What methods can be used to detect duplicacy in image dataset?












1












$begingroup$


I want to remove duplicate images from a dataset of 50Million images. What is the best method to detect all the duplicates?



Do you think one shot learning is good for this?






deep-learning predictive-modeling data-cleaning image-classification ensemble-modeling







share|improve this question











$endgroup$












  • $begingroup$
    Exact duplicates?
    $endgroup$
    – Michael M
    Sep 28 '18 at 19:16










  • $begingroup$
    No, even augmented ones.
    $endgroup$
    – thanatoz
    Sep 28 '18 at 19:17
















1












$begingroup$


I want to remove duplicate images from a dataset of 50Million images. What is the best method to detect all the duplicates?



Do you think one shot learning is good for this?






deep-learning predictive-modeling data-cleaning image-classification ensemble-modeling







share|improve this question











$endgroup$












  • $begingroup$
    Exact duplicates?
    $endgroup$
    – Michael M
    Sep 28 '18 at 19:16










  • $begingroup$
    No, even augmented ones.
    $endgroup$
    – thanatoz
    Sep 28 '18 at 19:17














1












1








1





$begingroup$


I want to remove duplicate images from a dataset of 50Million images. What is the best method to detect all the duplicates?



Do you think one shot learning is good for this?






deep-learning predictive-modeling data-cleaning image-classification ensemble-modeling







share|improve this question











$endgroup$




I want to remove duplicate images from a dataset of 50Million images. What is the best method to detect all the duplicates?



Do you think one shot learning is good for this?






deep-learning predictive-modeling data-cleaning image-classification ensemble-modeling




deep-learning predictive-modeling data-cleaning image-classification ensemble-modeling






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Sep 30 '18 at 14:07







thanatoz

















asked Sep 28 '18 at 19:09









thanatozthanatoz

504317




504317












  • $begingroup$
    Exact duplicates?
    $endgroup$
    – Michael M
    Sep 28 '18 at 19:16










  • $begingroup$
    No, even augmented ones.
    $endgroup$
    – thanatoz
    Sep 28 '18 at 19:17


















  • $begingroup$
    Exact duplicates?
    $endgroup$
    – Michael M
    Sep 28 '18 at 19:16










  • $begingroup$
    No, even augmented ones.
    $endgroup$
    – thanatoz
    Sep 28 '18 at 19:17
















$begingroup$
Exact duplicates?
$endgroup$
– Michael M
Sep 28 '18 at 19:16




$begingroup$
Exact duplicates?
$endgroup$
– Michael M
Sep 28 '18 at 19:16












$begingroup$
No, even augmented ones.
$endgroup$
– thanatoz
Sep 28 '18 at 19:17




$begingroup$
No, even augmented ones.
$endgroup$
– thanatoz
Sep 28 '18 at 19:17










2 Answers
2






active

oldest

votes


















1












$begingroup$

I think the dhash technique might help. It essentially creates a signature for each image, then you could isolate the duplicated images. 50M could take a while, so perhaps you can try that with a smaller subset and see how well it works.






share|improve this answer









$endgroup$













  • $begingroup$
    Is there a descriptive guide to use this apart from the official jetsetter page?
    $endgroup$
    – thanatoz
    Oct 1 '18 at 7:22












  • $begingroup$
    Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
    $endgroup$
    – The Lyrist
    Oct 1 '18 at 18:29



















0












$begingroup$

So, this is a simple problem that could be solved using one-shot learning technique. To achieve this, we must build a model that understands our data and is capable of finding similarity or dissimilarity in your data.



For this, we must carry out the following steps:




  1. Train (or finetune) the network on dataset of related images.

  2. After training the model, clip the last predicting layers to create embedding.

  3. Pass your testing data through the network and store individual embedding.

  4. Find the difference between the embedding and find the differences crossing a certain threshold.

  5. These images are potentially images having similar data and this could be easily used to find duplicacy in the dataset.


1shot



I referred this paper on oneshot learning and later found this blog to be a little helpful.






share|improve this answer









$endgroup$














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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1












    $begingroup$

    I think the dhash technique might help. It essentially creates a signature for each image, then you could isolate the duplicated images. 50M could take a while, so perhaps you can try that with a smaller subset and see how well it works.






    share|improve this answer









    $endgroup$













    • $begingroup$
      Is there a descriptive guide to use this apart from the official jetsetter page?
      $endgroup$
      – thanatoz
      Oct 1 '18 at 7:22












    • $begingroup$
      Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
      $endgroup$
      – The Lyrist
      Oct 1 '18 at 18:29
















    1












    $begingroup$

    I think the dhash technique might help. It essentially creates a signature for each image, then you could isolate the duplicated images. 50M could take a while, so perhaps you can try that with a smaller subset and see how well it works.






    share|improve this answer









    $endgroup$













    • $begingroup$
      Is there a descriptive guide to use this apart from the official jetsetter page?
      $endgroup$
      – thanatoz
      Oct 1 '18 at 7:22












    • $begingroup$
      Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
      $endgroup$
      – The Lyrist
      Oct 1 '18 at 18:29














    1












    1








    1





    $begingroup$

    I think the dhash technique might help. It essentially creates a signature for each image, then you could isolate the duplicated images. 50M could take a while, so perhaps you can try that with a smaller subset and see how well it works.






    share|improve this answer









    $endgroup$



    I think the dhash technique might help. It essentially creates a signature for each image, then you could isolate the duplicated images. 50M could take a while, so perhaps you can try that with a smaller subset and see how well it works.







    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered Sep 28 '18 at 21:32









    The LyristThe Lyrist

    419113




    419113












    • $begingroup$
      Is there a descriptive guide to use this apart from the official jetsetter page?
      $endgroup$
      – thanatoz
      Oct 1 '18 at 7:22












    • $begingroup$
      Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
      $endgroup$
      – The Lyrist
      Oct 1 '18 at 18:29


















    • $begingroup$
      Is there a descriptive guide to use this apart from the official jetsetter page?
      $endgroup$
      – thanatoz
      Oct 1 '18 at 7:22












    • $begingroup$
      Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
      $endgroup$
      – The Lyrist
      Oct 1 '18 at 18:29
















    $begingroup$
    Is there a descriptive guide to use this apart from the official jetsetter page?
    $endgroup$
    – thanatoz
    Oct 1 '18 at 7:22






    $begingroup$
    Is there a descriptive guide to use this apart from the official jetsetter page?
    $endgroup$
    – thanatoz
    Oct 1 '18 at 7:22














    $begingroup$
    Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
    $endgroup$
    – The Lyrist
    Oct 1 '18 at 18:29




    $begingroup$
    Are you looking for implementation example in a certain language? If you look through the jetsetter article and its references, you can see code sample implementation in C#, PHP, etc. Many people are kind enough to share the code via github as well and hopefully one of them would work for you.
    $endgroup$
    – The Lyrist
    Oct 1 '18 at 18:29











    0












    $begingroup$

    So, this is a simple problem that could be solved using one-shot learning technique. To achieve this, we must build a model that understands our data and is capable of finding similarity or dissimilarity in your data.



    For this, we must carry out the following steps:




    1. Train (or finetune) the network on dataset of related images.

    2. After training the model, clip the last predicting layers to create embedding.

    3. Pass your testing data through the network and store individual embedding.

    4. Find the difference between the embedding and find the differences crossing a certain threshold.

    5. These images are potentially images having similar data and this could be easily used to find duplicacy in the dataset.


    1shot



    I referred this paper on oneshot learning and later found this blog to be a little helpful.






    share|improve this answer









    $endgroup$


















      0












      $begingroup$

      So, this is a simple problem that could be solved using one-shot learning technique. To achieve this, we must build a model that understands our data and is capable of finding similarity or dissimilarity in your data.



      For this, we must carry out the following steps:




      1. Train (or finetune) the network on dataset of related images.

      2. After training the model, clip the last predicting layers to create embedding.

      3. Pass your testing data through the network and store individual embedding.

      4. Find the difference between the embedding and find the differences crossing a certain threshold.

      5. These images are potentially images having similar data and this could be easily used to find duplicacy in the dataset.


      1shot



      I referred this paper on oneshot learning and later found this blog to be a little helpful.






      share|improve this answer









      $endgroup$
















        0












        0








        0





        $begingroup$

        So, this is a simple problem that could be solved using one-shot learning technique. To achieve this, we must build a model that understands our data and is capable of finding similarity or dissimilarity in your data.



        For this, we must carry out the following steps:




        1. Train (or finetune) the network on dataset of related images.

        2. After training the model, clip the last predicting layers to create embedding.

        3. Pass your testing data through the network and store individual embedding.

        4. Find the difference between the embedding and find the differences crossing a certain threshold.

        5. These images are potentially images having similar data and this could be easily used to find duplicacy in the dataset.


        1shot



        I referred this paper on oneshot learning and later found this blog to be a little helpful.






        share|improve this answer









        $endgroup$



        So, this is a simple problem that could be solved using one-shot learning technique. To achieve this, we must build a model that understands our data and is capable of finding similarity or dissimilarity in your data.



        For this, we must carry out the following steps:




        1. Train (or finetune) the network on dataset of related images.

        2. After training the model, clip the last predicting layers to create embedding.

        3. Pass your testing data through the network and store individual embedding.

        4. Find the difference between the embedding and find the differences crossing a certain threshold.

        5. These images are potentially images having similar data and this could be easily used to find duplicacy in the dataset.


        1shot



        I referred this paper on oneshot learning and later found this blog to be a little helpful.







        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered 27 mins ago









        thanatozthanatoz

        504317




        504317






























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