rcnnBoxRegressionLayer

R2026b

(Not recommended) Box regression layer for Fast and Faster R-CNN

RCNNBoxRegressionLayer is not recommended. Instead, use a different type of object detector, such as a yolov4ObjectDetector detector. For more information, see Version History.

Description

A box regression layer refines bounding box locations by using a smooth L1 loss function. Use this layer to create a Fast or Faster R-CNN object detection network.

Creation

Syntax

layer = rcnnBoxRegressionLayer

layer = rcnnBoxRegressionLayer('Name',Name)

Description

layer = rcnnBoxRegressionLayer creates a box regression layer for a Fast or Faster R-CNN object detection network.

layer = rcnnBoxRegressionLayer('Name',Name) creates a box regression layer and sets the optional Name property.

Properties

Name — Layer name

'' (default) | character vector

Layer name, specified as a character vector. For Layer array input, the trainnet (Deep Learning Toolbox) and dlnetwork (Deep Learning Toolbox) functions automatically assign names to unnamed layers.

Data Types: char

NumInputs — Number of inputs

Read-only: 1 (default)

This property is read-only.

Number of inputs to the layer, represented as 1. This layer has a single input only.

Data Types: double

InputNames — Input name

Read-only: {'in'} (default)

This property is read-only.

Input name, represented as {'in'}. This layer has a single input only.

Examples

Create R-CNN Box Regression Layer

Create an R-CNN box regression layer with the name 'rcnn_box_reg'.

rcnnBoxRegression = rcnnBoxRegressionLayer('Name','rcnn_box_reg');

Extended Capabilities

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

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

To generate CUDA^® or C++ code by using GPU Coder™, you must first construct and train a deep neural network. Once the network is trained and evaluated, you can configure the code generator to generate code and deploy the convolutional neural network on platforms that use NVIDIA^® or ARM^® GPU processors. For more information, see Deep Learning with GPU Coder (GPU Coder).

For this layer, you can generate code that takes advantage of the NVIDIA CUDA deep neural network library (cuDNN), NVIDIA TensorRT™ high performance inference library, or the ARM Compute Library for Mali GPU.

Version History

Introduced in R2018b

R2024b: Not Recommended

Starting in R2024b, R-CNN object detectors are no longer recommended. Instead, use a different type of object detector, such as a yoloxObjectDetector (Visual Inspection Toolbox) or yolov4ObjectDetector object. These object detectors are faster than R-CNN object detectors. For more information, see Choose an Object Detector.