What does generate_anchor_base()'s arguments mean?











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Github page



Looking generate_anchor_base method, which is Faster R-CNN util method in ChainerCV.



What is the base_size = 16? I saw in the Documentation that it is




The width and the height of the reference window.




But what does "reference window" mean?



Also it says that anchor_scales=[8, 16, 32] are the areas of the anchors but I thought that that the areas are (128, 256, 512)



Another question:

If the base size is 16 and h = 128 and w=128, Does that mean anchor_base[index, 0] = py - h / 2 is a negative value?
since py = 8 and and h/2 = 128/2










share|improve this question




























    up vote
    1
    down vote

    favorite












    Github page



    Looking generate_anchor_base method, which is Faster R-CNN util method in ChainerCV.



    What is the base_size = 16? I saw in the Documentation that it is




    The width and the height of the reference window.




    But what does "reference window" mean?



    Also it says that anchor_scales=[8, 16, 32] are the areas of the anchors but I thought that that the areas are (128, 256, 512)



    Another question:

    If the base size is 16 and h = 128 and w=128, Does that mean anchor_base[index, 0] = py - h / 2 is a negative value?
    since py = 8 and and h/2 = 128/2










    share|improve this question


























      up vote
      1
      down vote

      favorite









      up vote
      1
      down vote

      favorite











      Github page



      Looking generate_anchor_base method, which is Faster R-CNN util method in ChainerCV.



      What is the base_size = 16? I saw in the Documentation that it is




      The width and the height of the reference window.




      But what does "reference window" mean?



      Also it says that anchor_scales=[8, 16, 32] are the areas of the anchors but I thought that that the areas are (128, 256, 512)



      Another question:

      If the base size is 16 and h = 128 and w=128, Does that mean anchor_base[index, 0] = py - h / 2 is a negative value?
      since py = 8 and and h/2 = 128/2










      share|improve this question















      Github page



      Looking generate_anchor_base method, which is Faster R-CNN util method in ChainerCV.



      What is the base_size = 16? I saw in the Documentation that it is




      The width and the height of the reference window.




      But what does "reference window" mean?



      Also it says that anchor_scales=[8, 16, 32] are the areas of the anchors but I thought that that the areas are (128, 256, 512)



      Another question:

      If the base size is 16 and h = 128 and w=128, Does that mean anchor_base[index, 0] = py - h / 2 is a negative value?
      since py = 8 and and h/2 = 128/2







      chainer chainercv






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      edited Nov 18 at 14:06









      corochann

      1,0351618




      1,0351618










      asked Nov 18 at 11:52









      floyd

      18410




      18410
























          1 Answer
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          accepted










          The method is a util function of Faster R-CNN, so I assume you understood what is the "anchor" proposed in Faster R-CNN.




          • "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" https://arxiv.org/abs/1506.01497


          base_size and anchor_scales determines the size of the anchor.
          For example, when base_size=16 and anchor_scales=[8, 16, 32] (and ratio=1.0), height and width of the anchor will be 16 * [8, 16, 32] = (128, 256, 512), as you expected.
          ratio determines the height and width aspect ratio.



          (I might be wrong in below paragraph, please correct if I'm wrong.)



          I think base_size need to be set as the size of the current hidden layer's scale. In the chainercv Faster R-CNN implementation, extractor's feature is fed into rpn (region proposal network) and generate_anchor_base is used in rpn. So you need to take care what is the feature of extractor's output. chainercv uses VGG16 as the feature extractor, and conv5_3 layer is used as extracted feature (see here), this layer is a place where max_pooling_2d is applied 4 times, which results 2^4=16 times smallen feature.



          For the another question, I think your understanding is correct, py - h / 2 will be negative value. But this anchor_base value is just a relative value. Once anchor_base is prepared at the initialization of model (here), actual (absolute value) anchor is created in each forward call (here) in _enumerate_shifted_anchor method.






          share|improve this answer





















          • Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
            – floyd
            Nov 18 at 14:04











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          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes








          up vote
          1
          down vote



          accepted










          The method is a util function of Faster R-CNN, so I assume you understood what is the "anchor" proposed in Faster R-CNN.




          • "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" https://arxiv.org/abs/1506.01497


          base_size and anchor_scales determines the size of the anchor.
          For example, when base_size=16 and anchor_scales=[8, 16, 32] (and ratio=1.0), height and width of the anchor will be 16 * [8, 16, 32] = (128, 256, 512), as you expected.
          ratio determines the height and width aspect ratio.



          (I might be wrong in below paragraph, please correct if I'm wrong.)



          I think base_size need to be set as the size of the current hidden layer's scale. In the chainercv Faster R-CNN implementation, extractor's feature is fed into rpn (region proposal network) and generate_anchor_base is used in rpn. So you need to take care what is the feature of extractor's output. chainercv uses VGG16 as the feature extractor, and conv5_3 layer is used as extracted feature (see here), this layer is a place where max_pooling_2d is applied 4 times, which results 2^4=16 times smallen feature.



          For the another question, I think your understanding is correct, py - h / 2 will be negative value. But this anchor_base value is just a relative value. Once anchor_base is prepared at the initialization of model (here), actual (absolute value) anchor is created in each forward call (here) in _enumerate_shifted_anchor method.






          share|improve this answer





















          • Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
            – floyd
            Nov 18 at 14:04















          up vote
          1
          down vote



          accepted










          The method is a util function of Faster R-CNN, so I assume you understood what is the "anchor" proposed in Faster R-CNN.




          • "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" https://arxiv.org/abs/1506.01497


          base_size and anchor_scales determines the size of the anchor.
          For example, when base_size=16 and anchor_scales=[8, 16, 32] (and ratio=1.0), height and width of the anchor will be 16 * [8, 16, 32] = (128, 256, 512), as you expected.
          ratio determines the height and width aspect ratio.



          (I might be wrong in below paragraph, please correct if I'm wrong.)



          I think base_size need to be set as the size of the current hidden layer's scale. In the chainercv Faster R-CNN implementation, extractor's feature is fed into rpn (region proposal network) and generate_anchor_base is used in rpn. So you need to take care what is the feature of extractor's output. chainercv uses VGG16 as the feature extractor, and conv5_3 layer is used as extracted feature (see here), this layer is a place where max_pooling_2d is applied 4 times, which results 2^4=16 times smallen feature.



          For the another question, I think your understanding is correct, py - h / 2 will be negative value. But this anchor_base value is just a relative value. Once anchor_base is prepared at the initialization of model (here), actual (absolute value) anchor is created in each forward call (here) in _enumerate_shifted_anchor method.






          share|improve this answer





















          • Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
            – floyd
            Nov 18 at 14:04













          up vote
          1
          down vote



          accepted







          up vote
          1
          down vote



          accepted






          The method is a util function of Faster R-CNN, so I assume you understood what is the "anchor" proposed in Faster R-CNN.




          • "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" https://arxiv.org/abs/1506.01497


          base_size and anchor_scales determines the size of the anchor.
          For example, when base_size=16 and anchor_scales=[8, 16, 32] (and ratio=1.0), height and width of the anchor will be 16 * [8, 16, 32] = (128, 256, 512), as you expected.
          ratio determines the height and width aspect ratio.



          (I might be wrong in below paragraph, please correct if I'm wrong.)



          I think base_size need to be set as the size of the current hidden layer's scale. In the chainercv Faster R-CNN implementation, extractor's feature is fed into rpn (region proposal network) and generate_anchor_base is used in rpn. So you need to take care what is the feature of extractor's output. chainercv uses VGG16 as the feature extractor, and conv5_3 layer is used as extracted feature (see here), this layer is a place where max_pooling_2d is applied 4 times, which results 2^4=16 times smallen feature.



          For the another question, I think your understanding is correct, py - h / 2 will be negative value. But this anchor_base value is just a relative value. Once anchor_base is prepared at the initialization of model (here), actual (absolute value) anchor is created in each forward call (here) in _enumerate_shifted_anchor method.






          share|improve this answer












          The method is a util function of Faster R-CNN, so I assume you understood what is the "anchor" proposed in Faster R-CNN.




          • "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" https://arxiv.org/abs/1506.01497


          base_size and anchor_scales determines the size of the anchor.
          For example, when base_size=16 and anchor_scales=[8, 16, 32] (and ratio=1.0), height and width of the anchor will be 16 * [8, 16, 32] = (128, 256, 512), as you expected.
          ratio determines the height and width aspect ratio.



          (I might be wrong in below paragraph, please correct if I'm wrong.)



          I think base_size need to be set as the size of the current hidden layer's scale. In the chainercv Faster R-CNN implementation, extractor's feature is fed into rpn (region proposal network) and generate_anchor_base is used in rpn. So you need to take care what is the feature of extractor's output. chainercv uses VGG16 as the feature extractor, and conv5_3 layer is used as extracted feature (see here), this layer is a place where max_pooling_2d is applied 4 times, which results 2^4=16 times smallen feature.



          For the another question, I think your understanding is correct, py - h / 2 will be negative value. But this anchor_base value is just a relative value. Once anchor_base is prepared at the initialization of model (here), actual (absolute value) anchor is created in each forward call (here) in _enumerate_shifted_anchor method.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 18 at 13:39









          corochann

          1,0351618




          1,0351618












          • Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
            – floyd
            Nov 18 at 14:04


















          • Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
            – floyd
            Nov 18 at 14:04
















          Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
          – floyd
          Nov 18 at 14:04




          Yes, thank you. 16 is the receptive field(in the original image) for every spatial location in "conv5_3". But I still can't get it, Why do we need to create variable "base_size"? We could just say anchor_scales=[128, 256, 512], right? What does the operation mean "base size * anchor_scales"? What does "reference window" mean? So sorry for all those questions :(
          – floyd
          Nov 18 at 14:04


















           

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