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yolov2ReorgLayer

(Removed) Create reorganization layer for YOLO v2 object detection network

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YOLOv2ReorgLayer function has been removed. Use spaceToDepthLayer instead.

Description

The yolov2ReorgLayer function creates a YOLOv2ReorgLayer object, which represents the reorganization layer for you only look once version 2 (YOLO v2) object detection network. The reorganization layer reorganizes the high-resolution feature maps from a lower layer by stacking adjacent features into different channels. The output of reorganization layer is fed to the depth concatenation layer. The depth concatenation layer concatenates the reorganized high-resolution features with the low-resolution features from a higher layer.

Creation

Syntax

layer = yolov2ReorgLayer(stride)
layer = yolov2ReorgLayer(stride,'Name',layerName)

Description

layer = yolov2ReorgLayer(stride) creates the reorganization layer for YOLO v2 object detection network. The layer reorganizes the dimension of the input feature maps according to the step size specified in stride.

layer = yolov2ReorgLayer(stride,'Name',layerName) sets the Name property using a name-value pair. For example, yolov2ReorgLayer('Name','yolo_Reorg') creates reorganization layer with the name 'yolo_Reorg'.

example

Input Arguments

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Step size for traversing the input vertically and horizontally, specified as a 2-element vector of positive integers in form [a b]. a is the vertical step size and b is the horizontal step size.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name of reorganization layer, specified as a character vector or string scalar. This input argument sets the Name property of the layer. If you do not specify the name, then the function automatically sets Name to ''.

Data Types: char | string

Properties

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Layer name, specified as a character vector. To include a layer in a layer graph, you must specify a nonempty unique layer name. If you train a series network with the layer and Name is set to '', then the software automatically assigns a name to the layer at training time.

Data Types: char

This property is read-only.

Number of inputs to the layer, returned as 1. This layer accepts a single input only.

Data Types: double

This property is read-only.

Input names, returned as {'in'}. This layer accepts a single input only.

Data Types: cell

This property is read-only.

Number of outputs from the layer, returned as 1. This layer has a single output only.

Data Types: double

This property is read-only.

Output names, returned as {'out'}. This layer has a single output only.

Data Types: cell

Examples

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Specify the step size for reorganising the dimension of input feature map.

stride = [2 2];

Create a YOLO v2 reorganization layer with the specified step size and the name as "yolo_Reorg".

layer = yolov2ReorgLayer(stride,'Name','yolo_Reorg');

Inspect the properties of the YOLO v2 reorganization layer.

layer
layer = 
  YOLOv2ReorgLayer with properties:

      Name: 'yolo_Reorg'

   Hyperparameters
    Stride: [2 2]

Tips

  • You can find the desired value of stride using:

    stride  =  floor(size of input feature map to reorganization layersize of output feature map from higher layer)

Algorithms

The reorganization layer improves the performance of the YOLO v2 object detection network by facilitating feature concatenation from different layers. It reorganizes the dimension of a lower layer feature map so that it can be concatenated with the higher layer feature map.

Consider an input feature map of size [H W C], where:

  • H is the height of the feature map.

  • W is the width of the feature map.

  • C is the number of channels.

The reorganization layer chooses feature map values from locations based on the step sizes in stride and adds those feature values to the third dimension C. The size of the reorganized feature map from the reorganization layer is

[floor(H/stride(1)) floor(W/stride(2)) C×stride(1)×stride(2)].

For feature concatenation, the height and width of the reorganized feature map must match with the height and width of the higher layer feature map.

References

[1] Joseph. R, S. K. Divvala, R. B. Girshick, and F. Ali. "You Only Look Once: Unified, Real-Time Object Detection." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. Las Vegas, NV: CVPR, 2016.

[2] Joseph. R and F. Ali. "YOLO 9000: Better, Faster, Stronger." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525. Honolulu, HI: CVPR, 2017.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2019a

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R2024a: yolov2ReorgLayer function removed

The YOLOv2ReorgLayer function has been removed. Use spaceToDepthLayer instead.

See Also

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