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    Metadata details:
    filename: complexYOLOv4TransferLearn.m
    path of file: ./repos/Lidar-object-detection-using-complex-yolov4
    
    the code of the file until where you have to start completion: %% Transfer Learning Using Pretrained Complex YOLO v4 Network
% The following code demonstrates how to perform transfer learning using
% the pretrained Complex YOLO v4 network for object detection. This script
% uses the "configureYOLOv4" function to create a custom Complex YOLO v4
% network using the pretrained model.

%% Setup
% Add path to the source directory.
addpath('src');

%% Download Pretrained Network
% This repository uses two variants of YOLO v4 models.
% *complex
    
function to create a custom Complex YOLO v4
% network using the pretrained model.

%% Setup
% Add path to the source directory.
addpath('src');

%% Download Pretrained Network
% This repository uses two variants of YOLO v4 models.
% *complex-yolov4-pandaset*
% *tiny-complex-yolov4-pandaset*
% Set the modelName from the above ones to download that pretrained model.
modelName = 'tiny-complex-yolov4-pandaset';
model = helper.downloadPretrainedYOLOv4(modelName);
net = model.net;

%% Load Data
% Create a datastore for loading the BEV images.
imageFileLocation = fullfile(tempdir,'Pandaset','BEVImages');
imds = imageDatastore(imageFileLocation);

% Create a datastore for loading the ground truth bounding boxes.
boxLabelLocation = fullfile(tempdir,'Pandaset','Cuboids','BEVGroundTruthLabels.mat');
load(boxLabelLocation,'processedLabels');
bds = boxLabelDatastore(processedLabels);

% Remove data with zero labels from the training data.
[imds,bds] = helper.removeEmptyData(imds,bds);

% Split the data set into a training set for training the network, and a test 
% set for evaluating the network. Use 60% of the data for training set and the 
% rest for the test set.
rng(0);
shuffledIndices = randperm(size(imds.Files,1));
idx = floor(0.6 * length(shuffledIndices));

% Split the image datastore into training and test set.
imdsTrain = subset(imds,shuffledIndices(1:idx));
imdsTest = subset(imds,shuffledIndices(idx+1:end