Answers.Body: <p>If the data is not labeled, you will have no way of knowing if your model was wrong or correct. You seem to suggest evaluating by eye, but that will not scale to datasets with thousands or millions of samples.</p>
  
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Answers.Text: I understand that. I;m saying for datasets of thousands of objects. In this case labelling is not a critical requirement, isnt ? I mean after training, there is some prediction performance and for test mode the idea is to check the false positive/negative.Tell me if I see it badly
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Answers.Text: I dont understand what you mean . How can you check false positive negative without labels???
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Answers.CreationDate: 2023-12-06T23:08:39.907Z
Answers.Text: Well, the model will classify bad or good,doesnt matter. And by eye you can take random output images to check it . No ? :D
Answers.UserDisplayName: Alexandru Lebada
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Answers.Text: No. You cannot check by eye. To properly check a real model (not a toy), your test set needs usually thousands of images. If you simply take a  few images and check by eye, you will have WAY too small a sample size.
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Answers.Body: <p>You're grading a multiple choice test, so you need to know the correct answers in order to mark your model's answers (predictions) as correct or incorrect. If you lack the answer key---which you can have because of external labeling, label yourself, or generate using sophisticated methods of semi-supervised learning---you cannot mark the predictions as incorrect or incorrect.</p>
  <p>If you don't have the labels and have to generate them youself, you have to take the time of doing the labeling. This can be done, but your time as a software engineer or data scientist is probably better spent on tasks other than deciding if images are of cars or helicopters (or it might be that you lack the expertise to make the labels, such as when the labels come from medical images that physicians spend years learning to interpret).</p>
  <p>Since it is common to have data without labels, there is an area of <em>semi-supervised learning</em> that uses labeled data to generated labels for unlabeled data that are then treated as labeled data (at least loosely speaking).</p>
  
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Body: <p>I have a problem understanding why test data needs to be labelled to test a trained faster R-CNN model. Maybe it's basic, but I don't get why it needs to be labelled.</p>
  <p>When an image is not obvious, like the type of disease, let's say, a label is useful to know if it's good or not because you're not a doctor. But for the classification between airplanes and cars, the label should be optional in my mind, because even if the classifier is wrong you'll be able to recognize that and will make adjustments accordingly.</p>
  <p>Why does test data need to be labelled even in &quot;obvious&quot; cases?</p>
  
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Comments.Text: That kind of blows the whole point of "testing" though.  When you are testing something, you are checking to see if it's right or not, which means it needs to know if it got the right answer in order to tell you how many answers it got right.  You don't need labeled data to run the neural network in production, so if you just want to "test" it by running it a bunch of times and seeing what happens you can do that, but that's not what a dedicated test mode is for.
Comments.UserDisplayName: Jeremiah
Tags: <convolutional-neural-networks><test-datasets>
Title: Why does test data need to be labelled?
UserDisplayName: Alexandru Lebada
ThreadText: # Why does test data need to be labelled?
  
  (Asked by: username_0 on 2023-12-06)
  
  I have a problem understanding why test data needs to be labelled to test a trained faster R-CNN model. Maybe it's basic, but I don't get why it needs to be labelled.
  
  When an image is not obvious, like the type of disease, let's say, a label is useful to know if it's good or not because you're not a doctor. But for the classification between airplanes and cars, the label should be optional in my mind, because even if the classifier is wrong you'll be able to recognize that and will make adjustments accordingly.
  
  Why does test data need to be labelled even in "obvious" cases?
  
  **Tags:** convolutional-neural-networks, test-datasets
  > **Comments:**
  > **username_1**: That kind of blows the whole point of "testing" though.  When you are testing something, you are checking to see if it's right or not, which means it needs to know if it got the right answer in order to tell you how many answers it got right.  You don't need labeled data to run the neural network in production, so if you just want to "test" it by running it a bunch of times and seeing what happens you can do that, but that's not what a dedicated test mode is for.  
  
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  ## 2 answers
  
  ### Answer by username_2 on 2023-12-06. Score: 2 | Accepted answer
  
  If the data is not labeled, you will have no way of knowing if your model was wrong or correct. You seem to suggest evaluating by eye, but that will not scale to datasets with thousands or millions of samples.
  
  > **Comments:**
  > **username_0**: I understand that. I;m saying for datasets of thousands of objects. In this case labelling is not a critical requirement, isnt ? I mean after training, there is some prediction performance and for test mode the idea is to check the false positive/negative.Tell me if I see it badly  
  > **username_2**: I dont understand what you mean . How can you check false positive negative without labels???  
  > **username_0**: Well, the model will classify bad or good,doesnt matter. And by eye you can take random output images to check it . No ? :D  
  > **username_2**: No. You cannot check by eye. To properly check a real model (not a toy), your test set needs usually thousands of images. If you simply take a  few images and check by eye, you will have WAY too small a sample size.  
  
  ---
  
  ### Answer by username_3 on 2023-12-06. Score: 1
  
  You're grading a multiple choice test, so you need to know the correct answers in order to mark your model's answers (predictions) as correct or incorrect. If you lack the answer key---which you can have because of external labeling, label yourself, or generate using sophisticated methods of semi-supervised learning---you cannot mark the predictions as incorrect or incorrect.
  
  If you don't have the labels and have to generate them youself, you have to take the time of doing the labeling. This can be done, but your time as a software engineer or data scientist is probably better spent on tasks other than deciding if images are of cars or helicopters (or it might be that you lack the expertise to make the labels, such as when the labels come from medical images that physicians spend years learning to interpret).
  
  Since it is common to have data without labels, there is an area of *semi-supervised learning* that uses labeled data to generated labels for unlabeled data that are then treated as labeled data (at least loosely speaking).
  
  
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