Answers.Body: <p>Marking these images as null is the correct thing to do. Object detection models will learn not to predict unlabeled areas (background area) through their loss function.</p>
  <p>It can <em>sometimes</em> be useful to add negative annotations if there’s a particularly subtle difference. For example “screw” and “bolt” — this will give the model more information to go off of, let the “objectness” part of the model so it’s thing, and the classification part do its thing also.</p>
  <p>By the way, be sure not to add too many null examples to your dataset or it might learn the optimal strategy to optimize its score is “predict nothing”. If this is happening you might want to use the “filter  null” preprocessing step in Roboflow to adjust the ratio.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-23T11:21:50.640Z
Answers.LastActivityDate: 2023-05-23T11:21:50.640Z
Answers.UserDisplayName: Brad Dwyer
Body: <p>I am currently using faster_rcnn to train a set of images with only one category, in fact, there are only good images and images with defect in the whole dataset,and I use roboflow to labeling this datasets,</p>
  <p>My problem is that when I encounter a good image, I just mark this image as null, but I don't know if it is effective to put it into the model for training.
  Should I frame the entire picture into a new category(two categories in total), or remaining mark those image as null?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-05-23T14:59:54.233Z
Comments.Text: Hi @Kekai and welcome to AI Stack Exchange! It would be helpful to add more details to this question to help identify the details of your problem. Thank you for posting, and we look forward to more of your questions on this site!
Comments.UserDisplayName: DeepQZero
Tags: <deep-learning><object-detection>
Title: Is it effective to use images without objects in object detection?
UserDisplayName: Kekai
ThreadText: # Is it effective to use images without objects in object detection?
  
  (Asked by: username_0 on 2023-05-22)
  
  I am currently using faster\_rcnn to train a set of images with only one category, in fact, there are only good images and images with defect in the whole dataset,and I use roboflow to labeling this datasets,
  
  My problem is that when I encounter a good image, I just mark this image as null, but I don't know if it is effective to put it into the model for training.
  Should I frame the entire picture into a new category(two categories in total), or remaining mark those image as null?
  
  **Tags:** deep-learning, object-detection
  
  > **Comments:**
  > **username_1**: Hi @username_0 and welcome to AI Stack Exchange! It would be helpful to add more details to this question to help identify the details of your problem. Thank you for posting, and we look forward to more of your questions on this site!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-05-23. Score: 0 | Accepted answer
  
  Marking these images as null is the correct thing to do. Object detection models will learn not to predict unlabeled areas (background area) through their loss function.
  
  It can *sometimes* be useful to add negative annotations if there’s a particularly subtle difference. For example “screw” and “bolt” — this will give the model more information to go off of, let the “objectness” part of the model so it’s thing, and the classification part do its thing also.
  
  By the way, be sure not to add too many null examples to your dataset or it might learn the optimal strategy to optimize its score is “predict nothing”. If this is happening you might want to use the “filter null” preprocessing step in Roboflow to adjust the ratio.
  
  
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