Answers.Body: <p>The feature extraction part is described in section 2.4 of the <a href="https://arxiv.org/pdf/1804.02767.pdf" rel="nofollow noreferrer">original paper</a>. The proposed darknet-53 is an improvement over the previous darknet-19 which didn't include residual layers.</p>
  <img src="https://i.sstatic.net/qQjtP.png" width="300" height="400">
  <p>After the darknet backbone, three detection blocks are responsible for predicting bounding boxes and probability scores for each object class. notice that YOLO is an end-to-end architecture, so bounding boxes and objects are predicted at the same time.</p>
  <img src="https://i.sstatic.net/vJZ68.png" width="600" height="300">
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-10T16:17:05.120Z
Answers.Text: Thank you for your reply. It has cleared all my doubts about this topic.
Answers.UserDisplayName: CodeExplorer23
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-09T12:26:16.053Z
Answers.LastActivityDate: 2022-08-09T12:26:16.053Z
Answers.UserDisplayName: Edoardo Guerriero
Body: <p>I am new to machine learning and am confused about whether its architecture has clearly defined boundaries which demarcate the feature extraction and classification part. Or is it that it classifies the features extracted at different scales throughtout the network ?</p>
  
Tags: <yolo><ai-basics>
Title: Can YOLOv3 architecture be clearly separated into feature extractor and classifier parts?
UserDisplayName: CodeExplorer23
ThreadText: # Can YOLOv3 architecture be clearly separated into feature extractor and classifier parts?
  
  (Asked by: username_0 on 2022-08-09)
  
  I am new to machine learning and am confused about whether its architecture has clearly defined boundaries which demarcate the feature extraction and classification part. Or is it that it classifies the features extracted at different scales throughtout the network ?
  
  **Tags:** yolo, ai-basics
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-08-09. Score: 0 | Accepted answer
  
  The feature extraction part is described in section 2.4 of the [original paper](https://arxiv.org/pdf/1804.02767.pdf). The proposed darknet-53 is an improvement over the previous darknet-19 which didn't include residual layers.
  
  ![](https://i.sstatic.net/qQjtP.png)
  
  After the darknet backbone, three detection blocks are responsible for predicting bounding boxes and probability scores for each object class. notice that YOLO is an end-to-end architecture, so bounding boxes and objects are predicted at the same time.
  
  ![](https://i.sstatic.net/vJZ68.png)
  
  > **Comments:**
  > **username_0**: Thank you for your reply. It has cleared all my doubts about this topic.  
  
  ---
  
  