Answers.Body: <p>The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task. </p>
  
  <p>I'm not aware about state-of-the models specific for images, but you can potentially use whatever architecture you desire to perform regression, CNN, RNN, the only thing to pay attention to is the loss function you will use. There are specific functions for classifications (which usually use the argmax function to turn probabilities into labels) and for regression (the most used is the Mean Squared Error, the model try to approximate the continuous values directly).</p>
  
  <p>For a quick overview of loss functions I suggest this easy tutorial <a href="https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0" rel="nofollow noreferrer">5 Regression Loss Functions</a>. Hope it might be of use.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-14T20:00:42.183Z
Answers.Text: Here is a tutorial that is a good starting point with the use of images to predict house prices (a continuous variable) https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/
Answers.UserDisplayName: Mark Neal
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-03-14T14:36:07.950Z
Answers.LastActivityDate: 2020-03-14T14:36:07.950Z
Answers.UserDisplayName: Edoardo Guerriero
Body: <p>I'm new to the topic, but I've used some off the shelf knowledge about computer vision for classifying images.</p>
  
  <p>For example, you can easily generate labels that can determine whether or not e.g. a cloud is in the image. However, what is the general type of problem called where you want to assign a value, or rate the image on a scale - in this example, the degree of cloudiness in the image? </p>
  
  <p>What are useful algorithms or techniques for addressing this type of problem?</p>
  
Tags: <machine-learning><classification><computer-vision><terminology><regression>
Title: What is the type of problem requiring to rate images on a scale?
UserDisplayName: Mark Neal
ThreadText: # What is the type of problem requiring to rate images on a scale?
  
  (Asked by: username_0 on 2020-03-14)
  
  I'm new to the topic, but I've used some off the shelf knowledge about computer vision for classifying images.
  
  For example, you can easily generate labels that can determine whether or not e.g. a cloud is in the image. However, what is the general type of problem called where you want to assign a value, or rate the image on a scale - in this example, the degree of cloudiness in the image?
  
  What are useful algorithms or techniques for addressing this type of problem?
  
  **Tags:** machine-learning, classification, computer-vision, terminology, regression
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-03-14. Score: 3 | Accepted answer
  
  The main distinction between tasks is 'classification' vs 'regression'. In classification you would try to identify the presence of a cloud or not in an image, if you want to predict the level of 'cloudness' with continuous values you are then performing a regression task.
  
  I'm not aware about state-of-the models specific for images, but you can potentially use whatever architecture you desire to perform regression, CNN, RNN, the only thing to pay attention to is the loss function you will use. There are specific functions for classifications (which usually use the argmax function to turn probabilities into labels) and for regression (the most used is the Mean Squared Error, the model try to approximate the continuous values directly).
  
  For a quick overview of loss functions I suggest this easy tutorial [5 Regression Loss Functions](https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0). Hope it might be of use.
  
  > **Comments:**
  > **username_0**: Here is a tutorial that is a good starting point with the use of images to predict house prices (a continuous variable) https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/  
  
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