Answers.Body: <p>The hardest part is image annotation: here the difference between object recognition and object detection becomes important. If you just want to answer the question "does this image contain object X?", then you just need to provide as many images that contain object X as possible, together with as many images  that don't contain object X (but are otherwise similar). However if you want to answer the question "Where exactly object X is located in this image?" then you will need to manually provide a bounding box for each instance of object X in each image. Obviously, the second scenario is a lot more labor intensive. </p>
  
  <p>After you've done this part, train either a binary image classifier (typically this will be a convolutional neural network) on your annotated images (split them into train and test partitions), or an object detector (googling "custom object detection" produces lots of code examples how to train it (e.g. <a href="https://towardsdatascience.com/tutorial-build-an-object-detection-system-using-yolo-9a930513643a" rel="nofollow noreferrer">https://towardsdatascience.com/tutorial-build-an-object-detection-system-using-yolo-9a930513643a</a> start with Step 2B).</p>
  
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Answers.CreationDate: 2019-06-14T22:37:50.440Z
Answers.Text: That I know.  "A lot of code examples" is a problem, the question is which one to select.
Answers.UserDisplayName: user31264
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-06-14T22:39:23.027Z
Answers.Text: Are you not happy with the one I suggested for some reason?
Answers.UserDisplayName: MichaelSB
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Answers.CreationDate: 2019-06-14T21:19:17.440Z
Answers.LastActivityDate: 2019-06-14T21:24:48.067Z
Answers.LastEditDate: 2019-06-14T21:24:48.067Z
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Body: <p>How do I do object detection (or identify the location of an object) if there is only one kind of object, and they are more of less similar size, but the picture does not look like standard scenes (it is detection of drops on a substrate in microscopic images)?  Which software is good for it?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-14T19:16:47.403Z
Comments.Text: Object detection and recognition are not exactly the same thing. See: https://dsp.stackexchange.com/q/12940/40095. So, please, edit your question to use the most appropriate expression in all cases.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-14T19:17:03.613Z
Comments.Text: what ouput do you want to have, meybe you do have some division in sub- categories?
Comments.UserDisplayName: user8426627
Tags: <deep-learning><object-detection>
Title: How do I perform object detection if there is only one type of object?
UserDisplayName: user31264
ThreadText: # How do I perform object detection if there is only one type of object?
  
  (Asked by: username_0 on 2019-06-14)
  
  How do I do object detection (or identify the location of an object) if there is only one kind of object, and they are more of less similar size, but the picture does not look like standard scenes (it is detection of drops on a substrate in microscopic images)? Which software is good for it?
  
  **Tags:** deep-learning, object-detection
  
  > **Comments:**
  > **username_1**: Object detection and recognition are not exactly the same thing. See: https://dsp.stackexchange.com/q/12940/40095. So, please, edit your question to use the most appropriate expression in all cases.  
  > **username_2**: what ouput do you want to have, meybe you do have some division in sub- categories?  
  
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  ## 1 answers
  
  ### Answer by username_3 on 2019-06-14. Score: 1
  
  The hardest part is image annotation: here the difference between object recognition and object detection becomes important. If you just want to answer the question "does this image contain object X?", then you just need to provide as many images that contain object X as possible, together with as many images that don't contain object X (but are otherwise similar). However if you want to answer the question "Where exactly object X is located in this image?" then you will need to manually provide a bounding box for each instance of object X in each image. Obviously, the second scenario is a lot more labor intensive.
  
  After you've done this part, train either a binary image classifier (typically this will be a convolutional neural network) on your annotated images (split them into train and test partitions), or an object detector (googling "custom object detection" produces lots of code examples how to train it (e.g. <https://towardsdatascience.com/tutorial-build-an-object-detection-system-using-yolo-9a930513643a> start with Step 2B).
  
  > **Comments:**
  > **username_0**: That I know.  "A lot of code examples" is a problem, the question is which one to select.  
  > **username_3**: Are you not happy with the one I suggested for some reason?  
  
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