ways to test simulated data quality












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I have a huge data set of logistic objects which I generated by creating a 3D model and rendering it in blender. In order to test the generated data, I've downloaded some images. The simulated data set contains around 5000 high-resolution images and around 1000 images of the actual object.
Now I would like to know the quality of my simulated data. I am new to ML and CNN.



1.Are there any specific models to test this kind of data?



2.Should I train with my simulated data and test with actual data or vice-versa which would give me a better way to find the quality of my data ?










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    $begingroup$


    I have a huge data set of logistic objects which I generated by creating a 3D model and rendering it in blender. In order to test the generated data, I've downloaded some images. The simulated data set contains around 5000 high-resolution images and around 1000 images of the actual object.
    Now I would like to know the quality of my simulated data. I am new to ML and CNN.



    1.Are there any specific models to test this kind of data?



    2.Should I train with my simulated data and test with actual data or vice-versa which would give me a better way to find the quality of my data ?










    share|improve this question









    New contributor




    Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I have a huge data set of logistic objects which I generated by creating a 3D model and rendering it in blender. In order to test the generated data, I've downloaded some images. The simulated data set contains around 5000 high-resolution images and around 1000 images of the actual object.
      Now I would like to know the quality of my simulated data. I am new to ML and CNN.



      1.Are there any specific models to test this kind of data?



      2.Should I train with my simulated data and test with actual data or vice-versa which would give me a better way to find the quality of my data ?










      share|improve this question









      New contributor




      Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have a huge data set of logistic objects which I generated by creating a 3D model and rendering it in blender. In order to test the generated data, I've downloaded some images. The simulated data set contains around 5000 high-resolution images and around 1000 images of the actual object.
      Now I would like to know the quality of my simulated data. I am new to ML and CNN.



      1.Are there any specific models to test this kind of data?



      2.Should I train with my simulated data and test with actual data or vice-versa which would give me a better way to find the quality of my data ?







      data






      share|improve this question









      New contributor




      Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




      Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question








      edited 15 hours ago









      Alireza Zolanvari

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      366117






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      asked 18 hours ago









      Karri Aravind ReddyKarri Aravind Reddy

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      Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Karri Aravind Reddy is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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