Why do I need pre-trained weights in transfer learning?












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$begingroup$


I am using a Mask-RCNN. I first chose the resnet50 backbone then downloaded COCO pre-trained weights.



Why do I need pre-trained weights for transfer learning?



The transfer learning approach is to train a base network and then copy its first layers of the target. My base network is resnet50 and I copied the first layers to Mask RCNN. So, why do I need pre-trained weights, for example, coco pre-trained weights?



I understand that I copy layers and pre-trained weights from resnet.



Thanks in advance.






deep-learning cnn transfer-learning







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    0












    $begingroup$


    I am using a Mask-RCNN. I first chose the resnet50 backbone then downloaded COCO pre-trained weights.



    Why do I need pre-trained weights for transfer learning?



    The transfer learning approach is to train a base network and then copy its first layers of the target. My base network is resnet50 and I copied the first layers to Mask RCNN. So, why do I need pre-trained weights, for example, coco pre-trained weights?



    I understand that I copy layers and pre-trained weights from resnet.



    Thanks in advance.






    deep-learning cnn transfer-learning







    share|improve this question











    $endgroup$















      0












      0








      0





      $begingroup$


      I am using a Mask-RCNN. I first chose the resnet50 backbone then downloaded COCO pre-trained weights.



      Why do I need pre-trained weights for transfer learning?



      The transfer learning approach is to train a base network and then copy its first layers of the target. My base network is resnet50 and I copied the first layers to Mask RCNN. So, why do I need pre-trained weights, for example, coco pre-trained weights?



      I understand that I copy layers and pre-trained weights from resnet.



      Thanks in advance.






      deep-learning cnn transfer-learning







      share|improve this question











      $endgroup$




      I am using a Mask-RCNN. I first chose the resnet50 backbone then downloaded COCO pre-trained weights.



      Why do I need pre-trained weights for transfer learning?



      The transfer learning approach is to train a base network and then copy its first layers of the target. My base network is resnet50 and I copied the first layers to Mask RCNN. So, why do I need pre-trained weights, for example, coco pre-trained weights?



      I understand that I copy layers and pre-trained weights from resnet.



      Thanks in advance.






      deep-learning cnn transfer-learning




      deep-learning cnn transfer-learning






      share|improve this question















      share|improve this question













      share|improve this question




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      edited 6 mins ago









      Ethan

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          $begingroup$

          You need pre-trainned weights for it to be Transfer Learning.



          Copying layer structures is not transfer learning, it is just structuring a network inspired/copied from others.



          The transferm learning lies in using pre-trained layers to construct a different network that migth have similarities in the first layers. That is usually useful for Deep Learning.



          So the why's to use Transfer Learning are:




          • You want to analyse something different in a dataset that was used to train another network


          • You want to perform classification in a class that was used to train a certain network but was not annotated before


          • You want to train a network for a problem similar to the other one and don't have time or computational power to retrain all layers







          share|improve this answer









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            $begingroup$

            You need pre-trainned weights for it to be Transfer Learning.



            Copying layer structures is not transfer learning, it is just structuring a network inspired/copied from others.



            The transferm learning lies in using pre-trained layers to construct a different network that migth have similarities in the first layers. That is usually useful for Deep Learning.



            So the why's to use Transfer Learning are:




            • You want to analyse something different in a dataset that was used to train another network


            • You want to perform classification in a class that was used to train a certain network but was not annotated before


            • You want to train a network for a problem similar to the other one and don't have time or computational power to retrain all layers







            share|improve this answer









            $endgroup$


















              0












              $begingroup$

              You need pre-trainned weights for it to be Transfer Learning.



              Copying layer structures is not transfer learning, it is just structuring a network inspired/copied from others.



              The transferm learning lies in using pre-trained layers to construct a different network that migth have similarities in the first layers. That is usually useful for Deep Learning.



              So the why's to use Transfer Learning are:




              • You want to analyse something different in a dataset that was used to train another network


              • You want to perform classification in a class that was used to train a certain network but was not annotated before


              • You want to train a network for a problem similar to the other one and don't have time or computational power to retrain all layers







              share|improve this answer









              $endgroup$
















                0












                0








                0





                $begingroup$

                You need pre-trainned weights for it to be Transfer Learning.



                Copying layer structures is not transfer learning, it is just structuring a network inspired/copied from others.



                The transferm learning lies in using pre-trained layers to construct a different network that migth have similarities in the first layers. That is usually useful for Deep Learning.



                So the why's to use Transfer Learning are:




                • You want to analyse something different in a dataset that was used to train another network


                • You want to perform classification in a class that was used to train a certain network but was not annotated before


                • You want to train a network for a problem similar to the other one and don't have time or computational power to retrain all layers







                share|improve this answer









                $endgroup$



                You need pre-trainned weights for it to be Transfer Learning.



                Copying layer structures is not transfer learning, it is just structuring a network inspired/copied from others.



                The transferm learning lies in using pre-trained layers to construct a different network that migth have similarities in the first layers. That is usually useful for Deep Learning.



                So the why's to use Transfer Learning are:




                • You want to analyse something different in a dataset that was used to train another network


                • You want to perform classification in a class that was used to train a certain network but was not annotated before


                • You want to train a network for a problem similar to the other one and don't have time or computational power to retrain all layers








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                share|improve this answer










                answered 1 hour ago









                Pedro Henrique MonfortePedro Henrique Monforte

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