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* optimize yolo docs
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‎docs/doc/en/sidebar.yaml‎

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- file: vision/classify.md
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label: AI object classification
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- file: vision/yolov5.md
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label: YOLO11/v8/v5 object detection
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label: YOLO object detection
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- file: vision/face_detection.md
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label: Face and keypoints detection
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- file: vision/face_landmarks.md
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- file: vision/customize_model_yolov5.md
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label: YOLOv5 model offline training
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- file: vision/customize_model_yolov8.md
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label: YOLO11/v8 model offline training
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label: YOLO26/YOLO11/v8 model offline training
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- label: AI audio

‎docs/doc/en/vision/customize_model_yolov8.md‎

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---
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title: Offline Training YOLO11/YOLOv8/YOLO26 Models for MaixCAM with MaixPy:Custom Object Detection & Keypoint Detection
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title: Offline Training YOLO26/YOLO11/YOLOv8 Models for MaixCAM with MaixPy:Custom Object Detection & Keypoint Detection
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update:
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- date: 2024-06-21
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version: v1.0
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| YOLO11-seg Segmentation |`/model.23/Concat_1_output_0`<br>`/model.23/Concat_2_output_0`<br>`/model.23/Concat_3_output_0`<br>`/model.23/Concat_output_0`<br>`output1`|`/model.23/dfl/conv/Conv_output_0`<br>`/model.23/Sigmoid_output_0`<br>`/model.23/Concat_output_0`<br>`output1`|
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| YOLOv8-obb Oriented BBox |`/model.22/Concat_1_output_0`<br>`/model.22/Concat_2_output_0`<br>`/model.22/Concat_3_output_0`<br>`/model.22/Concat_output_0`|`/model.22/dfl/conv/Conv_output_0`<br>`/model.22/Sigmoid_1_output_0`<br>`/model.22/Sigmoid_output_0`|
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| YOLO11-obb Oriented BBox |`/model.23/Concat_1_output_0`<br>`/model.23/Concat_2_output_0`<br>`/model.23/Concat_3_output_0`<br>`/model.23/Concat_output_0`|`/model.23/dfl/conv/Conv_output_0`<br>`/model.23/Sigmoid_1_output_0`<br>`/model.23/Sigmoid_output_0`|
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| YOLO26 Detection |`/model.23/one2one_cv2.0/one2one_cv2.0.2/Conv_output_0 /model.23/one2one_cv2.1/one2one_cv2.1.2/Conv_output_0 /model.23/one2one_cv2.2/one2one_cv2.2.2/Conv_output_0 /model.23/one2one_cv3.0/one2one_cv3.0.2/Conv_output_0 /model.23/one2one_cv3.1/one2one_cv3.1.2/Conv_output_0 /model.23/one2one_cv3.2/one2one_cv3.2.2/Conv_output_0`|Same as MaixCAM2|
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|YOLOv8/YOLO11 Detection Output Nodes| ![](../../assets/yolo11_detect_nodes.png) | ![](../../assets/yolov8_out.jpg)|
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|YOLOv8/YOLO11 Pose Extra Output Node | ![](../../assets/yolo11_pose_node.png) | See pose branch above |
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|YOLOv8/YOLO11 Seg Extra Output Node | ![](../../assets/yolo11_seg_node.png) | ![](../../assets/yolo11_seg_node.png)|
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|YOLOv8/YOLO11 OBB Extra Output Node | ![](../../assets/yolo11_obb_node.png) | ![](../../assets/yolo11_out_obb.jpg)|
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| Model | Node Names | Node Diagram |
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| -- | --- | --- |
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| YOLO26 Detection | `/model.23/one2one_cv2.0/one2one_cv2.0.2/Conv_output_0 /model.23/one2one_cv2.1/one2one_cv2.1.2/Conv_output_0 /model.23/one2one_cv2.2/one2one_cv2.2.2/Conv_output_0 /model.23/one2one_cv3.0/one2one_cv3.0.2/Conv_output_0 /model.23/one2one_cv3.1/one2one_cv3.1.2/Conv_output_0 /model.23/one2one_cv3.2/one2one_cv3.2.2/Conv_output_0` |![](../../assets/yolo26_out.png) |
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|YOLO26 Detection Output Nodes | ![](../../assets/yolo26_out.png) | Same as MaixCAM2 |
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### Conversion Scripts
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*One-click YOLO26 conversion script (run in container):*

‎docs/doc/en/vision/yolov5.md‎

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# MaixPy: Object Detection with YOLOv5 / YOLOv8 / YOLO11 / YOLO26 Models
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# MaixPy: Object Detection with YOLO Models
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## Concept of Object Detection
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Object detection refers to identifying the positions and categories of targets in images or videos—for example, detecting objects like apples and airplanes in an image and marking their locations.
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‎docs/doc/zh/sidebar.yaml‎

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- file: vision/classify.md
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label: AI 物体分类
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- file: vision/yolov5.md
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label: YOLO11/v8/v5 物体检测
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label: YOLO 物体检测
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- file: vision/face_detection.md
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label: 人脸及关键点检测
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- file: vision/face_landmarks.md
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- file: vision/customize_model_yolov5.md
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label: 离线训练 YOLOv5 模型
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- file: vision/customize_model_yolov8.md
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label: 离线训练 YOLO11/YOLOv8 模型
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label: 离线训练 YOLO26/YOLO11/YOLOv8 模型
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- label: AI 听觉
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items:

‎docs/doc/zh/vision/customize_model_yolov8.md‎

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title: 为 MaixCAM MaixPy 离线训练 YOLO11/YOLOv8/YOLO26 模型，自定义检测物体、关键点检测
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title: 为 MaixCAM MaixPy 离线训练 YOLO26/YOLO11/YOLOv8 模型，自定义检测物体、关键点检测
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- date: 2024-06-21
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version: v1.0
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| 分割 YOLO11-seg |`/model.23/Concat_1_output_0`<br>`/model.23/Concat_2_output_0`<br>`/model.23/Concat_3_output_0`<br>`/model.23/Concat_output_0`<br>`output1`|`/model.23/dfl/conv/Conv_output_0`<br>`/model.23/Sigmoid_output_0`<br>`/model.23/Concat_output_0`<br>`output1`|
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| 旋转框 YOLOv8-obb |`/model.22/Concat_1_output_0`<br>`/model.22/Concat_2_output_0`<br>`/model.22/Concat_3_output_0`<br>`/model.22/Concat_output_0`|`/model.22/dfl/conv/Conv_output_0`<br>`/model.22/Sigmoid_1_output_0`<br>`/model.22/Sigmoid_output_0`|
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| 旋转框 YOLO11-obb |`/model.23/Concat_1_output_0`<br>`/model.23/Concat_2_output_0`<br>`/model.23/Concat_3_output_0`<br>`/model.23/Concat_output_0`|`/model.23/dfl/conv/Conv_output_0`<br>`/model.23/Sigmoid_1_output_0`<br>`/model.23/Sigmoid_output_0`|
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| 检测 YOLO26 |`/model.23/one2one_cv2.0/one2one_cv2.0.2/Conv_output_0 /model.23/one2one_cv2.1/one2one_cv2.1.2/Conv_output_0 /model.23/one2one_cv2.2/one2one_cv2.2.2/Conv_output_0 /model.23/one2one_cv3.0/one2one_cv3.0.2/Conv_output_0 /model.23/one2one_cv3.1/one2one_cv3.1.2/Conv_output_0 /model.23/one2one_cv3.2/one2one_cv3.2.2/Conv_output_0`|同MaixCAM2|
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|YOLOv8/YOLO11 检测输出节点图| ![](../../assets/yolo11_detect_nodes.png) | ![](../../assets/yolov8_out.jpg)|
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|YOLOv8/YOLO11 pose 额外输出节点 | ![](../../assets/yolo11_pose_node.png) | 见上图 pose 分支|
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|YOLOv8/YOLO11 seg 额外输出节点 | ![](../../assets/yolo11_seg_node.png) | ![](../../assets/yolo11_seg_node.png)|
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|YOLOv8/YOLO11 OBB 额外输出节点 | ![](../../assets/yolo11_obb_node.png) | ![](../../assets/yolo11_out_obb.jpg)|
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|检测 YOLO26 输出节点图 | ![](../../assets/yolo26_out.png) | 同MaixCAM2 |
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| 模型 | 节点名 | 节点图 |
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| -- | --- | --- |
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| 检测 YOLO26 | `/model.23/one2one_cv2.0/one2one_cv2.0.2/Conv_output_0 /model.23/one2one_cv2.1/one2one_cv2.1.2/Conv_output_0 /model.23/one2one_cv2.2/one2one_cv2.2.2/Conv_output_0 /model.23/one2one_cv3.0/one2one_cv3.0.2/Conv_output_0 /model.23/one2one_cv3.1/one2one_cv3.1.2/Conv_output_0 /model.23/one2one_cv3.2/one2one_cv3.2.2/Conv_output_0` |![](../../assets/yolo26_out.png) |
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### 转换脚本
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到 [MaixHub 模型库](https://maixhub.com/model/zoo?platform=maixcam) 上传并分享你的模型，可以多提供几个分辨率供大家选择。
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‎docs/doc/zh/vision/yolov5.md‎

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title: MaixPy 使用 YOLOv5 / YOLOv8 / YOLO11 / YOLO26 模型进行目标检测
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title: MaixPy 使用 YOLO 模型进行目标检测
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## MaixPy 中使用目标检测
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MaixPy 默认提供了 `YOLOv5` 和 `YOLOv8` 和 `YOLO11` 模型，可以直接使用：
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MaixPy 默认提供了 `YOLOv5` , `YOLOv8` , `YOLO11` , `YOLO26` 模型，可以直接使用：
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> YOLOv8 需要 MaixPy >= 4.3.0。
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> YOLO11 需要 MaixPy >= 4.7.0。
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分辨率越大精度越高，但是运行耗时越长，根据你的应用场景选择合适的即可。
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## YOLOv5 和 YOLOv8 和 YOLO11 用哪个？
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## YOLOv5 , YOLOv8 , YOLO11 , YOLO26 用哪个？
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这里提供的 `YOLOv5s` 和 `YOLOv8n` 和 `YOLO11n` 和 `YOLO26n` 三种模型，`YOLOv5s`模型更大，`YOLOv8n YOLO11n YOLO26n`速度快一点点， 精度按照官方数据来说`YOLO26n > YOLO11n > YOLOv8n > YOLOv5s`，可以实际测试根据自己的实际情况选择。
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