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# trainFastRCNNObjectDetector

Train a Fast R-CNN deep learning object detector

## Syntax

``trainedDetector = trainFastRCNNObjectDetector(trainingData,network,options)``
``trainedDetector = trainFastRCNNObjectDetector(trainingData,checkpoint,options)``
``trainedDetector = trainFastRCNNObjectDetector(trainingData,detector,options)``
``trainedDetector = trainFastRCNNObjectDetector(___,'RegionProposalFcn',proposalFcn)``
``trainedDetector = trainFastRCNNObjectDetector(___,Name,Value)``

## Description

example

````trainedDetector = trainFastRCNNObjectDetector(trainingData,network,options)` trains a Fast R-CNN (regions with convolution neural networks) object detector using deep learning. You can train a Fast R-CNN detector to detect multiple object classes. Specify your ground truth training data, your network, and training options. The network can be a pretrained series network such as `alexnet` or `vgg16` for training using transfer learning, or you can train a network from scratch using an array of `Layer` objects with uninitialized weights.This function requires that you have Neural Network Toolbox™. It is recommended that you also have Parallel Computing Toolbox™ to use with a CUDA®-enabled NVIDIA® GPU with compute capability 3.0 or higher.```
````trainedDetector = trainFastRCNNObjectDetector(trainingData,checkpoint,options)` resumes training from a detector checkpoint.```
````trainedDetector = trainFastRCNNObjectDetector(trainingData,detector,options)` continues training a detector with additional training data or performs more training iterations to improve detector accuracy.```
````trainedDetector = trainFastRCNNObjectDetector(___,'RegionProposalFcn',proposalFcn)` optionally trains a custom region proposal function, `proposalFcn`, using any of the previous inputs. If you do not specify a proposal function, the function uses a variation of the Edge Boxes algorithm.```
````trainedDetector = trainFastRCNNObjectDetector(___,Name,Value)` uses additional options specified by one or more `Name,Value` pair arguments.```

## Examples

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```data = load('rcnnStopSigns.mat', 'stopSigns', 'fastRCNNLayers'); stopSigns = data.stopSigns; fastRCNNLayers = data.fastRCNNLayers; ```

```stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ... stopSigns.imageFilename); ```

Set network training options:

• Lower the InitialLearningRate to reduce the rate at which network parameters are changed.

• Set the CheckpointPath to save detector checkpoints to a temporary directory. Change this to another location if required.

```options = trainingOptions('sgdm', ... 'InitialLearnRate', 1e-6, ... 'MaxEpochs', 10, ... 'CheckpointPath', tempdir); ```

Train the Fast R-CNN detector. Training can take a few minutes to complete.

```frcnn = trainFastRCNNObjectDetector(stopSigns, fastRCNNLayers , options, ... 'NegativeOverlapRange', [0 0.1], ... 'PositiveOverlapRange', [0.7 1], ... 'SmallestImageDimension', 600); ```
```******************************************************************* Training a Fast R-CNN Object Detector for the following object classes: * stopSign --> Extracting region proposals from 27 training images...done. |=========================================================================================| | Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning| | | | (seconds) | Loss | Accuracy | Rate | |=========================================================================================| | 1 | 1 | 0.19 | 0.0618 | 100.00% | 0.0000 | | 3 | 50 | 9.72 | 0.0122 | 100.00% | 0.0000 | | 5 | 100 | 19.41 | 0.0174 | 100.00% | 0.0000 | | 8 | 150 | 29.57 | 0.0124 | 100.00% | 0.0000 | | 10 | 200 | 40.43 | 0.0273 | 100.00% | 0.0000 | | 10 | 210 | 42.59 | 0.0300 | 100.00% | 0.0000 | |=========================================================================================| ```

Test the Fast R-CNN detector on a test image.

```img = imread('stopSignTest.jpg'); ```

Run the detector.

```[bbox, score, label] = detect(frcnn, img); ```

Display detection results.

```detectedImg = insertShape(img, 'Rectangle', bbox); figure imshow(detectedImg) ```

## Input Arguments

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Labeled ground truth images, specified as a table with two or more columns. The first column must contain paths and file names to grayscale or truecolor (RGB) images. The remaining columns must contain bounding boxes related to the corresponding image. Each column represents a single object class, such as a car, dog, flower, or stop sign.

Each bounding box must be in the format [x y width height]. The format specifies the upper-left corner location and size of the object in the corresponding image. The table variable name defines the object class name. To create the ground truth table, use the Image Labeler app. Boxes smaller than 32-by-32 are not used for training.

Network, specified as a `SeriesNetwork` object or as an array of `Layer` objects. For example:

```layers = [imageInputLayer([28 28 3]) convolution2dLayer([5 5],10) reluLayer() fullyConnectedLayer(10) softmaxLayer() classificationLayer()]; ```

The network is trained to classify the object classes defined in the `trainingData` table.

When the network is a `SeriesNetwork` object, the function adjusts the network layers to support the number of object classes defined within the specified `trainingData`. The background is added as an additional class.

When the network is an array of `Layer` objects, the network must have a classification layer that supports the number of object classes, plus a background class. Use this input type to customize the learning rates of each layer.

The function replaces the last `averagePooling2dLayer` or `maxPooling2dLayer` with an ROI pooling layer.

### Note

`trainFastRCNNObjectDetector` does not support DAG networks, such as ResNet-50, Inception-v3, or GoogLeNet. Additionally, you cannot pass a Layers array from a DAG network to the training function, because the Layers property from a DAG network does not contain the connection information.

Training parameters of the neural network, specified using the `trainingOptions` function.

To fine-tune a pretrained network for detection, lower the initial learning rate to avoid changing the model parameters too rapidly. For example:

```options = trainingOptions('sgdm', ... 'InitialLearningRate',1e-6, ... 'CheckpointPath',tempdir); detector = trainFastRCNNObjectDetector(trainingData,network,options);```

To save the detector after every epoch, set the `'CheckpointPath'` property when using the `trainingOptions` function. Saving a checkpoint after every epoch is recommended because network training can take a few hours.

### Note

`trainFastRCNNObjectDetector` does not support these training options:

• The `ExecutionEnvironment` values: `'multi-gpu'` or `'parallel'`

• The `Plots` value: `'training-progress'`

• The `ValidationData`, `ValidationFrequency`, or `ValidationPatience` options

Saved detector checkpoint, specified as a `fastRCNNObjectDetector` object. To save the detector after every epoch, set the `'CheckpointPath'` property when using the `trainingOptions` function. Saving a checkpoint after every epoch is recommended because network training can take a few hours.

To load a checkpoint for a previously trained detector, load the MAT-file from the checkpoint path. For example, if the `'CheckpointPath'` property of `options` is `'/tmp'`, load a checkpoint MAT-file using:

`data = load('/tmp/fast_rcnn_checkpoint__105__2016_11_18__14_25_08.mat');`

The name of the MAT-file includes the iteration number and timestamp of when the detector checkpoint was saved. The detector is saved in the `detector` variable of the file. Pass this file back into the `trainFastRCNNObjectDetector` function:

```frcnn = trainFastRCNNObjectDetector(stopSigns,... data.detector,options);```

Previously trained Fast R-CNN object detector, specified as a `fastRCNNObjectDetector` object.

Region proposal method, specified as a function handle. The function must have the form:

`[bboxes,scores] = proposalFcn(I)`

The input, `I`, is an image defined in the `trainingData` table. The function must return rectangular bound boxes, `bboxes`, in an m-by-4 array. Each row of `bboxes` contains a four-element vector, `[x y width height]`. This vector specifies the upper-left corner and size of a bounding box in pixels. The function must also return a score for each bounding box in an m-by-1 vector. Higher score values indicate that the bounding box is more likely to contain an object. The scores are used to select the strongest n regions, where n is defined by the value of `NumStrongestRegions`.

If you do not specify a custom proposal function, the function uses a variation of the Edge Boxes algorithm.

### Name-Value Pair Arguments

Specify optional comma-separated pairs of `Name,Value` arguments. `Name` is the argument name and `Value` is the corresponding value. `Name` must appear inside single quotes (`' '`). You can specify several name and value pair arguments in any order as `Name1,Value1,...,NameN,ValueN`.

Example: `'PositiveOverlapRange',[0.75 1]`

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Bounding box overlap ratios for positive training samples, specified as the comma-separated pair consisting of `'PositiveOverlapRange'` and a two-element vector. The vector contains values in the range [0,1]. Region proposals that overlap with ground truth bounding boxes within the specified range are used as positive training samples.

The overlap ratio used for both the `PositiveOverlapRange` and `NegativeOverlapRange` is defined as:

`$\frac{area\left(A\cap B\right)}{area\left(A\cup B\right)}$`

A and B are bounding boxes.

Bounding box overlap ratios for negative training samples, specified as the comma-separated pair consisting of `NegativeOverlapRange` and a two-element vector. The vector contains values in the range [0,1]. Region proposals that overlap with the ground truth bounding boxes within the specified range are used as negative training samples.

The overlap ratio used for both the `PositiveOverlapRange` and `NegativeOverlapRange` is defined as:

`$\frac{area\left(A\cap B\right)}{area\left(A\cup B\right)}$`

A and B are bounding boxes.

Maximum number of strongest region proposals to use for generating training samples, specified as the comma-separated pair consisting of `'NumStrongestRegions'` and a positive integer. Reduce this value to speed up processing time at the cost of training accuracy. To use all region proposals, set this value to `Inf`.

Length of smallest image dimension, either width or height, specified as the comma-separated pair consisting of `'SmallestImageDimension'` and a positive integer. Training images are resized such that the length of the shortest dimension is equal to the specified integer. By default, training images are not resized. Resizing training images helps reduce computational costs and memory used when training images are large. Typical values range from 400–600 pixels.

## Output Arguments

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Trained Fast R-CNN object detector, returned as a `fastRCNNObjectDetector` object.

## Tips

• To accelerate data preprocessing for training, `trainFastRCNNObjectDetector` automatically creates and uses a parallel pool based on your parallel preference settings. This requires Parallel Computing Toolbox.

• If you have a large network (such as VGG-16) or large images, you may encounter an "Out of Memory" error. To avoid this error for large images, set the '`SmallestImageDimension`' parameter to `600` or smaller, which will automatically resize the images during training. Alternatively, manually resize the images along with the bounding box ground truth data before calling `trainFastRCNNObjectDetector`.

• Transfer learning is supported for series networks such as AlexNet and VGG-16. Passing a network or an array of Layers to the training function preserves the weights of the pretrained network. You can perform transfer learning using code such as this.

```net = alexnet; detector = trainFastRCNNObjectDetector(trainingData,net,options);```

For more information, see Get Started with Transfer Learning (Neural Network Toolbox) and Transfer Learning Using AlexNet (Neural Network Toolbox).

• Use the `trainingOptions` function to enable or disable verbose printing.

## References

[1] Girshick, Ross. "Fast R-CNN." Proceedings of the IEEE International Conference on Computer Vision. 2015.

[2] Zitnick, C. Lawrence, and Piotr Dollar. "Edge Boxes: Locating Object Proposals From Edges." Computer Vision-ECCV 2014. Springer International Publishing, 2014, pp. 391–405.