yolov8改进swim transformer V2网络结构
时间: 2023-09-10 17:06:34 浏览: 92
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#### 引用[.reference_title]
- *1* *2* *3* [改进YOLOv7系列:28.YOLOv7 结合 Swin Transformer V2结构,Swin Transformer V2:通向视觉大模型之路](https://blog.csdn.net/qq_38668236/article/details/126735304)[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
yolov7与swim transformer结合
YOLOv7与Swin Transformer V2的结合是一种新的目标检测算法模型。Swin Transformer V2是基于Transformer的新型模型结构,通过突破传统CNN的性能瓶颈,具有更好的扩展性和可解释性。我们通过在YOLOv7的backbone网络中集成Swin Transformer V2模块,形成了这个新的检测算法模型。经过在多个数据集上的测试,这个模型不仅保持了YOLOv7的高速和高精度特点,还进一步提升了性能。具体的改进细节可以参考相关的博文和论文。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* *3* [改进YOLOv7系列:YOLOv7与Swin Transformer V2的结合](https://blog.csdn.net/wellcoder/article/details/130935432)[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^v92^chatsearchT0_1"}} ] [.reference_item]
- *2* [改进YOLOv5系列:27.YOLOv5 结合 Swin Transformer V2结构,Swin Transformer V2:通向视觉大模型之路](https://blog.csdn.net/qq_38668236/article/details/126735107)[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^v92^chatsearchT0_1"}} ] [.reference_item]
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yolov7与swim transformer相结合
将YOLOv7与Swin Transformer V2相结合可以进一步提升目标检测算法的性能。Swin Transformer V2是一种基于Transformer的新型模型结构,具有更好的扩展性和可解释性,并突破了传统CNN的性能瓶颈。通过在YOLOv7的backbone网络中集成Swin Transformer V2模块,形成了新的检测算法模型。这个新模型在多个数据集上进行测试,既保持了YOLOv7的高速和高精度特点,又进一步提升了性能。最近有一篇题为《Improving YOLOv5 Series by Combining Swin Transformer Structure》的论文获得了最佳论文奖项,该论文提出了一种新颖的算法YOLOv5 Swin Transformer,其在目标检测任务上表现出色。因此,将YOLOv7与Swin Transformer相结合可以提供更优秀的目标检测结果。<em>1</em><em>2</em><em>3</em>
#### 引用[.reference_title]
- *1* *2* [改进YOLOv7系列:YOLOv7与Swin Transformer V2的结合](https://blog.csdn.net/wellcoder/article/details/130935432)[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^v92^chatsearchT0_1"}} ] [.reference_item]
- *3* [YOLOv5与Swin Transformer相结合的分层视觉转换器——计算机视觉进阶](https://blog.csdn.net/wellcoder/article/details/131028235)[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^v92^chatsearchT0_1"}} ] [.reference_item]
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