Body: <p>Suppose that we have a labeled training set of <span class="math-container">$n$</span> closely cropped images of cars <span class="math-container">$(x_1, y_1) , \dots, (x_n, y_n)$</span>. We then train a CNN on this. Let's say we have <span class="math-container">$m$</span> test images. Then for each of the <span class="math-container">$m$</span> images, do we use the trained CNN on a cropped out portion of the box to detect whether there is a car or not? If the object is large, wouldn't having a large sliding window have better performance than a smaller sliding window?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-09T22:00:05.317Z
Comments.Text: Hi and welcome to this community! I think it would be useful if you can explain more in detail the relationship between the sliding window and the test dataset: why do you need a sliding window in the first place? Are you referring to a specific model that uses a sliding window? If yes, edit your question to add these details. What is the context of your question, model and problem?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-09-09T23:42:11.530Z
Comments.Text: @nbro: https://www.pyimagesearch.com/2015/03/23/sliding-windows-for-object-detection-with-python-and-opencv/   The idea is to use the "non-convolutional" version of YOLO.
Comments.UserDisplayName: ekjrnke
Tags: <convolutional-neural-networks>
Title: Sliding Window Detection
UserDisplayName: ekjrnke
ThreadText: # Sliding Window Detection
  
  (Asked by: username_0 on 2019-09-09)
  
  Suppose that we have a labeled training set of $n$ closely cropped images of cars $(x\_1, y\_1) , \dots, (x\_n, y\_n)$. We then train a CNN on this. Let's say we have $m$ test images. Then for each of the $m$ images, do we use the trained CNN on a cropped out portion of the box to detect whether there is a car or not? If the object is large, wouldn't having a large sliding window have better performance than a smaller sliding window?
  
  **Tags:** convolutional-neural-networks
  > **Comments:**
  > **username_1**: Hi and welcome to this community! I think it would be useful if you can explain more in detail the relationship between the sliding window and the test dataset: why do you need a sliding window in the first place? Are you referring to a specific model that uses a sliding window? If yes, edit your question to add these details. What is the context of your question, model and problem?  
  > **username_0**: @nbro: https://www.pyimagesearch.com/2015/03/23/sliding-windows-for-object-detection-with-python-and-opencv/   The idea is to use the "non-convolutional" version of YOLO.  
  
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  ## 0 answers
  
  