yolov8n网络图
时间: 2023-08-29 13:09:57 浏览: 111
很抱歉,根据提供的引用内容,我无法确定yolov8n的网络图是什么。引用提到了yolov8的代码,但并未提及网络图。引用则提到了yolox和yolov5,与yolov8n无关。引用则提到了ONNX,但没有具体涉及yolov8n的网络图。如果您有其他相关的信息,我会尽力帮助您回答问题。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [yolov8s网络模型结构图](https://blog.csdn.net/zhangdaoliang1/article/details/128577291)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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相关问题
yolov8n网络模型结构图
yolov8n网络模型结构如下所示:
1. 输入层:接收图像作为输入。
2. Darknet-53层:一个由53个卷积层和残差连接组成的主干网络,用于提取图像特征。
3. Neck层:由SPP(Spatial Pyramid Pooling)和PAN(Path Aggregation Network)模块组成,用于融合多尺度特征。
4. Detection层:包括3个不同大小的特征金字塔层,每个层都与3个不同大小的锚点框相关联,用于预测目标的边界框和类别。
5. 输出层:输出预测的目标边界框和类别。
总结起来,yolov8n网络模型结构由输入层、Darknet-53层、Neck层、Detection层和输出层组成,通过多层网络结构实现目标检测。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [YOLOV8-网络结构](https://blog.csdn.net/weixin_40723264/article/details/130745025)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [YOLOv8详解 【网络结构+代码+实操】](https://blog.csdn.net/zyw2002/article/details/128732494)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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yolov8n结构图
很抱歉,我无法提供yolov8n的结构图。我的引用内容中没有关于yolov8n结构图的信息。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [YOLOV8-网络结构](https://blog.csdn.net/weixin_40723264/article/details/130745025)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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