I have been working on implementing an object detection network that takes an image of a car and returns a bounding box around the license plate. I have tried using Fast and Faster RCNN networks for this, but the training images are too large and my hardware is not adiquitte enough causing the network to run out of memory immediatly.
My training data is 427 images of size [720 1280 3] with each image having a bounding box to identify the license plate, I am trying to train an SSD Object Detection Network and though I can train the network, when I try to use the trained network on an image it does not return any bounding box.
My Code
my_data = load('Copy_of_mydata.mat');
trainingData = my_data.vehicleTrainingData;
my_dataDir = 'D:/Senior_Project_Matlab/dataset/train/images';
trainigData.imageFilename = fullfile(my_dataDir,trainingData.imageFilename);
imds = datastore('./dataset/train/images/*.jpg');
blds = boxLabelDatastore(trainingData(:,2:end));
ds = combine(imds,blds);
inputImageSize = [360 640 3];
numClasses = 2;
network = 'resnet101';
lgraph = ssdLayers(inputImageSize, numClasses, network);
options = trainingOptions('sgdm','InitialLearnRate',18.0e-4,'LearnRateSchedule','piecewise', ...
'LearnRateDropFactor',0.7,'LearnRateDropPeriod',1,'Verbose',true,'MiniBatchSize',9,'MaxEpochs',8, ...
'Shuffle','every-epoch','VerboseFrequency',10);
detector = trainSSDObjectDetector(ds, lgraph, options);
Files = dir('dataset/train/images/*.jpg');
for x = 1:round(length(Files)/10)
FileName = append('dataset/train/images/',Files(x).name);
I = imread(FileName);
[bboxes, scores, labels] = detect(detector, I)
end
Training:
*************************************************************************
Training an SSD Object Detector for the following object classes:
* licensePlate
* Other
Training on single GPU.
Initializing input data normalization.
|=======================================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Mini-batch | Base Learning |
| | | (hh:mm:ss) | Loss | Accuracy | RMSE | Rate |
|=======================================================================================================|
| 1 | 1 | 00:00:03 | NaN | 25.86% | NaN | 0.0018 |
| 1 | 10 | 00:00:26 | NaN | 100.00% | NaN | 0.0018 |
| 1 | 20 | 00:00:51 | NaN | 100.00% | NaN | 0.0018 |
| 1 | 30 | 00:01:16 | NaN | 100.00% | NaN | 0.0018 |
| 1 | 40 | 00:01:41 | NaN | 100.00% | NaN | 0.0018 |
| 2 | 50 | 00:02:07 | NaN | 100.00% | NaN | 0.0013 |
Output:
labels = 1x0 catagorical
bboxes = empty array
scores = empty array
I have tried training both with just the license plate bounding boxes(mydata.mat) and with license plate bounding boxes and other bounding boxes(Coby_of_mydata.mat) with both trained networks producing the same output for every image I try. I have also tried and image input size of both [720 1280 3] and [360 640 3] again with the same outputs and I have tried all of the above options with resnet101, resnet50 and vgg16.
