bubbliiiing yolov7改进

Bubbliiiing YoloV7是对YoloV7目标检测模型的改进。具体的改进部分可以在引用中的GitHub链接中找到。其中列举的改进部分并非全部，还有其他一些我比较感兴趣和有效的改进没有列出来。整体来看，Bubbliiiing YoloV7的实现思路可以在引用中找到。该模型基于Bubbliiiing代码仓库中的yolov7进行改进，原仓库的介绍可以在引用中找到，其中详细描述了YoloV7支持的功能，例如支持step、cos学习率下降法、支持adam、sgd优化器选择等。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [睿智的目标检测62——Keras搭建YoloV7目标检测平台](https://blog.csdn.net/weixin_44791964/article/details/127503879)[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%"]
- *3* [基于Bubbliiiing代码仓库yolov7的改进，](https://download.csdn.net/download/qq_41701956/87975015)[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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