bottleneck transformer

瓶颈 Transformer 是一种在 Transformer 模型中引入瓶颈层以降低模型复杂度和参数量的方法。它通过在每层 Transformer 中引入一个瓶颈层来限制输入和输出维度，从而减少参数的数量。这样做可以在保持模型性能的同时减少计算复杂度。

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Bottleneck Transformer

Bottleneck Transformer（简称BotNet）是一种深度学习模型，通过在Multi-Head Self-Attention结构前后加上1x1卷积，从而扩展了传统的Transformer结构。BotNet和ViT中的Transformer block有一定的相似性，可以将具有MHSA的ResNet bottleneck块视为具有bottleneck结构的Transformer块，尽管它们在残差连接、标准化层等方面存在一些微小差异。这个模型在2021年由Google的研究人员在论文"Bottleneck Transformers for Visual Recognition"中提出，并引入了CNN与Attention的融合网络。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *3* [深度学习之图像分类（十九）-- Bottleneck Transformer(BoTNet)网络详解](https://blog.csdn.net/baidu_36913330/article/details/120218954)[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: 50%"]
- *2* [第53步 深度学习图像识别：Bottleneck Transformer建模（Pytorch）](https://blog.csdn.net/qq_30452897/article/details/131741943)[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: 50%"]
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Bottleneck transformer

Bottleneck Transformer是一种深度学习模型，它是在ViT（Vision Transformer）中引入的一种变体。它通过在Multi-Head Self-Attention结构前后加上1×1卷积层来构建。Bottleneck Transformer和ViT中的Transformer block具有亲属关系，它们的结构并不完全不同。作者在论文中指出，具有MHSA的ResNet bottleneck块可以被视为具有bottleneck结构的Transformer块，除了一些细微的差异，如残差连接和规范化层的选择等。Bottleneck Transformer模型在2021年由Google的研究人员在论文"Bottleneck Transformers for Visual Recognition"中提出。

如果你想使用Bottleneck Transformer模型，你可以通过pip命令安装bottleneck-transformer-pytorch库，并按照提供的用法进行引用和使用。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [深度学习之图像分类（十九）-- Bottleneck Transformer(BoTNet)网络详解](https://blog.csdn.net/baidu_36913330/article/details/120218954)[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: 33.333333333333336%"]
- *2* [第53步 深度学习图像识别：Bottleneck Transformer建模（Pytorch）](https://blog.csdn.net/qq_30452897/article/details/131741943)[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: 33.333333333333336%"]
- *3* [bottleneck-transformer-pytorch:Pytorch中瓶颈变压器的实现](https://download.csdn.net/download/weixin_42111465/15605077)[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: 33.333333333333336%"]
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