Answers.Body: <p>I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-23T12:16:34.817Z
Answers.LastActivityDate: 2019-08-23T12:16:34.817Z
Answers.UserDisplayName: razvanc92
Body: <p>I am wondering why tf object detection api needs so few picture samples for training while regular cnns needs many more?</p>
  
  <p>What I read in tutorials is that tf object detection api needs around 100-500 pictures per class for training (is it true?) while regular CNNs need many many more samples, like tens of thousands or more. Why is it so?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-22T14:09:36.107Z
Comments.Text: I haven't work with the tf object detection api so I might be totally wrong but I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning.
Comments.UserDisplayName: razvanc92
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-22T14:17:39.060Z
Comments.Text: You actually might be right :) haven't thought about it, thanks!
Comments.UserDisplayName: GKozinski
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-22T15:26:00.520Z
Comments.Text: you are right. Actually from scratch OD needs more images than Classifiers because its a more difficult task
Comments.UserDisplayName: mshlis
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-23T08:54:21.187Z
Comments.Text: @razvanc92 you can put your comment as an answer for this question :)
Comments.UserDisplayName: malioboro
Tags: <tensorflow><object-detection>
Title: Why tf object detection api needs so few pictures?
UserDisplayName: GKozinski
ThreadText: # Why tf object detection api needs so few pictures?
  
  (Asked by: username_0 on 2019-08-22)
  
  I am wondering why tf object detection api needs so few picture samples for training while regular cnns needs many more?
  
  What I read in tutorials is that tf object detection api needs around 100-500 pictures per class for training (is it true?) while regular CNNs need many many more samples, like tens of thousands or more. Why is it so?
  
  **Tags:** tensorflow, object-detection
  > **Comments:**
  > **username_1**: I haven't work with the tf object detection api so I might be totally wrong but I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning.  
  > **username_0**: You actually might be right :) haven't thought about it, thanks!  
  > **username_2**: you are right. Actually from scratch OD needs more images than Classifiers because its a more difficult task  
  > **username_3**: @razvanc92 you can put your comment as an answer for this question :)  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-08-23. Score: 2 | Accepted answer
  
  I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).
  
  
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