Answers.Body: <p>Your initial idea seems about right. Before creating your own classifier you might want to try transfer learning, using some pretrained network like VGG16 that is incorporated in most of machine learning frameworks.</p>
  
  <p>As to inference on mobile devices, TensorFlow offers some tutorials in this subject:
  <a href="https://www.tensorflow.org/mobile/tflite/" rel="nofollow noreferrer">https://www.tensorflow.org/mobile/tflite/</a> </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-07-17T13:43:55.307Z
Answers.LastActivityDate: 2018-07-17T13:43:55.307Z
Answers.UserDisplayName: don_pablito
Body: <p>I have a large dataset of skin images, each one associated with a hydration value (percentage).</p>
  
  <p>Now I'm looking into predicting the hydration value from an image. My thinking: train a CNN on the dataset and evaluate the model with a mean square error regression.</p>
  
  <p>First, does this sound like a sensible way to try this?</p>
  
  <p>Second, I'd like to run the model on mobile. Can you recommend any examples with Caffe2 (or alternatively TensorFlow) or diagrams that might explain a similar task?</p>
  
Tags: <convolutional-neural-networks><prediction><regression><testing><mean-squared-error>
Title: Is it a good idea to train a CNN to detect the hydration value (percentage) in skin images and evaluate it with the MSE?
UserDisplayName: Patrick Samy
ThreadText: # Is it a good idea to train a CNN to detect the hydration value (percentage) in skin images and evaluate it with the MSE?
  
  (Asked by: username_0 on 2018-01-09)
  
  I have a large dataset of skin images, each one associated with a hydration value (percentage).
  
  Now I'm looking into predicting the hydration value from an image. My thinking: train a CNN on the dataset and evaluate the model with a mean square error regression.
  
  First, does this sound like a sensible way to try this?
  
  Second, I'd like to run the model on mobile. Can you recommend any examples with Caffe2 (or alternatively TensorFlow) or diagrams that might explain a similar task?
  
  **Tags:** convolutional-neural-networks, prediction, regression, testing, mean-squared-error
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-07-17. Score: 1
  
  Your initial idea seems about right. Before creating your own classifier you might want to try transfer learning, using some pretrained network like VGG16 that is incorporated in most of machine learning frameworks.
  
  As to inference on mobile devices, TensorFlow offers some tutorials in this subject:
  <https://www.tensorflow.org/mobile/tflite/>
  
  
  ---
  
  