Answers.Body: <p>The min and max input size should be the min and max image size of the input images. The numbers represent pixels in both axis of the image. </p>
  
  <p>The anchors represents the size of the anchor box. Anchor box does not have coordinates, only have size. </p>
  
  <p>Hope I can help you</p>
  
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Answers.CreationDate: 2019-11-15T14:09:50.450Z
Answers.Text: So "min_input_size":       288 means that the minimum size of of images is 288 x 288?  Also "anchors" =  [55,69, 75,234] means anchors of size 55x69 and 75x234 (using first 4 numbers in "anchors" field)?
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Answers.Text: Yes. This should be the case.
Answers.UserDisplayName: Clement
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Answers.CreationDate: 2019-11-15T14:15:48.050Z
Answers.Text: The min input size are here as the model is a fully convolutional neural network so it can accept a range of image size. However to get a suitable output size only the image size in the range can do it.
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Answers.CreationDate: 2019-11-15T14:20:32.030Z
Answers.Text: I see. So in the example above, there are 9 anchor boxes of varying sizes generated for each grid cell? What is the parameter that controls the number of grid cells usually called? Actually it seems that this parameter is in the YOLO class itself.
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Answers.Text: First of all, anchor box is not generated, it is just used to choose which output bounding box is good quality. YOLO loss computes IOU of the anchor box to the output box. The number of grid cell called, do you mean output? I assume you mean the output grid. If you mean so, then it is not changeble. It is in the model's architecture. However since it is a CNN if you use a lower image size the grid size will be smaller. Default is 13*13 if I am not mistaken
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Answers.Text: That's what I meant. So in the above example, a output bounding box would be compared to 9 anchor boxes (e.g. the IOU of the output bounding box and each of the 9 anchor boxes is computed and the one with the highest is the "best" bounding box)?
Answers.UserDisplayName: NebulousReveal
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Answers.Text: It's something similar.
Answers.UserDisplayName: Clement
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Answers.UserDisplayName: Clement
Body: <p>How does one interpret the "min_input_size", "max_input_size" and "anchors" fields in the Yolov3 config file <a href="https://github.com/experiencor/keras-yolo3/blob/master/config.json" rel="nofollow noreferrer">here</a>. In particular, suppose we have the following:</p>
  
  <pre><code>    "min_input_size":       288,
  
      "max_input_size":       448,
  
      "anchors":              [55,69, 75,234, 133,240, 136,129, 142,363, 203,290, 228,184, 285,359, 341,260]
  </code></pre>
  
  <p>Does the min_input_size and max_input_size indicate the maximum number of training images we can have? What do the numbers in the "anchors" field indicate? Are they the coordinates of the anchor boxes? Surprisingly, I have not been able to find a good explanation of many of these  fields within this file.</p>
  
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Comments.CreationDate: 2019-11-15T15:33:45.457Z
Comments.Text: Hi and welcome to this community! This type of question is off-topic here because you are asking about the meaning of certain parameters in the source code. Have a look at [https://ai.stackexchange.com/help/on-topic](https://ai.stackexchange.com/help/on-topic) for more details. This type of question is more suited for [Data Science SE](https://datascience.stackexchange.com/questions/ask).
Comments.UserDisplayName: nbro
Tags: <convolutional-neural-networks><object-detection>
Title: Interpreting Keras Yolov3 config file
UserDisplayName: NebulousReveal
ThreadText: # Interpreting Keras Yolov3 config file
  
  (Asked by: username_0 on 2019-11-15)
  
  How does one interpret the "min_input_size", "max_input_size" and "anchors" fields in the Yolov3 config file here. In particular, suppose we have the following:
      "min_input_size":       288,
  
      "max_input_size":       448,
  
      "anchors":              [55,69, 75,234, 133,240, 136,129, 142,363, 203,290, 228,184, 285,359, 341,260]
  
  Does the min_input_size and max_input_size indicate the maximum number of training images we can have? What do the numbers in the "anchors" field indicate? Are they the coordinates of the anchor boxes? Surprisingly, I have not been able to find a good explanation of many of these  fields within this file.
  
  **Tags:** convolutional-neural-networks, object-detection
  > **Comments:**
  > **username_1**: Hi and welcome to this community! This type of question is off-topic here because you are asking about the meaning of certain parameters in the source code. Have a look at [https://ai.stackexchange.com/help/on-topic](https://ai.stackexchange.com/help/on-topic) for more details. This type of question is more suited for [Data Science SE](https://datascience.stackexchange.com/questions/ask).  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-11-15. Score: 0 | Accepted answer
  
  The min and max input size should be the min and max image size of the input images. The numbers represent pixels in both axis of the image.
  
  The anchors represents the size of the anchor box. Anchor box does not have coordinates, only have size.
  
  Hope I can help you
  
  > **Comments:**
  > **username_0**: So "min_input_size":       288 means that the minimum size of of images is 288 x 288?  Also "anchors" =  [55,69, 75,234] means anchors of size 55x69 and 75x234 (using first 4 numbers in "anchors" field)?  
  > **username_2**: Yes. This should be the case.  
  > **username_2**: The min input size are here as the model is a fully convolutional neural network so it can accept a range of image size. However to get a suitable output size only the image size in the range can do it.  
  > **username_0**: I see. So in the example above, there are 9 anchor boxes of varying sizes generated for each grid cell? What is the parameter that controls the number of grid cells usually called? Actually it seems that this parameter is in the YOLO class itself.  
  > **username_2**: First of all, anchor box is not generated, it is just used to choose which output bounding box is good quality. YOLO loss computes IOU of the anchor box to the output box. The number of grid cell called, do you mean output? I assume you mean the output grid. If you mean so, then it is not changeble. It is in the model's architecture. However since it is a CNN if you use a lower image size the grid size will be smaller. Default is 13*13 if I am not mistaken  
  > **username_0**: That's what I meant. So in the above example, a output bounding box would be compared to 9 anchor boxes (e.g. the IOU of the output bounding box and each of the 9 anchor boxes is computed and the one with the highest is the "best" bounding box)?  
  > **username_2**: It's something similar.  
  
  ---
  
  