Answers.Body: <p>The problem might not be caused by your loss function. Deep learning models tend to be computationally demanding. Mobile devices are not usually prepared for handling models with high throughput. That being said, you might try to:</p>
  
  <ol>
  <li><p>Prepare smaller model - less layers, less computation</p></li>
  <li><p>Mobile models optimization - Google provides some materials on optimizing Tensorflow models for mobile inference:</p>
  
  <p><a href="https://www.tensorflow.org/mobile/prepare_models" rel="nofollow noreferrer">https://www.tensorflow.org/mobile/prepare_models</a>
  <a href="https://www.tensorflow.org/mobile/optimizing" rel="nofollow noreferrer">https://www.tensorflow.org/mobile/optimizing</a></p></li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-02T19:00:47.480Z
Answers.LastActivityDate: 2018-08-02T19:00:47.480Z
Answers.UserDisplayName: don_pablito
Answers.Body: <p>For face ID, Apple is using Siamese Network. You may get a better idea here <a href="https://towardsdatascience.com/one-shot-learning-face-recognition-using-siamese-neural-network-a13dcf739e" rel="nofollow noreferrer">https://towardsdatascience.com/one-shot-learning-face-recognition-using-siamese-neural-network-a13dcf739e</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-03T04:49:13.943Z
Answers.LastActivityDate: 2018-08-03T04:49:13.943Z
Answers.UserDisplayName: Patel Sunil
Body: <p>I want to implement a real-time system for image comparison (e.g. compare a face with a reference one) on an Odroid. I would like to know what are the most suitable architectures for this task. I started with methods based on triplet loss (like Facenet) but I realized that a real-time solution is not feasible. Are there good, light alternatives?</p>
  
Tags: <image-recognition><comparison>
Title: Methods for fast image comparison
UserDisplayName: firion
ThreadText: # Methods for fast image comparison
  
  (Asked by: username_0 on 2018-07-30)
  
  I want to implement a real-time system for image comparison (e.g. compare a face with a reference one) on an Odroid. I would like to know what are the most suitable architectures for this task. I started with methods based on triplet loss (like Facenet) but I realized that a real-time solution is not feasible. Are there good, light alternatives?
  
  **Tags:** image-recognition, comparison
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2018-08-02. Score: 1
  
  The problem might not be caused by your loss function. Deep learning models tend to be computationally demanding. Mobile devices are not usually prepared for handling models with high throughput. That being said, you might try to:
  
  1. Prepare smaller model - less layers, less computation
  2. Mobile models optimization - Google provides some materials on optimizing Tensorflow models for mobile inference:
  
     <https://www.tensorflow.org/mobile/prepare_models>
     <https://www.tensorflow.org/mobile/optimizing>
  
  
  ---
  
  ### Answer by username_2 on 2018-08-03. Score: 0
  
  For face ID, Apple is using Siamese Network. You may get a better idea here <https://towardsdatascience.com/one-shot-learning-face-recognition-using-siamese-neural-network-a13dcf739e>
  
  
  ---
  
  