5 Pillars for Using Regularization Techniques in Object Detection

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Object detection is a technique for finding and identifying objects/things in an image. It is one of the biggest accomplishments of deep learning and image processing in the fields of Artificial Intelligence. The training of an object detection model is done by creating bounding boxes to identify and detect more than one specific object either in images, videos, or real-time streams, thus it is versatile.

But, with training, a deep learning model comes with the problem of overfitting. Overfitting is nothing but a state where the model has performed too well on the training set but fails to perform on the test set. In simple terms, we can say that the model has memorized the objects instead of learning them while training. Thus, the problems of overfitting and overconfidence are common to be seen while training an object detection algorithm.


So, in order to make the object detection algorithm generalize more on all training, validation, and test sets, a few regularization techniques can be applied. 

Some of these techniques are:

  • Dropout: This particular technique is the most famous and common regularization technique used to make any deep learning algorithm generalize more. Basically, while learning, the model just drops out ‘n’ number of layers of a deep neural network randomly. By dropping off some of the layers while learning, the information carried out by these layers is also dropped, thus in some way it adds noise to the training data and helps in generalizing the model on the data never seen before in the training dataset.
  • Early Stopping: This technique is basically monitoring the difference between training and validation loss. When the difference between the two losses is encountered to be lowest or the validation loss seems to be similar or near to training loss, the training can be stopped. This helps in making decisions on where the model performed the best, i.e., it is generalized. So, whenever the performance of the model is seen on the validation set or never seen before data while training seems to be degrading or not improving at all for a certain amount of iterations, then early stopping is applied.
Early Stopping
  • Batch Normalization: While training a deep neural network the inputs in each mini-batch from the above layers might change due to weight updates. Thus, to introduce stability in the results, batch normalization is used which standardizes the inputs to a layer for each mini-batch. This technique not only helps with the problem of overfitting but also with the problem of optimization by providing stability in the learning process and dramatically reducing the number of epochs while training required to train a deep neural network.
  • Label Smoothing: This technique majorly works on the problem of overconfidence rather than overfitting. Overconfidence is the problem where the model identifies and detects an object as wrongly classified with extremely high confidence, say an image containing an object dog but the model has detected it like a cat with a confidence score of 99% when the actual confidence would be around 40%. So, label smoothing just smooths out those labels and thus helps in generalizing the model. It helps in model reliability, deciding decision thresholds, and working with ensemble models’ results.
Label Smoothing

Data Augmentation: What is a much better way to generalize the model more than by adding more data to it for it to train upon. Data augmentation is a technique that helps in increasing the dataset by making minor changes in the existing data through rotation, flipping, cropping, or blurring a few pixels in the image and thus generating more and more data. Other data augmentation techniques like Cutout, hiding a part of the object (image) for the model to learn better than memorize the object, Mixup, combining the pixels of two different objects (images) for the model to learn better and thus generalize on the data, help in reducing the model variance, and subsequently, the regularization error.

These are some of the famous and commonly used regularization techniques to overcome the problem of overfitted and/or overconfident models. For any model where the training error seems to be near zero, the regularization should be set MAXX. That means, all the regularizers should be added while training an object detection model. Apart from above-stated techniques, there are a few more that can be applied depending on the kind of problem one is working upon. Thus, this overfitting problem is real and almost everyone faces this problem while training a deep neural network or an object detection algorithm and by using some good regularizers, that problem can be minimized to a good extent.

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5 Pillars for Using Regularization Techniques in Object Detection
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BugendaiTech Pvt Ltd
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