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Data formate of images and annotations for DL-Networks


Strange out of memory issue while loading an image to a Bitmap objectLazy load of images in ListViewHow do I auto-resize an image to fit a 'div' container?Which @NotNull Java annotation should I use?What's the difference between @Component, @Repository & @Service annotations in Spring?How to vertically align an image inside a divMS COCO dataset multiple instance annotation ruleHow to format my data for training and testing on KerasKeras CNN: Add text as additional input besides image to CNN






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








0















The question first:



what is the image and annotation format for image classification / object recognition



This is based on this question which did not get any answers.



What I noticed is that different datasets use different annotation formats. For example COCO uses json and imagNet csv. Also, some use flattened images in RGB code others images as they are with annotations. If using RGB code, the RGB is stored differently... and so on.



That is all very confusing.



Can someone give me an overview of the main and most common approaches? How to Sort and annotate the images and then how to conect image/boundingbox/annotation?



Thanks in advance



Cheers



edit: a help would be a tutorial from A to Z. Meaning, taking your own images, annotate them, organize them, load them with Keras/tensorflow use a simple CNN for classification or object recognition.



Would be interesting a side by side classification / object detection as they are similar but different in the whole setup.










share|improve this question
































    0















    The question first:



    what is the image and annotation format for image classification / object recognition



    This is based on this question which did not get any answers.



    What I noticed is that different datasets use different annotation formats. For example COCO uses json and imagNet csv. Also, some use flattened images in RGB code others images as they are with annotations. If using RGB code, the RGB is stored differently... and so on.



    That is all very confusing.



    Can someone give me an overview of the main and most common approaches? How to Sort and annotate the images and then how to conect image/boundingbox/annotation?



    Thanks in advance



    Cheers



    edit: a help would be a tutorial from A to Z. Meaning, taking your own images, annotate them, organize them, load them with Keras/tensorflow use a simple CNN for classification or object recognition.



    Would be interesting a side by side classification / object detection as they are similar but different in the whole setup.










    share|improve this question




























      0












      0








      0








      The question first:



      what is the image and annotation format for image classification / object recognition



      This is based on this question which did not get any answers.



      What I noticed is that different datasets use different annotation formats. For example COCO uses json and imagNet csv. Also, some use flattened images in RGB code others images as they are with annotations. If using RGB code, the RGB is stored differently... and so on.



      That is all very confusing.



      Can someone give me an overview of the main and most common approaches? How to Sort and annotate the images and then how to conect image/boundingbox/annotation?



      Thanks in advance



      Cheers



      edit: a help would be a tutorial from A to Z. Meaning, taking your own images, annotate them, organize them, load them with Keras/tensorflow use a simple CNN for classification or object recognition.



      Would be interesting a side by side classification / object detection as they are similar but different in the whole setup.










      share|improve this question
















      The question first:



      what is the image and annotation format for image classification / object recognition



      This is based on this question which did not get any answers.



      What I noticed is that different datasets use different annotation formats. For example COCO uses json and imagNet csv. Also, some use flattened images in RGB code others images as they are with annotations. If using RGB code, the RGB is stored differently... and so on.



      That is all very confusing.



      Can someone give me an overview of the main and most common approaches? How to Sort and annotate the images and then how to conect image/boundingbox/annotation?



      Thanks in advance



      Cheers



      edit: a help would be a tutorial from A to Z. Meaning, taking your own images, annotate them, organize them, load them with Keras/tensorflow use a simple CNN for classification or object recognition.



      Would be interesting a side by side classification / object detection as they are similar but different in the whole setup.







      image annotations conv-neural-network






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 28 at 15:04







      Florida Man

















      asked Mar 28 at 14:52









      Florida ManFlorida Man

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