Answers.Body: <p>Yes, you are correct The base network block is also a feature map block.</p>
  <p>The base network block extracts features from the input image. It is using CNN modules with filter num of <strong>[3, 16, 32, 64]</strong>.  It Outputs the feature maps that are used for generation of anchor boxes and predicting clases as well as the offset of those anchor boxes.</p>
  <pre><code>def base_net():
      blk = []
      num_filters = [3, 16, 32, 64]
      for i in range(len(num_filters) - 1):
          blk.append(down_sample_blk(num_filters[i], num_filters[i+1]))
      return nn.Sequential(*blk)
  
  forward(torch.zeros((2, 3, 256, 256)), base_net()).shape
  </code></pre>
  <p><a href="https://d2l.ai/chapter_computer-vision/ssd.html#base-network-block" rel="nofollow noreferrer">reference</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-20T15:04:12.757Z
Answers.Text: thank you. so, the original phrasing seems to be somewhat misleading. A clearer way might be 'All 5 blocks are multiscale feature map blocks.', right?
Answers.UserDisplayName: singularli
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-20T15:16:55.967Z
Answers.Text: Yes exactly all blocks are feature maps...
Answers.UserDisplayName: Hiren Namera
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-12-19T05:34:02.347Z
Answers.LastActivityDate: 2023-12-19T05:34:02.347Z
Answers.UserDisplayName: Hiren Namera
Body: <p>I'm trying to understand Single Shot Multibox Detection following a <a href="http://d2l.ai/chapter_computer-vision/ssd.html#the-complete-model" rel="nofollow noreferrer">book</a> adopted at 500 universities from 70 countries</p>
  <blockquote>
  <p>The complete single shot multibox detection model consists of five blocks. The feature maps produced by each block are used for both (i) generating anchor boxes and (ii) predicting classes and offsets of these anchor boxes. Among these five blocks, the first one is the base network block, the second to the fourth are downsampling blocks, and the last block uses global max-pooling to reduce both the height and width to 1. Technically, the second to the fifth blocks are all those multiscale feature map blocks in Fig. 14.7.1.</p>
  </blockquote>
  <p>the following is Fig. 14.7.1. mentioned in the above excerpt</p>
  <p><a href="https://i.sstatic.net/gUUdV.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/gUUdV.png" alt="enter image description here" /></a></p>
  <p>I'm aware that the second to the fifth blocks are all those multiscale feature map blocks. My only concern is whether the base network is also a feature map block. I guess it is though the book doesn't convey the idea explicitly. So I guess I need a double confirm.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-19T20:20:31.930Z
Comments.Text: Hi @singularli, and welcome to AI Stack Exchange! If possible, please outline your question a little more explicitly. That might help in getting more answers.
Comments.UserDisplayName: DeepQZero
Tags: <computer-vision>
Title: does it make sense to consider the base/backbone network one of the multiscale feature map blocks in SSD?
UserDisplayName: singularli
ThreadText: # does it make sense to consider the base/backbone network one of the multiscale feature map blocks in SSD?
  
  (Asked by: username_0 on 2023-12-14)
  
  I'm trying to understand Single Shot Multibox Detection following a [book](http://d2l.ai/chapter_computer-vision/ssd.html#the-complete-model) adopted at 500 universities from 70 countries
  
  > The complete single shot multibox detection model consists of five blocks. The feature maps produced by each block are used for both (i) generating anchor boxes and (ii) predicting classes and offsets of these anchor boxes. Among these five blocks, the first one is the base network block, the second to the fourth are downsampling blocks, and the last block uses global max-pooling to reduce both the height and width to 1. Technically, the second to the fifth blocks are all those multiscale feature map blocks in Fig. 14.7.1.
  
  the following is Fig. 14.7.1. mentioned in the above excerpt
  
  [![enter image description here](https://i.sstatic.net/gUUdV.png)](https://i.sstatic.net/gUUdV.png)
  
  I'm aware that the second to the fifth blocks are all those multiscale feature map blocks. My only concern is whether the base network is also a feature map block. I guess it is though the book doesn't convey the idea explicitly. So I guess I need a double confirm.
  
  **Tags:** computer-vision
  
  > **Comments:**
  > **username_1**: Hi @username_0, and welcome to AI Stack Exchange! If possible, please outline your question a little more explicitly. That might help in getting more answers.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-12-19. Score: 2 | Accepted answer
  
  Yes, you are correct The base network block is also a feature map block.
  
  The base network block extracts features from the input image. It is using CNN modules with filter num of **[3, 16, 32, 64]**. It Outputs the feature maps that are used for generation of anchor boxes and predicting clases as well as the offset of those anchor boxes.
  
  ```
  def base_net():
      blk = []
      num_filters = [3, 16, 32, 64]
      for i in range(len(num_filters) - 1):
          blk.append(down_sample_blk(num_filters[i], num_filters[i+1]))
      return nn.Sequential(*blk)
  
  forward(torch.zeros((2, 3, 256, 256)), base_net()).shape
  
  ```
  
  [reference](https://d2l.ai/chapter_computer-vision/ssd.html#base-network-block)
  
  > **Comments:**
  > **username_0**: thank you. so, the original phrasing seems to be somewhat misleading. A clearer way might be 'All 5 blocks are multiscale feature map blocks.', right?  
  > **username_2**: Yes exactly all blocks are feature maps...  
  
  ---
  
  