Answers.Body: <p>Training time depends on a lot of parameters. Some of them are:</p>
  
  <ol>
  <li>Size of each image (resolution)</li>
  <li>Color/Monochrome image (color image has 3 times data if you consider RGB image)</li>
  <li>Like you mentioned on the type of DNN.
  No. of layers of DNN.
  No. of neurons in each layer. </li>
  <li>Total no. of images in the dataset. (2.6 million here)</li>
  <li>GPU you are using (you didn't mention which GPU you are using. There are GPUs with wide range of capabilities, to predict the time, you need to know the exact specs of GPU). </li>
  <li>Training time also depends on RAM on your machine and also how fast host PC can transfer data to GPU for processing.</li>
  <li>Since you mentioned face recognition, i am assuming you are using CNN, but if you again use fully connected network, training time will change and will obviously increase manifold.</li>
  </ol>
  
  <p>Your task of classification and your database is mostly similar to ILSVRC that uses imagenet database.</p>
  
  <p><strong>Making some reasonable assumptions for the parameters you didn't mention, i feel your task is similar to ILSVRC and i am predicting the training time will be a few days.</strong></p>
  
  <p>Below are the links which mention time of training for ILSVRC.</p>
  
  <p><a href="https://mxnet-tqchen.readthedocs.io/en/latest/tutorials/imagenet_full.html" rel="nofollow noreferrer">https://mxnet-tqchen.readthedocs.io/en/latest/tutorials/imagenet_full.html</a></p>
  
  <p>Below are the details of imagenet database for your comparison</p>
  
  <p><a href="https://en.wikipedia.org/wiki/ImageNet" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/ImageNet</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-25T03:44:05.007Z
Answers.LastActivityDate: 2019-05-25T03:44:05.007Z
Answers.UserDisplayName: Kartik Podugu
Answers.Body: <p>YELP Dataset (200k images) used to take 5 hr for training to identify Five (5) classes on GPU - Nvidia 1080 Ti with 11 GB RAM. So I guess in your case it will take days. Again it will depend on the type of your GPU configuration and type of Architecture you will be using.  </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-27T14:41:54.490Z
Answers.LastActivityDate: 2019-05-27T14:41:54.490Z
Answers.UserDisplayName: Sanmati Jain
Body: <p>If I use a desktop PC with a GPU, how long it might take to train face recognition deep neural network on let's say dataset of 2.6 million images and 2600 identities? I guess it should depend on various properties (e.g., type of the DNN). But I am just looking for a rough estimation. Is it a matter of hours/days or years?</p>
  
  <p>Thanks!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-29T20:32:42.010Z
Comments.Text: Very much dependent on your processing power and the computational complexity per DuttaA's comment.
Comments.UserDisplayName: DukeZhou
Tags: <deep-learning><deep-neural-networks><hardware><facial-recognition>
Title: How long it takes to train face recognition deep neural network? (rough estimation)
UserDisplayName: John
ThreadText: # How long it takes to train face recognition deep neural network? (rough estimation)
  
  (Asked by: username_0 on 2019-05-24)
  
  If I use a desktop PC with a GPU, how long it might take to train face recognition deep neural network on let's say dataset of 2.6 million images and 2600 identities? I guess it should depend on various properties (e.g., type of the DNN). But I am just looking for a rough estimation. Is it a matter of hours/days or years?
  
  Thanks!
  
  **Tags:** deep-learning, deep-neural-networks, hardware, facial-recognition
  > **Comments:**
  > **username_1**: Very much dependent on your processing power and the computational complexity per DuttaA's comment.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2019-05-25. Score: 2 | Accepted answer
  
  Training time depends on a lot of parameters. Some of them are:
  
  1. Size of each image (resolution)
  2. Color/Monochrome image (color image has 3 times data if you consider RGB image)
  3. Like you mentioned on the type of DNN.
     No. of layers of DNN.
     No. of neurons in each layer.
  4. Total no. of images in the dataset. (2.6 million here)
  5. GPU you are using (you didn't mention which GPU you are using. There are GPUs with wide range of capabilities, to predict the time, you need to know the exact specs of GPU).
  6. Training time also depends on RAM on your machine and also how fast host PC can transfer data to GPU for processing.
  7. Since you mentioned face recognition, i am assuming you are using CNN, but if you again use fully connected network, training time will change and will obviously increase manifold.
  
  Your task of classification and your database is mostly similar to ILSVRC that uses imagenet database.
  
  **Making some reasonable assumptions for the parameters you didn't mention, i feel your task is similar to ILSVRC and i am predicting the training time will be a few days.**
  
  Below are the links which mention time of training for ILSVRC.
  
  <https://mxnet-tqchen.readthedocs.io/en/latest/tutorials/imagenet_full.html>
  
  Below are the details of imagenet database for your comparison
  
  <https://en.wikipedia.org/wiki/ImageNet>
  
  
  ---
  
  ### Answer by username_3 on 2019-05-27. Score: 1
  
  YELP Dataset (200k images) used to take 5 hr for training to identify Five (5) classes on GPU - Nvidia 1080 Ti with 11 GB RAM. So I guess in your case it will take days. Again it will depend on the type of your GPU configuration and type of Architecture you will be using.
  
  
  ---
  
  