How to quickly setup Google's Tensorflow image recognition - Source Dexter

8 Comments

  1. Ben
    May 30, 2017 @ 5:16 pm

    Hello! Thank you for the excellent post! Did you create the image at the top of the article? If so, how do you make such beautiful animated gifs?

    Reply

    • admin
      May 30, 2017 @ 7:54 pm

      Hi Ben, I haven’t created this GIF and it was created by Google to explain the concept of neural networks.

      Reply

  2. Seb
    July 28, 2017 @ 6:28 pm

    Tried it, but always get the same result, no matter which image I feed to it.

    Reply

    • akshay pai
      July 28, 2017 @ 11:05 pm

      hi Seb,
      I need more details on what image you are using, which OS you are trying this on and what is the output that you are getting in order to help you.

      Reply

  3. Dory
    September 1, 2017 @ 7:29 pm

    Hey – I have read your Transfer Learning post as well. Have a few questions for you:
    a. What is the average pixel size you used for re-training?
    b. What is the time complexity you noticed both in straight Classification and for re-training?

    Reply

    • akshay pai
      September 1, 2017 @ 8:01 pm

      Hi.
      When you say pixel size, I’m assuming you mean what is the image size I used while re-training. I used 500×500 as a standard size while re-training. I also tried re-training without any standard size, that is all images were of different dimensions and in that case, the accuracy was almost the same.

      And regarding the time. The retraining is much much faster when you compare it with training from scratch. Straight Training probably takes more than double the time or even more.

      What I can suggest you is to try out re-training and see if you get the accuracy you desire. Only if it does not satisfy your criteria, then, go for training from scratch. From my experience , I was able to get around 94% accuracy for a pretty big dataset by retraining.

      Reply

      • Dory
        September 2, 2017 @ 7:43 pm

        Thanks for Replying.
        I got your answer for the first question.

        And as part of the second q, I am looking for the time complexity you experienced especially while re-training?

        Reply

        • admin
          September 3, 2017 @ 10:57 am

          With respect to time, it is heavily (stressing again, “heavily”) dependent on the hardware you are running. For example, a 5 class classifier with 1000 images each takes 4 hours to run on a Nvidia 940MX but will complete in less than 30 minutes when running on a Nvidia 1080TI. The time complexity is sometimes linear and sometimes exponential. Many factors here are dependent on data, hardware and type of neural network used. So I cannot give a generic answer to your question.

          Reply

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