLOPs<br>(B)</p> |
| ------------------------------------------------------------------------------------ | ----------------------- | ---------------------- | -------------------------------- | ------------------------------------- | -------------------- | ------------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                     | 37.3                   | 80.4                             | 0.99                                  | 3.2                  | 8.7                 |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                     | 44.9                   | 128.4                            | 1.20                                  | 11.2                 | 28.6                |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                     | 50.2                   | 234.7                            | 1.83                                  | 25.9                 | 78.9                |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                     | 52.9                   | 375.2                            | 2.39                                  | 43.7                 | 165.2               |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                     | 53.9                   | 479.1                            | 3.53                                  | 68.2                 | 257.8               |

The larger the model is, the higher the latency will present in the ros node that holds the model. Therefore, the smaller model should be used as long as the model works for the objects of interest.

This is a quick ann simply way to train a customized model that is powerful in objects detection of robot vision.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://campus-rover.gitbook.io/lab-notebook/zzz/computer-vision-with-model-yolo8.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.